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Unpacking the New ML Threat Matrix

Ep. 9

Yeehaw! “Data Cowboy” is in the building. Join us as Nic Fillingham and Natalia Godyla sit down with Ram Shankar Siva Kumar, aka “Data Cowboy” at Microsoft, for an exciting conversation about the release of a new adversarial ML threat matrix created for security analysts. Have no fear, we made sure to find out how Ram acquired the name, “Data Cowboy”, so saddle up and get ready for the ride!


Stick around to hear Nic and Natalia explore the urgency of surfacing threats at a faster rate with Justin Carroll, a Threat Analyst at Microsoft, and why it is more important now than ever before.


In This Episode, You Will Learn: 

• How Microsoft is using the new ML threat matrix against cyber attacks 

• The approach and philosophy for putting the threat matrix on GitHub  

• ML applications in regard to healthcare and why it is worrisome 

• What needs to happen in order to be successful in combating certain threats   


Some Questions We Ask:

• What is an adversarial ML threat matrix? 

• How will the community on GitHub contribute to the evolution of the ML threat matrix? 

• What resources are available to learn about all things VM? 

• What techniques are being used to find threats at a faster speed? 

• How do AI and ML factor into the role of managing data and collaborating with other teams? 


Resources

 

Ram’s Blog:  

https://www.microsoft.com/security/blog/2020/10/22/cyberattacks-against-machine-learning-systems-are-more-common-than-you-think/ 


Microsoft Security Blog:  

https://www.microsoft.com/security/blog/ 


Nic’s LinkedIn 

https://www.linkedin.com/in/nicfill/ 


Natalia’s LinkedIn 

https://www.linkedin.com/in/nataliagodyla/  


Ram’s LinkedIn 

https://www.linkedin.com/in/ram-shankar-siva-kumar-7b04a73a/ 


Justin’s LinkedIn 

https://www.linkedin.com/in/justin-carroll-20616574/  


Transcript

(Full transcript can be found at https://aka.ms/SecurityUnlockedEp09)


Nic Fillingham:

Hello, and welcome to Security Unlocked. A new podcast from Microsoft, where we unlock insights from the latest in news and research from across Microsoft security engineering and operations teams. I'm Nic Fillingham.


Natalia Godyla:

And I'm Natalia Godyla. In each episode, we'll discuss the latest stories from Microsoft security, deep dive into the newest threat intel, research and data science.


Nic Fillingham:

And profile some of the fascinating people working on artificial intelligence in Microsoft security. If you enjoy the podcast, have a request for a topic you'd like covered, or have some feedback on how we can make the podcast better.


Natalia Godyla:

Please contact us at securityunlocked@microsoft.com or via Microsoft security on Twitter. We'd love to hear from you. Hi Nic. Welcome back. How were your holidays?


Nic Fillingham:

Yes. Thank you, Natalia. Welcome back to you as well. Mine were great. You know, normally you drive somewhere or you fly somewhere, you go visit people, but this was all the FaceTimes and the Zooms and the Skypes, staycation, but it was still nice to eat too much and drink too much over the holiday period. How about you?


Natalia Godyla:

Yes, it was... to quote my boss. "It was vegetative." It was definitely just... well actually you know what? I did have a big moment over the holidays. I got engaged.


Nic Fillingham:

Oh, what!


Natalia Godyla:

I know.


Nic Fillingham:

Congratulations.


Natalia Godyla:

Thanks.


Nic Fillingham:

That's amazing.


Natalia Godyla:

I feel like it was absolute relaxation, really high point during the five minute proposal. And then we went back to our natural state and just absolute relaxation, lots of video games.


Nic Fillingham:

Hang on. So were you both sitting on the couch, playing some switch, eating your 95th packet of Doritos, and then all of a sudden your partner pauses and says, "You want to get hitched?"


Natalia Godyla:

There was a little bit more pomp and circumstance to it. Though I think that would have been very fitting for us.


Nic Fillingham:

Wow! Good on you guys. That's awesome.


Natalia Godyla:

I'm sure that like us, everyone has forgotten what they were doing at work, and I'm sure also what this podcast is doing. So why don't we give everyone a after the holiday refresher?


Nic Fillingham:

So just before the holidays, we partnered with Petri who run the Petri.com site Thurrott.com. First Ring Daily, a bunch of other great blogs, podcasts, email newsletters, and so welcome to all our new listeners who've come to us from Petri, from Throughout from First Ring Daily. Yeah. So what is security unlocked? Well, first and foremost, Natalia, and all your co-hosts, we are Microsoft employees and we will be interviewing, and we do interview on this podcast, other Microsoft employees, but we talk about security topics that hopefully are relevant to all security professionals and those who are interested in the state of cybersecurity.


Nic Fillingham:

And what we'll do in each episode is the first half is we'll pick a sort of a recent ish topic and we'll speak to a subject matter expert or an author of a recent blog post and ask them about the thing that they're working on, or that they've announced in the AI and ML space, hopefully try and demystify some new terms or concepts that may be either nascent or sort of difficult to wrap one's head around. And then in the second half...


Natalia Godyla:

We talk to again, another Microsoft security expert, this time more focused on the individual and their path to cybersecurity. So we'll ask them about what interested them about cyber security, what compelled them to join the industry, what jobs they've had, how they've come to Microsoft or their current role. In addition, we also have a new announcement about the podcast, which is we'll be switching to a weekly cadence. So prior to this, we were bi-weekly, now more goodness coming your way.


Nic Fillingham:

More pod in your pod app. What is the collective receptacle for pod? What is it? More pods in your cast, more cast in your pod?


Natalia Godyla:

More beans in your pod.


Nic Fillingham:

I like that. More beans in your pod. And I think the other thing that's worth reiterating Natalia is if you have a cyber-security topic you would love to learn more about, or a perspective you'd like to hear from, please let us know, we'll go after it for you and try and bring that to a future episode.


Natalia Godyla:

Yes, absolutely. We're really thankful to everyone who has reached out thus far and just keep it coming.


Nic Fillingham:

On today's episode in the first segment, which we call our deep dive, we speak with Ram Shankar Siva Kumar, whose title I will not give away in the intro because we talk about it in the conversation. And it's an awesome one. Ram works in the Azure Trustworthy ML team. And he's here to talk to us about a blog post that Ram co-authored with Ann Johnson that announces a new adversarial ML threat matrix that has been built and published up on GitHub as a collaboration between Microsoft, MITRE, IBM, Nvidia, Bosch, a bunch of other organizations as a sort of open source approach to this upcoming sort of nascent threat category in adversarial machine learning. And it was a great conversation. And then after that, we speak with...


Natalia Godyla:

Justin Carroll of the Microsoft Threat Intelligence Global Engagement and Response team. He started in networking very on the ground and only got his education in cybersecurity later in his career, which I think to anybody out there, who's looking to transition to security, who has a different background in security and is wondering whether they can make it, you can. He also chats a little bit about what inspired him to join cybersecurity. Some of it came from video games, which is a theme we're seeing again and again.


Natalia Godyla:

So he had a unique spin on vigilantism within video games and ensuring that those who had an unfair advantage by using mods were checked and tried to level the playing field for all the rest of the players of that game. And of course we touch on Ninja Turtles, which is really the highlight of the episode. I think, with that on with the pod.


Nic Fillingham:

Ram Shankar Siva Kumar, thank you for joining us on Security Unlocked.


Ram Shankar Siva Kumar:

Hey, thanks for having me, Nick and Natalia. Really appreciate it.


Nic Fillingham:

So we're going to talk about a blog post that you co-authored with the wonderful Ann Johnson. The title is, it's a great title. I'll get straight to the point. Cyber attacks against machine learning systems are more common than you think. Before we get into that, though, I just have to ask, you list your title as data cowboy, which is fantastic. I would love data cowboy, anything cowboy. I would love that for my title. Could you explain to people, what does a data cowboy do and what is the Azure Trustworthy ML group?


Ram Shankar Siva Kumar:

Oh, totally. First of all, this is like every kid's dream is to be Woody from Toy Story. It's just like, I realize it in my own way. So when I joined Microsoft in 2013, there really wasn't an ML engineer position. So my boss was like, "You can be whatever you want. You can pick your own title." I was like, "Yes, Toy Story comes to life." So it was like, this is a brown version of this Woody that you kind of get. So basically what the Trustworthy Machine Learning group does is our promise to Microsoft is to essentially ensure we can enable engineers and customers to develop and deploy ML systems securely. So it's kind of a broad promise that we make to Microsoft and our customers.


Nic Fillingham:

Got it. I would love to come back to just the data cowboy one more time. Tell me what you do. I mean, I have visions of you riding around the office on a hobby horse. Lassoing errant databases. Tell us about your day to day. What does it look like?


Ram Shankar Siva Kumar:

Yeah. So what really happens is that, like I said, I really wish I can ride it on my office, now I am at my home and my 500 square foot apartment- definitely not recommended. But most of the time we end up doing is this wonderful Hiram Anderson who's part of our team, he's militantly looking at how we can detect attacks on machine learning systems. So really working with him and the rest of the Microsoft community to kind of keep our eyes and ears on the ground, see like what sort of attacks on machine learning systems we are seeing, our various different channels and trying to see how we can detect and respond and remediate those sort of attacks. So that's the first one big one. The second thing is like I get to work with a wonderful Will Pears. So I get to work with him to think about actively attacking red teaming Microsoft's machine learning system. So even before our attackers can look at, exploit the vulnerabilities Will and Hiram go and actively attack Microsoft ML systems.


Natalia Godyla:

So how does the work you do connect to the different product groups. So as you're identifying these cyber attacks, are you then partnering with our products to build those into the detections?


Ram Shankar Siva Kumar:

Yeah, that's a great question. So one of the things I really like about Microsoft is that super low slake to meet with somebody from another product team. So the amazing Mira Lane who heads the Azure Cognitive Services, really worked very closely with her. And I believe you ever had a Holly Stewart in your podcast as well, so worked very closely with her team. So it's really a big partnership with working with leaders from across Microsoft and kind of shopping around what we're doing and seeing how we can kind of help them and also learn from them because they also have sensors that necessarily might not have.


Nic Fillingham:

Let's talk about this blog post. So you and Ann both announced this really interesting sort of consortium of 11 organizations, and you're releasing an adversarial ML threat matrix. It's open source, it's on GitHub. Very exciting. Tell us about it.


Ram Shankar Siva Kumar:

So the goal of the adversarial ML threat matrix is essentially to empower the security analyst community so that they can start thinking about building detections and updating their response playbooks in the context of protecting ML systems. And one of the things that's kind of like we want to be mindfully different is the attacks that we see to this framework with, all these techniques, we kind of only put the ones that Microsoft and MITRE jointly vetted that were effective to be against production machine learning systems.


Ram Shankar Siva Kumar:

So first of all, the whole area of attacking machine learning systems goes all the way back to 2004. In fact, you can find Daniel Loud, whose Twitter handle is Dloud on Twitter today. He continues to work on this super cool fields and there's a wonderful timeline by this other researcher called Battista Bisho that he also linked to the blog, but he can basically see that this work has gotten immense academic interests for the last 16 years. And especially in the last four years after a very seminal paper was released in 2014.


Ram Shankar Siva Kumar:

So when a lot of people think about spiel, they think of as, oh, this is something that is really theoretical. This is something that... Oh, Great, you're working in academic setting, but no, that's not true. There are marquee companies, who've all had their ML systems subverted for fun and profit. So the whole point of this blog post with MITRE and this whole corpus of industry organizations was, this is real. Attacks on machine learning systems is real, you need to start thinking about this.


Ram Shankar Siva Kumar:

Gartner released a report on 2019 saying, 30% of all cyber attacks in 2022 is going to involve a tax on machine learning systems. So this is not a pie in the sky. Oh, I'll get to it when I get to it. 2022 was a year and a half, it's a year away from now. So we got together in this blog post to really empower our security analysts community and help them orient for this new threats.


Natalia Godyla:

Can you talk a little bit more about what exactly is the adversarial ML threat matrix and how you envision security analysts using this tool?


Ram Shankar Siva Kumar:

Yeah, totally. So one of the things that before we even put this matrix together, we kind of conducted a survey of 28 organizations. We spoke to everybody from SMBs to governments to large organizations and we spoke to the security analyst Persona, as well as the MLG person. I asked them, "Hey, how do you think about securing ML systems? This is a big deal. What are you doing about it?" And they were like, "Well, we don't have the tools and processes in place to actually go and fix these problems." So the first thing we realized is that we wanted the security analysts community to be introduced to adversarial ML as a field, try to condense the work that's happening in a framework that they already know. Because the last thing we want to do is to put another framework another toolkit on their head.


Ram Shankar Siva Kumar:

And they're just going to be like, "Nope, this is not going to work out. This is one more thing for them to learn." So we took the MITRE's attack framework. So this is something that was again, bread and butter for any security analyst today. So we took the attack framework and we kind of said, "Hey, we've been really cool." If you took all the ML attacks and put it in this framework, and that's exactly what we did. So if you look at our track matrix, it's modeled after the MITRE attack framework.


Ram Shankar Siva Kumar:

So the wonderful folks from MITRE's ML research team and us, we got together and we basically aligned the attacks on machine learning systems, along reconnaissance persistence, model evasion, ex-filtration. So if you look at the top of our matrix, the column headers are essentially tactics and the individual ones are techniques.


Ram Shankar Siva Kumar:

So let's say that an attacker wants to gain initial access to a machine learning subsystem, let's say that's her goal. So she has a couple of options to kind of execute her goal. She has a couple of techniques in her kit. The first thing is that she can just send a phishing email to an ML engineer. That's very valid. Phishing is not going to go away. The second thing that she can do is she can take a pre-trained ML model available that people generally download and she can backdoor it. So the whole point of this attack matrix is to A, build a common corpus of attack techniques and attack tactics in a framework that a security analyst already has knowledge of.


Natalia Godyla:

Are you seeing any trends? What's most common to combine.


Ram Shankar Siva Kumar:

Oh, that's a great question. So before I just step into this, I first want to tell you about this attack called model replication. So the easy way to think about this and Natalia, I will get to this, I promise.


Natalia Godyla:

I love the excitement. I'm so ready for it.


Ram Shankar Siva Kumar:

We're going to take a little detour like Virgil and Homer. So essentially the best way to think about model replication is that open AI is a very famous ML start up. And they last year released a model called GPT-2, and they said, "Hey, you know what? We're not going to release the entire model immediately. We're going to release it in a stage process." We're going to just... because we want to do our own verification and before they could release the entire model, these spunky researchers, so I love that. They're still cool. Vania Cohen. And I know this other person's name is Skylion with a O, they replicated GPT-2 it was like 1.5 billion parameter model, and they've leased it on the internet on Twitter. And they call it open GPT-2. And I love their tagline, which is GPT-2 of equal or lower value.


Ram Shankar Siva Kumar:

So even before the company could release, they replicated the ML model based on the data sets that were available based on the architecture. And they basically at the end of the day, and we also references our case study is that they basically tweaked an existing model to match GPT-2 and they publish that for everybody to use. No, it does not have the same accuracy or the same metrics as the original GPT-2 model. But the fact that an attacker can even replicate a ML model using publicly available data sets and having some insights about the architecture is something for people to think about.


Ram Shankar Siva Kumar:

So now to come back to your excellent question. So what exactly is a common pattern? So what essentially we see attackers doing is that they go interact with the machine learning system, attackers might send some data. They might get some responses back and they keep doing that enough amount of time. And they now have sufficient data to replicate the ML model. So the first step is that they go and replicate the ML model and from the ML model that they have replicated, they go do an offline attack. Because now they their own ML model, they try to evade this ML model and then they find a way to evade the ML model. And they take the examples of the test points that evade the ML model and now evade the online, the real ML that's out there taking that and then boom, fooling the real online ML model. So that's a common data point, but three case studies in our adversarial ML GitHub page that actually kind of shows this.


Nic Fillingham:

So the sort of takeaway from that. If your data set is public, don't make your ML architecture public and or vice versa.


Ram Shankar Siva Kumar:

That's a great question. And I've been thinking about this a lot, first of all, we definitely want to be transparent about the baby builder ML models, right? Marcus Sanovich, Oh gosh, he's such an amazing guy. But for the last so many years in RSA has been like militantly, been talking about how we build our ML models for security purposes, because we want to give insights into our customers about how we actually built ML models. And the data sets are machine learning as a field, it has as norms of opening up our data sets. In fact, one can attribute the entire deep learning revolution to Dr. Fei-Fei Li's image in a dataset which really sparked this whole revolution. So, I really don't want anybody to think that being open with our data sets or being open with our ML platforms is a good idea.


Ram Shankar Siva Kumar:

Because even if you think of traditional cyber security, right? Security by obscurity is never a good strategy. So the way we want to push people to think about is how are you thinking about detection? How are you thinking about response? How are we thinking about remediation? So really trying to take the assumed breach mindset and feeding it into your ML systems is how we want to push the field towards. So if you take away anything from this is continue to be opening your systems for scrutiny, because that's the right thing to do, that's the norms that we've set. And that's important to advance research in this field and think about detection strategies and think about, and assume breach strategies for building ML systems.


Ram Shankar Siva Kumar:

We wanted to distinguish between traditional attacks and attacks on ML systems. So the one thing that I want to think about is the threat matrix contains both traditional attacks and attacks on ML systems. Whereas the taxonomy only contains attacks on ML systems. The second difference is that, like I said, the matrix is meant for security analysts. This one is meant for policymakers and engineers. The third that's the more important difference is that in the context of the threat matrix, essentially we are only putting attacks that we have validated against commercial ML systems. It's not a laundry list of attacks. We're not trying to taxonomize.


Nic Fillingham:

I wonder if you could talk about the approach and the philosophy here for putting this on GitHub and making it open to the community. How do you hope folks will contribute? How would you like them to contribute?


Ram Shankar Siva Kumar:

Yeah, absolutely. So Miguel Rodriguez, who runs the MITRE, who we collaborated with, wonderful team over there before putting this out on GitHub, there was a little bot of angst, right? Because this is not fully baked product. This is something that 13 organizations found useful, but doesn't mean everybody in the community might find useful. And I think he said something to the effect of-


Nic Fillingham:

It's almost as if you're a cowboy.


Ram Shankar Siva Kumar:

Yeah. There you go, herding people. It was like, we're putting this out, acknowledging this is a first cut attempt. This is a living document. This is something that we have found useful as 13 organizations, but we really are hoping to get feedback from the community. So if you're listening to this podcast and you're excited about this, please come and contribute to this matrix. If you think there are attacks that are missing, if you would like to spotlight a case study on a commercial ML system, we are super looking to get feedback on this.


Ram Shankar Siva Kumar:

And we also kind of realized that we wanted a safe space almost to talk about attacks on ML systems. So we were like, you know what? We're just going to have a little Google groups. And the membership of the Google groups is extremely diverse. You've got philosophers that are interested in adversarial machine learning. We've got people who are looking from various perspectives, joining our Google groups and kind of like giving us feedback and how we can make it better.


Natalia Godyla:

Yeah. As you mentioned, there are tons of different perspectives coming into play here. So how do you envision the different roles within the community interacting? What do you think needs to happen for us to be successful in combating these threats?


Ram Shankar Siva Kumar:

Yeah. This is a great question. The one thing that I've learned is that this topic is immensely complex. It's mind boggling to wrap the different personas here. So I'll just give you a rundown, right? So, so far we know that policymakers are interested in securing ML systems because every national AI strategy out there is like, securing ML systems is top priority for them. ML engineers are thinking about this, academic researchers. There were like 2000 papers published in the last, I want to say five or six years on this topic. So they are like a hotbed of research we want to rope into. We've got security analysts from these companies that we're talking to are interested. Csos are also thinking about this because this is a new threat for them. So as a business decision maker, how should they think about this?


Ram Shankar Siva Kumar:

One thing that I got an opportunity with Frank Nagle, who's a professor at HBS. We wrote up piece at Harvard Business Review talking about, is it time to insure ML systems. ML systems are failing so if you're ML powered like vacuum cleaner burns a home down, what do you do about it? We try and rope in the insurers to come in participate in this. So, Natalia this is such a green field and the only way we're going to like get ahead to really get people excited and try for clarity together as a community.


Nic Fillingham:

How would an ML powered vacuum cleaner work?


Natalia Godyla:

I was going to say that sounds like a 2020 headline, ML powered vacuum cleaner burns down house and threat.


Ram Shankar Siva Kumar:

Oh my gosh. So, okay-


Nic Fillingham:

Man bites dog.


Ram Shankar Siva Kumar:

There you go. It's funny because this was not an example that I made up. I wish I did. I know. Yes, Nic. I see, yes.


Nic Fillingham:

What?


Ram Shankar Siva Kumar:

Yes.


Nic Fillingham:

All right.


Ram Shankar Siva Kumar:

This is a well-documented paper called a concrete problems in AI safety. And they talked to the most it's like Final Fantasy. Everything that needs to go wrong is going wrong. So, they're like robots that are burning down homes, breaking things that they can clean up. So if your machine learning system is not trustworthy, there are going to be problems. And you really need to think about that.


Nic Fillingham:

I can't even get my kettle to boil.


Ram Shankar Siva Kumar:

But the thing that really worries me is ML applications used in health care. You keep seeing headlines like machine learning systems being used by radiologists, amidst radiologists when it comes to identifying Mulligan tumors and things like that. There's a fantastic work by Samuel Finlayson from Harvard. He show that if you take an x-ray image, just take it and slightly rotate it and you give it to the ML system. It goes from very confidently thinking that it's malignant to very confidently judging it's benign. And that is really scary.


Ram Shankar Siva Kumar:

In the beginning of the podcast, we spoke a lot about how an adversary can subvert machine learning systems for fun and profit. Oh boy, there is an entirely separate world of how machine learning systems can fail by themselves. What we call unintentional failure modes. And trust me, you will want to go live in the middle of the North cascades in a cabin after you read that work. It'd be like, I am not getting anything ML powered until they figure this out. But the good news is there're extremely smart people, including Hiram and Will from my team who are looking into this problem. So you can feel a little bit like a shore that they're the true Avengers out there.


Natalia Godyla:

I love all the head nods from Nic. I feel like it underscores the fact that we only know a percentage of the knowledge on ML. So we just need a community behind this. No one company person can know all of it.


Ram Shankar Siva Kumar:

Absolutely. Oh my gosh. Yeah. When we open the adversarial ML threat matrix Google group, we now went from zero. We felt like nobody's going to join this Google group. It's going to be like a pity party where I'm going to email Michel from MITRE and he's going to respond back to me. But no, we went from zero to 150 right now over just the last four days.


Natalia Godyla:

Ram, thank you for giving us all of this context on the adversarial ML threat matrix. So what's Microsoft's continued role. What's next for you in ML?


Ram Shankar Siva Kumar:

First of all, we are hiring. So, if you'd like to come and join us, we are looking for developers to come and join us in this quest. So please email anybody, even Nic, and he can forward his resume.


Nic Fillingham:

Do you need to have a cowboy hat? Is a cowboy hat a necessity?


Ram Shankar Siva Kumar:

Not at all. We will accept you for who you are.


Natalia Godyla:

Do you provide the cowboy hats?


Ram Shankar Siva Kumar:

We will provide everything. Anything to make you feel comfortable. So we are growing and we'd love to work with the folks. With the adversarial ML threat matrix, like I said, we really are looking for feedback from the community. We really think that like Natalia very correctly pointed out this is a problem so big that we can only solve it if we all come together. So please go to our GitHub link. I'm sure Nic and Natalia might put the link to it. We'd love to get their feedback.


Ram Shankar Siva Kumar:

The second thing is if you kind of are... We are especially looking for people to come in at case studies, if you think we're missing a tactic, or if you think that you've seen an attack on a ML system on a commercial Ml system, please reach out to us and we'd be happy to include that in the repository.


Nic Fillingham:

If your autonomous vacuum cleaner has attempted to undermine democracy, let us know.


Ram Shankar Siva Kumar:

And the one thing that I want everybody to take away is that when we did our survey, 25 out of 28 organizations did not have tools and processes to kind of secure the ML systems. So if you're listening to this podcast and you're like, "Oh my gosh, I don't have a guidance." Do not feel alarmed. You're tracking with the majority of the industry. In fact, three organizations, all of whom were large in our survey even thought about this problem. So there are tools for you and processes that we put out. So in our docs at Microsoft.com, there's a chat modeling guidance, there's taxonomy, there's a bug bar that you can give to your incident responders so that they can track bugs. And for the security analysts community, there is the adversarial ML chat matrix. So please go read them and please give us feedback because we really want to grow.


Natalia Godyla:

I love it. Thank you for that. That's a great message to end on.


Ram Shankar Siva Kumar:

Awesome. Thank you, Nic and Natalia for having me. Really appreciate it. This was really fun.


Natalia Godyla:

And now let's meet an expert in the Microsoft security team to learn more about the diverse backgrounds and experiences of the humans, creating AI and tech at Microsoft. Today, we're joined by Justin Carroll, threat analyst on the Microsoft threat intelligence, global engagement and response team. Well thank you for joining us, Justin.


Justin Carroll:

Thanks for having me.


Natalia Godyla:

Well can we kick things off by you just sharing your role at Microsoft. What does your day to day look like?


Justin Carroll:

So my role is related to threat hunting across large data sets to find advanced adversaries and understand what they're doing. Look for detection opportunities and communicate out the behaviors of the specific threats that we're finding to partner teams or to our customers to help them understand the threat landscape and kind of staying on top of what attackers are doing.


Natalia Godyla:

That's super interesting. And can you talk a little bit about any recent patterns that you've identified or interesting findings in your last six, eight months?


Justin Carroll:

Well, it's been a busy six or eight months, I would say, because everybody's been very busy with COVID. We've been seeing quite a large increase in human-operated ransomware and stuff like that. So I've been working really hard to try and figure out different ways to try and surface their behaviors as early as we can to customers to help them take action before the ransom happens. And we've been seeing quite a few other different really advanced adversaries compromising networks.


Justin Carroll:

A lot of it's kind of the same old, same old, just more of it, but it's always interesting and there's never a shortage of new findings each day and kind of moments of, "Oh, that looks like this, or they're doing this now." Awesome. Great.


Natalia Godyla:

You mentioned you're constantly trying to find new ways to identify these faster. What are the techniques that you're trying to use to find the threats quicker?


Justin Carroll:

There's a whole bunch of different ways that you kind of try and surface the threats quicker. Some of it's research and reading other people's work and blogs and stuff like that. I tend to live in the data most of all, where I'm constantly looking at existing attacks and then trying to find similar related behaviors or payloads or infrastructure and pivoting on those to try and attempt to find the attack, to be ready to find it as early as possible. And what's called the kill chain.


Justin Carroll:

So from the time that the attacker gets in the network, how quick can we find them before they've had a chance to conduct their next set of actions? So whether if they're stealing credentials or something like that, can we surface them before they've had a chance to do the credential theft and then kind of always trying to move earlier and earlier in the kill chain to understand how they got there. And then what are some of the first things that they did when they did get there and how do we surface those next?


Justin Carroll:

Because a lot of those are a little bit more difficult to surface because it can kind of tend to blend in with a lot of other legitimate activities.


Nic Fillingham:

What kind of tools do you use Justin? Are you in network logs and sort of writing queries, is there a big giant futuristic dashboard that you sit in front of and you have virtual reality gloves moving big jumps of numbers left and right. Well, what are the tools of your trade?


Justin Carroll:

So one of the tools that we use a lot, there is a bunch of data that's stored... Customer facing, it's usually called Azure data Lake. It's these huge databases with large amounts of information where you can construct queries with what's called KQL, I believe it's Kusto query language. So there's a specific tool for kind of deep diving into all of that data across our many different sources. And then using that to basically structure and create different queries or methods of finding interesting data and then kind of pivoting on that data.


Justin Carroll:

Then in addition, I've built some of my own tools to kind of help improve my efficiency or automate some of the stuff that I have to do all the time and then just to make me faster at hunting for the things that I'm looking for.


Nic Fillingham:

Is it an AI version of yourself? Is it a virtual Justin?


Justin Carroll:

No. We work with the ML team to try and share as much knowledge with them as possible. There is no tool for an AI Justin, as of yet.


Nic Fillingham:

Well, let's back it up a bit. So one of the things we would like to do in these interviews with the security SMEs, I'm not even sure if we've explained what an SME yet. We call it a Subject Matter Expert. That's an acronym. We use a lot here at Microsoft. I think it's pretty broadly known, but if you've heard of SME or SME, that's what it means.


Nic Fillingham:

Now, you and I, we crossed paths about a year ago for the first time when Jessica Payne, who actually hasn't been on the podcast yet, Jessica introduced me to you and she said, "You have to talk to Justin." And she gave me three sort of very disparate, but intriguing bits of data about you. She said, "Justin used to climb telegraph poles. He is a big Star Wars fan and is in a metal band." And I'm sure I've gotten those three things slightly wrong. Could you kind of talk about your journey into the security space and then sort of how you found yourself working for Microsoft. But first of all, these three things that Jessica told me are any of them true?


Justin Carroll:

Mostly they are. So some of these will kind of combine for the telephone climbing aspect. I used to work for a wireless internet provider that had leases or specific towers, cell phone towers or other towers on top of mountains, essentially, where we would have wireless radio dishes that would communicate to each other. So I was occasionally tasked with installing and or fixing said towers, which is okay if you are fine with heights, I wasn't at first, but you just kind of get used to it. And you kind of realize once you're above 20 feet, it really doesn't make any difference. If you fall, it's going to hurt, but climbing a tower in the winter and in the wind and where you can barely feel your hands and all that wasn't great.


Justin Carroll:

I was a pretty big Star Wars fan growing up as a kid, even more of a Ninja Turtle fan. And as for metal, I used to be in a band with some friends and have been playing guitar for 25 or 26 years. And music has been a very huge part of my life and remains to be.


Nic Fillingham:

I think we'll circle back to Ninja Turtles. I'm not going to let that one go, but so let's talk about your path into security. So was this you're working for the wireless internet provider was this your first job. Was this mid career. Where does that fit in your sort of LinkedIn chronology? And at what point did you use formerly into insecurity?


Justin Carroll:

So it's been a long and winding road to get here I would say. So the internet provider was what I would guess I'd call my first career job of sorts. I had started there in my early 20s and worked for them for about... sorry my cat is right in front of the microphone. One second.


Nic Fillingham:

There's a cat there.


Justin Carroll:

She wanted to say her piece. So I worked for the internet company for just under a decade. I used to do some networking type fun stuff in Halo 2, to kind of maybe garner a little bit of an advantage, I guess I would say, and use those learned skills to land that first job. And I did that for quite a while, but realized I was kind of stuck in this job. It was in a city that I didn't want to live in. And I had kind of maxed out my capabilities there. I had attempted to move to Portland because I wanted to have a bigger city experience. I applied to 254 jobs, got one interview for basically an office tech support role was the only position I got hired, but it wasn't feasible to live in Portland.


Justin Carroll:

So after quite a bit of soul searching and realizing that basically nobody cared that I had eight years of on the job experience because I didn't have a college degree. There were not any doors open for me for the most part. I then decided to take a pay cut and go get a job at a university that was just a city over and work full-time and go to school for a degree in cybersecurity while working full-time for the university doing kind of technical work for them, helping them understand their... Sorry, my cat is a whole thing right now.


Nic Fillingham:

Your cat's just trying to interject with like don't. Hey, you glossed over that Halo 2 thing, you better to come back to that.


Justin Carroll:

Aria, come here.


Nic Fillingham:

We're leaving all this in, by the way.


Natalia Godyla:

Yeah. We're very much enjoying it.


Justin Carroll:

So kind of advising the university on different technologies that they could use for their students. So I did that for about three and a half years while going to school and then graduated top of my class and applied for another 150 some odd jobs and mostly the Seattle area this time and was about to give up because even though I now had a degree and almost 10 years of experience, it still wasn't enough. And everybody that I kept losing to had between 10 and 20 years experience. And it just wasn't an option for folks with less specific cybersecurity experience to kind of enter the field.


Justin Carroll:

There were a lot of walls that were put up. I had a friend of a friend who worked for cybersecurity at a company somewhere in Arizona, who I'd never met. And he decided to go out of his way, even though I'd never met him and looked for some cybersecurity type jobs in my area that he thought maybe I'd be good for and helped me look at my resume and stuff like this. And that helped me land a vendor role for Microsoft, where I kind of started my path and career towards cybersecurity specific stuff.


Justin Carroll:

I had basically given up at that point on ever working in cybersecurity and had kind of thought that it just wasn't meant for me. So that was kind of a big break and a guy almost closed the application to apply for the job and then figured what's the worst they can say is no, that is kind of how I finally got to Microsoft and cybersecurity, where I was able to work as a vendor for the team evaluating kind of telemetry. And I was kind of given an opportunity to learn a lot and that eventually transitioned into when a position became available, where I started working full-time as a Microsoft employee and went from there.


Natalia Godyla:

So what in your soul search brought you to cyber security? Was it your background, the fact that you already had those foundations as a network admin, or was there something in particular in the cybersecurity world that just attracted you?


Justin Carroll:

I'd always found it fascinating. When I started university, they just launched the cybersecurity program. The quarter that I started there, and one of my friends who was a computer science major, basically called me up immediately and was like, "Hey, they just launched this. You need to do this." And there's the very popular culture aspect of it where everybody thinks it's fascinating and you sure there was a little bit of a grab with that. But I like learning how computers work and I like kind of the constant problem solving nature of everything. And the first class I took on it I was hooked and still remains that day where it's just, it's fascinating and it's really fun to just kind of continually work to see what attackers are doing. But I also, there's a huge aspect of it like I like helping people. I think it's important and having a role where I'm able to help millions or even potentially billions of people through better detections or stopping malware. It feels pretty great.


Nic Fillingham:

What other aspects Justin, of your path to security, your path to Microsoft, do you feel you're sort of bringing forward? I want to ask about you very briefly mentioned something about Halo 2 and I want to know what that was. And then I wonder if there were other sort of dare I say, sort of maybe unorthodox or non-traditional things that you worked on where you learned a bunch of bunch of tools or tricks of the trade that you're bringing forward to your work right now.


Justin Carroll:

So Halo 2 was a fun one. Back in those days, there were lots of what were called modders, who would mod their Xbox's to gain an unfair advantage. So I would use my networking know-how basically, and learned a lot of it too, when encountering a modder to kick them out of the game. I think it was possibly a little frowned upon, but I was tired of having cheaters constantly win, so I did a lot of research and I didn't know a whole lot about networking at that point, but I tried to not use it as a competitive advantage, but more to just level the playing field, but it was a great way to learn how firewalls worked and network traffic and building more on my understanding of computers.


Justin Carroll:

And then, kind of, that set a foundation for me, of understanding, there's always going to be stuff that I don't know and what I have done, but I did it all through college and continued all the way till basically getting full-time employment at Microsoft was I set up a lab environment and I would set up servers and clients and I would attack them and monitor the logs on my own little private lab on my machine and see what worked, what didn't, try and figure out why it worked, what didn't and try and build different tools to see how I could make it more effective or deal with different issues.


Justin Carroll:

Just kind of both playing attacker and defender at the same time on my network, all by myself, essentially and kind of learning from all of that data was massively important and anybody who's looking to get into security, I highly recommend both learning how to attack, on a safe, your own little lab environment where you're not hurting anybody. And what's it like to try and defend and find those attacks because both sides are-


Nic Fillingham:

Red Justin versus blue Justin.


Justin Carroll:

Exactly. Yes.


Natalia Godyla:

You noted earlier that just the sheer amount of data can be overwhelming, especially as you moved through your career and then came to Microsoft where we have billions of signals. So the same transition happens from Halo to now just the sheer scale and scope of your role and the amount of good that you can do. So, how did you handle that overwhelming amount of information, amount of impact that you can have?


Justin Carroll:

So when I was first brought on one of the things that made a significant difference was I had somebody that kind of instructed me in a lot of the ways of kind of how to work with the data, but I was also given quite a bit of an area for trial and error. So there was lots of opportunity to fail and to learn from what didn't work and to kind of keep building on that. And then any time that I got stuck or I would kind of just do everything I could to attempt to solve the problem or work with the data. If I kind of hit a wall that I couldn't climb on my own, I could go to him and then we would solve it together. So it was kind of both a mentoring and a guidance thing, but also kind of given that ability to experiment and try and learn. So that was kind of one of the biggest ways of learning to pivot on that data and understand it and consume it.


Justin Carroll:

And then honestly, collaboration with other folks on my team and other team was massively instrumental to be able to kind of learn what they had already learned or pass on my knowledge to them. And just that constant sharing and understanding because there is so much data, it's quite impossible almost to be an expert at all of it. So having those folks that you can reach out to you that are experts in each basically set of their data. So you can understand what the data is trying to tell you, because that's one of the things that is particularly difficult is to take the data and actually glean understanding from it. The data is trying to tell you something, you just need to make sure you're interpreting the message correctly.


Natalia Godyla:

How do AI and ML factor into your role into helping you manage this data and collaborating with other teams.


Justin Carroll:

So I work quite a bit with a lot of different data science folks on a few different teams to either use a lot of the models that they're creating to kind of a source, a lot of the malicious information or a particular attackers or stuff like that. And then also collaborating back in sharing my knowledge and intelligence to them to say, this is what an attack looks like. This is what it should look like in the data and kind of giving them the ideas and signals for what they should be looking in their data to kind of train those models.


Justin Carroll:

It's really important to have that partnership between security and data science for AI and ML to kind of help them understand the security sphere of it. And then they can kind of take the real math and data prowess that they've got and turn our knowledge into ML or AI to detect and surface a lot of these things.


Nic Fillingham:

If it's possible, Justin, how would you sort of summarize your guidance to other Justin Carroll's that are out there that are... They want to get into security, they're fascinated by cybersecurity in sort of a macro sense, but they feel either don't have a degree or they're not even sure what they should go study or they're trying to work at, how can they translate their current sort of career experience and sort of skills? Can you summarize that into some guidance of what folks should do to try and break in?


Justin Carroll:

Sure. One, if you're in school, remember that school is not going to teach you a lot of the stuff that you need to know. It's lots of taking what you're learning and building upon it outside. So if it's cybersecurity, that's an interest, try and experiment and fail. Cyber security is huge. There are so different facets of it. Find out the thing that kind of scratches the itch and piques your interest. For me, that was setting up a lab, right? Where I could play both the attacker, the defender, the person monitoring logs, the person setting up all the configurations to try and stop the attacks and was able to kind of see all different aspects of the industry.


Nic Fillingham:

So just jumping in, was that literally just a bunch of VMs on your machine or did you have multiple PCs sort of networked together? Just very quickly, what did that look like? How accessible is setting up a lab? I guess I'm what I'm asking.


Justin Carroll:

It is pretty accessible. So while I was in college, it was actually multiple machines and I had four different machines and I set up a router that you can pick up for 50 bucks and a smart switch that I could mirror the traffic on to understand everything for 100 bucks. So there's a little bit of cost. That was kind of my college setup. And as I was kind of learning where I at that point, it made a little more sense to do it with actual machines and for extra clarity. My college was only a couple of years ago. I did not go to college young. So the next route that I did once I headlined did my vendor role and was kind of like security is for me and I want to keep building on it.


Justin Carroll:

I did it all with VMs. So I just had a desktop computer that had okay specifications and I configured two clients, the domain controller, server on the device and then a mail server. And then basically you just connect to each client and then network them all together. So at that point you can use VirtualBox, you can use lots of different stuff. So the availability of doing that, it's actually pretty good. There isn't a lot of overhead costs or anything like that. You just have to have a okay computer.


Natalia Godyla:

What about resources to learn how to do all of that? Are there organizations or sites that someone could turn to, if they're interested in starting to do some of this starting to experiment with what they're interested in?


Justin Carroll:

Honestly, I would say one of the best resources that I had throughout was YouTube. It was a great place to get walkthroughs for every different thing. So like I wanted to learn how to set up a VM and configure it with networking to another VM. I turned to YouTube. I wanted to learn how to attack the VM using Kali Linux, YouTube. And there's a whole bunch of different channels out there that specifically focus on that. And then the other thing is because it's so much more open for creators to share content. You can find people who are at a similar level or maybe just a few steps ahead of you. So you can really kind of join along with other people.


Justin Carroll:

There are a few websites for coding, I think one's called hacking the box as far as attacking different things. And that was also kind of fun where a lot of the devices that need to be attacked we're already pre-configured for you. But for me, honestly, a lot of the fun was setting up those devices and then learning what I did that worked and didn't and what allowed it to be attacked and what I could do to stop that.


Natalia Godyla:

Quick plug Microsoft security also has a YouTube channel in case somebody would like to get any, how to content on our products.


Nic Fillingham:

Natalia may or may not have been involved in that channel, just full disclosure there.


Natalia Godyla:

Yeah. I couldn't help myself. But it is also great to hear that you found people to work with in the community as well. That's something that's been noted by a few of our guests, like Michelle Lamb, that as she was entering the space, she found mentors. She found conversations, people readily available to either work on a problem alongside her, or just answer questions. So I'm glad that you've also been able to turn to the community for that. So what's next for you? Is there a new challenge that you'd like to solve?


Justin Carroll:

Definitely want to work on the toolkit that I'm building and kind of continue that growth. It's been interesting to kind of see the hurdles I run into. And even last week I ran into one that felt insurmountable and was able to chat with one of the devs and solve in a few minutes and learned a whole lot and going forward, now I have that in my pocket. And then both-


Nic Fillingham:

Hang on. Did you say you went from found a new challenge, thought all this is insurmountable and then a few minutes later you solved it?


Justin Carroll:

With a little support from people that knew how to solve the problems. So collaborating with like one of the other devs on the team and basically having him kind of explain the part it felt like a giant wall, but really once you kind of have somebody to break it down a little bit for you, it was just like, "Oh, okay. I see what I'm missing here." And then it was just like, "Got it. Okay. Moving forward."


Nic Fillingham:

Oh, I see. So that that's more an endorsement. Yeah, I got it.


Justin Carroll:

Yeah. Yeah. It's more an endorsement of others teaching abilities and just kind of those times of being able to reach out to others for when you really get stuck and how much of a difference it can make. I had spent an hour on something and was just like, this is ridiculous. This should work. Why isn't it working? What's wrong with me. I'm not smart. And then just chatting with them a little bit and then figuring it out and then like, "Oh, okay. Oh, okay. That's actually pretty simple." I wasn't thinking about it in the right way and kind of getting that other perspective.


Justin Carroll:

And then what's next kind of going forward is a kind of continued partnership with a lot of the data science folks to, I think we've only scratched the surface in many ways as an industry on how data science and cybersecurity can work together. So I am very excited to kind of see what kind of stuff we can accomplish, whether it's, you know, surfacing attacks shortly after they happen, very early in the kill chain or understanding related behaviors and trying to understand who the might be, or I think most of all, the intent of the attack or adversary.


Justin Carroll:

Intent can sometimes be a very difficult to suss out, even for SOCs and their entire center. They have all these folks that are trying to figure out what happened. Why did it happen? What does it actually mean? So if we can have data science that can provide a lot of context on that, through understanding existing attacks and modeling what future ones might look like, I think there's some pretty exciting opportunities there.


Nic Fillingham:

All right, I'm doing it. We're coming to Teenage Mutant Ninja Turtles. You're a fan. How much of a fan are you, Justin?


Justin Carroll:

I'd say quite a fan. I do have a couple of figurines and a mint package unopened from '87 I think, something like that. And then have a Ninja Turtles tattoo on my back of Raphael. So that was kind of one of those moments where I was trying to think about what steps I wanted to take forward in life and things like that. And I had kind of thought about what are the things that actually make me happy?


Justin Carroll:

This was probably my mid 20s quarter life crisis kind of thing. And I was like, "I always liked the Ninja Turtles as a kid." They always brought me great joy. I still get excited about watching them. The movies are definitely a guilty pleasure. I realized they're not great. But now I'm talking about the original movies, not the new ones. We won't talk about the new movies. And it was just one of those like, "Yeah, I identify with this. This is a huge part of my life. It's been around since I was... it was started the year I was born." So I was just like, "All right, let's do it." And haven't regretted it at all.


Nic Fillingham:

I was going to ask who your favorite turtle was, but you've obviously... If you've inked Rafaelle on your back so that question is moot. I'm a Donatello guy. I've always been a Donatello guy.


Justin Carroll:

I would think of myself as Raf, but really I'm more of a Donatello. Ralph was kind of the cool guy with a little bit of an attitude, but really I was Donatello. When I was 10 dressed up for Halloween, I was Donatello. I'm definitely Donatello with a little bits Raf thrown in for good measure.


Nic Fillingham:

Well, this has been a blast. Thank you, Justin, for walking us down, Teenage Mutant Ninja Turtle memory lane, and Halo 2 memory lane and sharing your story with us. It was great. Wonderful to get your perspective. Great to have you as a part of the threat hunter team here at Microsoft and contributing in all the ways that you do. Thanks for joining us. I'm sure we'll talk to you again at some point on the Security Unlocked podcast, but keep doing you Cowabunga, dude.


Justin Carroll:

Thanks very much for having me. I appreciate it. It was great to talk to you all.


Natalia Godyla:

Well, we had a great time unlocking insights into security from research to artificial intelligence. Keep an eye out for our next episode.


Nic Fillingham:

And don't forget to tweet us @msftsecurity or email us at securityunlocked@microsoft.com with topics you'd like to hear on a future episode. Until then stay safe.


Natalia Godyla:

Stay secure.

More Episodes

2/24/2021

Judging a Bug by Its Title

Ep. 16
Most people know the age-old adage, “Don’t judge a book by its cover.” I can still see my grandmother wagging her finger at me when I was younger as she said it. But what if it's not the book cover we’re judging, but the title? And what if it’s not a book we’re analyzing, but instead a security bug? The times have changed, and age-old adages don’t always translate well in the digital landscape.In this case, we’re using machine learning (ML) to identify and “judge” security bugs based solely on their titles. And, believe it or not, it works! (Sorry, Grandma!)Mayana Pereira, Data Scientist at Microsoft, joins hosts Nic Fillingham and Natalia Godyla to dig into the endeavors that aresaving security experts’ time. Mayana explains how data science and security teams have come together to explore ways that ML can help software developers identify and classify security bugs more efficiently. A task that, without machine learning, has traditionally provided false positives or led developers to overlook misclassified critical security vulnerabilities.In This Episode, You Will Learn:• How data science and ML can improve security protocols and identify and classify bugs for software developers• How to determine the appropriate amount of data needed to create an accurate ML training model• The techniques used to classify bugs based simply on their titleSome Questions We Ask:• What questions need to be asked in order to obtain the right data to train a security model?• How does Microsoft utilize the outputs of these data-driven security models?• What is AI for Good and how is it using AI to foster positive change in protecting children, data and privacy online?Resources:Microsoft Digital Defense Reporthttps://www.microsoft.com/en-us/security/business/security-intelligence-reportArticle: “Identifying Security Bug Reports Based Solely on Report Titles and Noisy Data”https://docs.microsoft.com/en-us/security/engineering/identifying-security-bug-reportsMayana’s LinkedInhttps://www.linkedin.com/in/mayana-pereira-2aa284b0Nic’s LinkedInhttps://www.linkedin.com/in/nicfill/Natalia’s LinkedInhttps://www.linkedin.com/in/nataliagodyla/Microsoft Security Blog:https://www.microsoft.com/security/blog/Transcript(Full transcript can be found at https://aka.ms/SecurityUnlockedEp16)Nic Fillingham:Hello, and welcome to Security Unlocked, a new podcast from Microsoft where we unlock insights from the latest in news and research from across Microsoft Security engineering and operations teams. I'm Nic Fillingham-Natalia Godyla:And I'm Natalia Godyla. In each episode we'll discuss the latest stories from Microsoft Security, deep dive into the newest threat, intel, research and data science-Nic Fillingham:And profile some of the fascinating people working on artificial intelligence in Microsoft Security.Natalia Godyla:And now let's unlock the pod.Natalia Godyla:Hello, Nic. How's it going?Nic Fillingham:Hello, Natalia. Welcome back. Well, I guess welcome back to Boston to you. But welcome to Episode 16. I'm confused because I saw you in person last week for the first time. Well, technically it was the first time for you, 'cause you didn't remember our first time. It was the second time for me. But it was-Natalia Godyla:I feel like I just need to justify myself a little bit there. It was a 10 second exchange, so I feel like it's fair that I, I was new to Microsoft. There was a lot coming at me, so, uh-Nic Fillingham:Uh, I'm not very memorable, too, so that's the other, that's the other part, which is fine. But yeah. You were, you were here in Seattle. We both did COVID tests because we filmed... Can I say? You, you tell us. What did we do? It's a secret. It is announced? What's the deal?Natalia Godyla:All right. Well, it, it's sort of a secret, but everyone who's listening to our podcast gets to be in the know. So in, in March you and I will be launching a new series, and it's a, a video series in which we talk to industry experts. But really we're, we're hanging with the industry experts. So they get to tell us a ton of really cool things about [Sec Ups 00:01:42] and AppSec while we all play games together. So lots of puzzling. Really, we're just, we're just getting paid to do puzzles with people cooler than us.Nic Fillingham:Speaking of hanging out with cool people, on the podcast today we have Mayana Pereira whose name you may have heard from a few episodes ago Scott Christiansen was on talking about the work that he does. And he had partnered Mayana to build and launch a, uh, machine learning model that looked at the titles of bugs across Microsoft's various code repositories, and using machine learning determined whether those bugs were actually security related or not, and if they were, what the correct severity rating should be. Nic Fillingham:So this episode we thought we'd experiment with the format. And instead of having two guests, instead of having a, a deep dive upfront and then a, a profile on someone in the back off, we thought we would just have one guest. We'd give them a little bit extra time, uh, about 30 minutes and allow them to sort of really unpack the particular problem or, or challenge that they're working on. So, yeah. We, we hope you like this experiment.Natalia Godyla:And as always, we are open to feedback on the new format, so tweet us, uh, @msftsecurity or send us an email securityunlocked@microsoft.com. Let us know what you wanna hear more of, whether you like hearing just one guest. We are super open. And with that, on with the pod?Nic Fillingham:On with the pod.Nic Fillingham:Welcome to the Security Unlocked podcast. Mayana Pereira, thanks for joining us.Mayana Pereira:Thank you for having me. I'm so happy to be here today, and I'm very excited to share some of the things that I have done in the intersection of [ML 00:03:27] and security.Nic Fillingham:Wonderful. Well, listeners of the podcast will have heard your name back in Episode 13 when we talked to Scott Christiansen, and he talked about, um, a fascinating project about looking for or, uh, utilizing machine learning to classify bugs based simply on, on their title, and we'll get to that in a minute. But could you please introduce you- yourself to our audience. Tell us about your title, but sort of what does that look like in terms of day-to-day and, and, and the work that you do for Microsoft?Mayana Pereira:I'm a data scientist at Microsoft. I've been, I have been working at Microsoft for two years and a half now. And I've always worked inside Microsoft with machine learning applied to security, trust, safety, and I also do some work in the data privacy world. And this area of ML applications to the security world has always been my passion, so before Microsoft I was also working with ML applied to cyber security more in the malware world, but still security. And since I joined Microsoft, I've been working on data science projects that kinda look like this project that we're gonna, um, talk today about. So those are machine learning applications to interesting problems where we can either increase the trust and the security Microsoft products, or the safety for the customer. You know, you would develop m- machine learning models with that in mind. Mayana Pereira:And my day-to-day work includes trying to understand which are those interesting programs across the company, talk to my amazing colleagues such as Scott. And I have a, I have been so blessed with an amazing great team around me. And thinking about these problems, gathering data, and then getting, you know, heads down and training models, and testing new machine learning techniques that have never been used for a specific applications, and trying to understand how well or if they will work for those applications, or if they're gonna get us to better performance, or better accuracy precision and those, those metrics that we tend to use in data science works. And when we feel like, oh, this is an interesting project and I think it is interesting enough to share with the community, we write a paper, we write a blog, we go to a conference such as RSA and we present it to the community, and we get to share the work and the findings with colleagues internal to Microsoft, but also external. So this is kinda what I do on a day-to-day basis.Mayana Pereira:Right now my team is the data science team inside Microsoft that is called AI For Good, so the AI for Good has this for good in a sense of we want to, to guarantee safety, not only for Microsoft customers, but for the community in general. So one of my line of work is thinking about how can I collaborate with NGOs that are also thinking about the security or, and the safety of kids, for example. And this is another thing that I have been doing as part of this AI for Good effort inside Microsoft.Natalia Godyla:Before we dive into the bug report classification project, can you just share a couple of the projects that your team works for AI for Good? I think it would be really interesting for the audience to hear that.Mayana Pereira:Oh, absolutely. So we have various pillars inside the AI for Good team. There is AI for Health, AI for Humanitarian Action, AI for Earth. We have also been collaborating in an effort for having a platform with a library for data privacy. It is a library where we have, uh, various tools to apply the data and get us an output, data with strong privacy guarantees. So guaranteeing privacy for whoever was, had their information in a specific dataset or contributed with their own information to a specific research and et cetera. So this is another thing that our team is currently doing.Mayana Pereira:And we have various partners inside and outside of Microsoft. Like I mentioned, we do a lot of work in NGOs. So you can think like project like AI for Earth several NGOs that are taking care of endangered species and other satellite images for understanding problems with the first station and et cetera. And then Humanitarian Action, I have worked with NGOs that are developing tools to combat child sexual abuse and exploration. AI for Health has so many interesting projects, and it is a big variety of projects. Mayana Pereira:So this is what the AI for Good team does. We are, I think right now we're over 15 data scientists. All of us are doing this work that it is a- applied research. Somehow it is work that we need to sit down with, with our customers or partners, and really understand where the problem is. It's usually some, some problems that required us to dig a little deeper and come up with some novel or creative solution for that. So this is basically the overall, the AI for Good team.Nic Fillingham:Let's get back in the way back machine to I think it was April of 2020, which feels like 700 years ago.Mayana Pereira:(laughs) Nic Fillingham:But you and Scott (laughs) published a blog. Scott talked about on Episode 13 called securing Nic Fillingham:The s- the software development lifecycle with machine learning, and the thing that I think both Natalia and I picked up on when Scott was talking about this, is it sounded first-, firstly it sounded like a exceptionally complex premise, and I don't mean to diminish, but I think Natalia and I were both "oh wow you built a model that sort of went through repro steps and passed all the logs inside of security bugs in order to better classify them but that's not what this does", this is about literally looking at the words that are the title of the security bug, and then building a model to try and determine whether it was truly security or something else, is that right?Mayana Pereira:That's exactly it. This was such an interesting project. When I started collaborating with Scott, and some other engineers in the team. I was a little skeptical about using only titles, to make prediction about whether a bug has, is security related or not. And, it seems. Now that I have trained several models and passed it and later retrained to- to get more of a variety of data in our model. I have learned that people are really good at describing what is going on in a bug, in the title, it feels like they really summarize it somehow so it's- it's doing a good job because, yes, that's exactly what we're doing, we are using bug titles only from several sources across Microsoft, and then we use that to understand which bugs are security related or not, and how we can have an overall view of everything that is happening, you know in various teams across different products. And, that has given a lot of visibilities to some unknown problems and some visibility to some things that we were not seeing before, because now you can scan, millions of bugs in a few seconds. Just reading titles, you have a model that does it really fast. And, I think it is a game changer in that sense, in the visibility and how do you see everything that is happening in that bug world.Natalia Godyla:So what drove that decision? Why are we relying only on the titles, why can't we use the- the full bug reports? Mayana Pereira:There are so many reasons for that. I think, the first reason was the fact that the full bug report, sometimes, has sensitive information. And we were a little bit scared about pulling all that sensitive information which could include passwords, could include, you know, maybe things that should not be available to anyone, and include that in a- in a VM to train a model, or, in a data science pipeline. And, having to be extremely careful also about not having our model learning passwords, not having that. So that was one of the big, I think incentives off, let's try titles only, and see if it works. If it doesn't work then we can move on and see how we can overcome the problem of the sensitive information. And it did work, when we saw that we had a lot of signal in bug titles only, we decided to really invest in that and get really good models by u- utilizing bug titles only. Nic Fillingham:I'm going to read from the blog just for a second here, because some of the numbers here, uh, are pretty staggering, so, again this was written 2020, uh, in April, so there's obviously, probably updated numbers since then but it said that Microsoft 47,000 developers generate nearly 30,000 bugs a month, which is amazing that's coming across over 100 Azure DevOps and GitHub repositories. And then you had it you, you actually have a count here saying since 2001 Microsoft has collected 13 million work items and bugs which I just thinks amazing. So, do you want to speak to, sort of, the volume of inputs and, sort of, signals here in to building that model and maybe some of the challenges, and then a follow on question is, is this model, still active today, is this- is this work still ongoing, has it been incorporated into a product or another, another process?Nic Fillingham:Do you want to start with, with numbers or. Mayana Pereira:Yes, I think that from my data scientist point of view, having such large numbers is absolutely fantastic because it gives us a historical data set, very rich so we can understand how data has evolved over time. And also, if this- the security terminology has changed the law, or how long will this model last, in a sense. And it was interesting to see that you can have different tools, different products, different things coming up, but the security problems, at least for, I would say for the past six, seven years, when it comes to terminology, because what I was analyzing was the terminology of the security problems. My model was a natural language processing model. It was pretty consistent, so that was really interesting to see from that perspective we have. And by having so much data, you know, this amazing volume. It helped us to build better classifiers for sure. So this is my- my data scientist side saying, amazing. I love it so much data.Nic Fillingham:What's the status of this project on this model now.? Is it- is it still going? Has it been embedded into another- another product, uh, or process?Mayana Pereira:Yes, it's still active. It's still being used. So, right now, this product. This, not the product- the product, but the model is mainly used by the customer security interest team in [Sila 00:16:16], so they use the model in order to understand the security state of Microsoft products in general, and, uh, different products and looking at specific products as well, are using the model to get the- the bugs statistics and security bugs statistics for all these different products across Microsoft. And there are plans on integrating the- this specific model or a variation of the model into other security lifecycle pipelines, but this is a decision that is more on CST customer Security Trust side and I have, um, only followed it, but I don't have specific details for that right now. But, I have seen a lot of good interesting results coming out of that model, good insights and security engineers using the results of the model to identify potential problems, and fix those problems much faster.Natalia Godyla:So, taking a step back and just thinking about the journey that your team has gone on to get the model to the state that it's in today. Uh, in the blog you listed a number of questions to figure out what would be the right data to train the model. So the questions were, is there enough data? How good is the data? Are there data usage restrictions? And, can data be generated in a lab? Natalia Godyla:So can you talk us through how you answered these questions like, as a- as a data scientist you were thrilled that there was a ton of data out there, but what was enough data? How did you define how good the data was? Or, whether it was good enough.Mayana Pereira:Great. So, those were questions that I asked myself before even knowing what the project was about, and the answer to is there enough data? It seemed very clear from the beginning that, yes, we had enough data, but those were questions that I brought up on the blog, not only for myself but for anyone else that was interested in replicating those experiments in their company or maybe university or s- anywhere any- any data scientist that is interested to train your own model for classification, which questions should be asked? Once you start a project like this. So the, is there enough data for me? Was clear from the beginning, we had several products so we had a variety of data sources. I think that when you reach, the number of millions of samples of data. I think that speaks for itself. It is a high volume. So I felt, we did have enough data.Mayana Pereira:And, when it came to data quality. That was a more complex question. We had data in our hands, bugs. We wanted to be able to train a model that could different- differentiate from security bugs and non security bugs, you know. And, for that, Usually what we do with machine learning, is we have data, that data has labels, so you have data that represents security bugs, data that represents non security bugs. And then we use that to train the model. And those labels were not so great. So we needed to understand how the not so great labels was going to impact our model, you know, we're going to train a model with labels that were not so great. So Mayana Pereira:That was gonna happen. So that was one of the questions that we asked ourselves. And I did a study on that, on understanding what is the impact of these noisy labels and the training data set. And how is it gonna impact the classification results that we get once using this, this training data? So this was one of the questions that I asked and we, I did several experiments, adding noise. I did that myself, I, I added noise on purpose to the data set to see what was the limits of this noise resilience. You know, when you have noisy labels in training, we published it in a, in an academic conference in 2019, and we understood that it was okay to have noisy labels. So security bugs that were actually labeled as not security and not security bugs labeled as security. There was a limit to that.Mayana Pereira:We kinda understood the limitations of the model. And then we started investigating our own data to see, is our own data within those limits. If yes, then we can use this data confidentially to train our models. If no, then we'll have to have some processes for correcting labels and understanding these data set a little bit better. What can we use and what can we not use to train the models. So what we found out is that, we didn't have noisy labels in the data set. And we had to make a few corrections in our labels, but it was much less work because we understood exactly what needed to be done, and not correct every single data sample or every single label in a, an enormous data set of millions of entries. So that was something that really helped. Mayana Pereira:And then the other question, um, that we asked is, can we generate data in the lab? So we could sometimes force a specific security issue and generate some, some box that had that security description into titles. And why did we include that in the list of questions? Because a lot of bugs that we have in our database are generated by automated tools. So when you have a new tool being included in your ecosystem, how is your model going to recognize the bugs that are coming from this new tool? So does our, ma- automatically generated box. And we could wait for the tool to be used, and then after a while we gathered the data that the tool provided us and including a retraining set. But we can also do that in the lab ecosystem, generate data and then incorporate in a training set. So this is where this comes from.Nic Fillingham:I wanted to ask possibly a, a very rudimentary question, uh, especially to those that are, you know, very familiar with machine learning. When you have a data set, there's words, there is text in which you're trying to generate labels for that text. Does the text itself help the process of creating labels? So for example, if I've got a bug and the name of that bug is the word security is in the, the actual bug name. Am I jump-starting, am I, am I skipping some steps to be able to generate good labels for that data? Because I already have the word I'm looking for. Like I, I think my question here is, was it helpful to generate your labels because you were looking at text in the actual title of the bug and trying to ascertain whether something was security or not?Mayana Pereira:So the labels were never generated by us or by me, the data scientists. The labels were coming from the engineering systems where we collected the data from. So we were relying on what- whatever happened in the, in the engineering team, engineering group and relying that they did, uh, a good job of manually labeling the bugs as security or not security. But that's not always the case, and that doesn't mean that the, the engineers are not good or are bad, but sometimes they have their own ways of identifying it in their systems. And not necessarily, it is the same database that we had access to. So sometimes the data is completely unlabeled, the data that comes to us, and sometimes there are mistakes. Sometimes you have, um, specific engineer that doesn't have a lot of security background. The person sees a, a problem, describes the problem, but doesn't necessarily attribute the problem as a security problem. Well, that can happen as well.Mayana Pereira:So that is where the labels came from. The interesting thing about the terminology is that, out of the millions and millions of security bugs that I did review, like manually reviewed, because I kinda wanted to understand what was going on in the data. I would say that for sure, less than 1%, even less than that, had the word security in it. So it is a very specific terminology when you see that. So people tend to be very literal in what the problem is, but not what the problem will generate. In a sense of they will, they will use things like Cross-site Scripting or passwords in clear, but not necessarily, there's a security pr- there's a security problem. But just what the issue is, so it is more of getting them all to understand that security lingual and what is that vocabulary that constitutes security problems. So that's wh- that's why it is a little bit hard to generate a list of words and see if it matches. If a specific title matches to this list of words, then it's security.Mayana Pereira:It was a little bit hard to do that way. And sometimes you have in the title, a few different words that in a specific order, it is a security problem. In another order, it is not. And then, I don't have that example here with me, but I, I could see some of those examples in the data. For example, I think the Cross-site Scripting is a good example. Sometimes you have site and cross in another place in the title. It has nothing to do with Cross-site Scripting. Both those two words are there. The model can actually understand the order and how close they are in the bug title, and make the decision if it is security or not security. So that's why the model is quite easier to distinguish than if we had to use rules to do that.Natalia Godyla:I have literally so many questions. Nic Fillingham:[laughs].Natalia Godyla:I'm gonna start with, uh, how did you teach at the lingo? So what did you feed the model so that it started to pick up on different types of attacks like Cross-site Scripting?Mayana Pereira:Perfect. The training algorithm will do that for me. So basically what I need to guarantee is that we're using the correct technique to do that. So the technique will, the machine learning technique will basically identify from this data set. So I have a big data set of titles. And each title will have a label which is security or non-security related to it. Once we feed the training algorithm with all this text and their associated labels, the training algorithm will, will start understanding that, some words are associated with security, some words are associated with non-security. And then the algorithm will, itself will learn those patterns. And then we're gonna train this algorithm. So in the future, we'll just give the algorithm a new title and say, "Hey, you've learned all these different words, because I gave you this data set from the past. Now tell me if this new ti- if this new title that someone just came up with is a security problem or a, a non-security problem." And the algorithm will, based on all of these examples that it has seen before, will make a decision if it is security or non-security.Natalia Godyla:Awesome. That makes sense. So nothing was provided beforehand, it was all a process of leveraging the labels. Mayana Pereira:Yes.Natalia Godyla:Also then thinking about just the dataset that you received, you were working with how many different business groups to get this data? I mean, it, it must've been from several different product teams, right?Mayana Pereira:Right. So I had the huge advantage of having an amazing team that is a data center team that is just focused on doing that. So their business is go around the company, gather data and have everything harmonized in a database. So basically, what I had to do is work with this specific team that had already done this amazing job, going across the company, collecting data and doing this hard work of harvesting data and harmonizing data. And they had it with them. So it is a team that does that inside Microsoft. Collects the data, gets everything together. They have their databases updated several times a day, um, collecting Mayana Pereira:... Data from across the company, so it is a lot of work, yeah.Natalia Godyla:So do different teams treat bug reports differently, meaning is there any standardization that you had to do or anything that you wanted to implement within the bug reports in order to get better data?Mayana Pereira:Yes. Teams across the company will report bugs differently using different systems. Sometimes it's Azure DevOps, sometimes it can be GitHub. And as I mentioned, there is a, there was a lot of work done in the data harmonization side before I touched the data. So there was a lot of things done to get the data in, in shape. This was something that, fortunately, several amazing engineers did before I touched the data. Basically, what I had to do once I touched it, was I just applied the data as is to the model and the data was very well treated before I touched it. Nic Fillingham:Wow. So many questions. I did wanna ask about measuring the success of this technique. Were you able to apply a metric, a score to the ... And I'm, I, I don't even know what it would be. Perhaps it would be the time to address a security bug pre and post this work. So, did this measurably decrease the amount of time for prioritized security bugs to be, to be addressed?Mayana Pereira:Oh, definitely. Yes, it did. So not only it helped in that sense, but it helped in understanding how some teams were not identifying specific classes of bugs as security. Because we would see this inconsistency with the labels that they were including in their own databases. These labels would come to this big database that is harmonized and then we would apply the model on top of these data and see that specific teams were treating their, some data points as non-security and should have been security. Or sometimes they were treating as security, but not with the correct severity. So it would, should have been a critical bug and they were actually treating it as a moderate bug. So, that, I think, not only the, the timing issue was really important, but now you have a visibility of behavior and patterns across the company that the model gives us.Nic Fillingham:That's amazing. And so, so if I'm an engineer at Microsoft right now and I'm in my, my DevOps environment and I'm logging a bug and I use the words cross- cross scripting somewhere in the bug, what's the timing with which I get the feedback from your model that says, "Hey, your prioritization's wrong," or, "Hey, this has been classified incorrectly"? Are we at the point now where this model is actually sort of integrated into the DevOps cycle or is that still coming further down the, the, the path?Mayana Pereira:So you have, the main customer is Customer Security and Trust team inside Microsoft. They are the ones using it. But as soon as they start seeing problems in the data or specific patterns and problems in specific teams' datasets, they will go to that team and then have this, they have a campaign where they go to different teams and, and talk to them. And some teams, they do have access to the datasets after they are classified by our model. Right now, there's, they don't have the instant response, but that's, that's definitely coming.Nic Fillingham:So, Mayana, how is Customer Security and Trust, your organization, utilizing the outputs of this model when a, when a, when a bug gets flagged as being incorrectly classified, you know, is there a threshold, and then sort of what happens when you, when you get those flags?Mayana Pereira:So the engineering team, the security engineering team in Customer Security and Trust, they will use the model to understand the overall state of security of Microsoft products, you know, like the products across the company, our products, basically. And they will have an understanding of how fast those bugs are being mitigated. They'll have an understanding of the volume of bugs, and security bugs in this case, and they can follow this bugs in, in a, in a timely manner. You know, as soon as the bug comes to the CST system, they bug gets flagged as either security or not security. Once it's flagged as security, there, there is a second model that will classify the severity of the bug and the CST will track these bugs and understand how fast the teams are closing those bugs and how well they're dealing with the security bugs.Natalia Godyla:So as someone who works in the AI for Good group within Microsoft, what is your personal passion? What would you like to apply AI to if it, if it's not this project or, uh, maybe not a project within Microsoft, what is, what is something you want to tackle in your life?Mayana Pereira:Oh, love the question. I think my big passion right now is developing machine learning models for eradication of child sexual abuse medias in, across different platforms. So you can think about platform online from search engines to data sharing platforms, social media, anything that you can have the user uploading content. You can have problems in that area. And anything where you have using visualizing content. You want to protect that customer, that user, from that as well. But most importantly, protect the victims from those crimes and I think that has been, um, something that I have been dedicating s- some time now. I was fortunate to work with an NGO, um, recently in that se- in that area, in that specific area. Um, developed a few models for them. She would attacked those kind of medias. And these would be my AI for Good passion for now. The other thing that I am really passionate about is privacy, data privacy. I feel like we have so much data out there and there's so much of our information out there and I feel like the great things that we get from having data and having machine learning we should not, not have those great things because of privacy compromises. Mayana Pereira:So how can we guarantee that no one's gonna have their privacy compromised? And at the same time, we're gonna have all these amazing systems working. You know, how can we learn from data without learning from specific individuals or without learning anything private from a specific person, but still learn from a population, still learn from data. That is another big passion of mine that I have been fortunate enough to work in such kind of initiatives inside Microsoft. I absolutely love it. When, when I think about guaranteeing privacy of our customers or our partners or anyone, I think that is also a big thing for me. And that, that falls under the AI for Good umbrella as well since that there's so much, you know, personal information in some of these AI for Good projects. Natalia Godyla:Thank you, Mayana, for joining us on the show today.Nic Fillingham:We'd love to have you back especially, uh, folks, uh, on your team to talk more about some of those AI for Good projects. Just, finally, where can we go to follow your work? Do you have a blog, do you have Twitter, do you have LinkedIn, do you have GitHub? Where should, where should folks go to find you on the interwebs?Mayana Pereira:LinkedIn is where I usually post my latest works, and links, and interesting things that are happening in the security, safety, privacy world. I love to, you know, share on LinkedIn. So m- I'm Mayana Pereira on LinkedIn and if anyone finds me there, feel free to connect. I love to connect with people on LinkedIn and just chat and meet new people networking.Natalia Godyla:Awesome. Thank you. Mayana Pereira:Thank you. I had so much fun. It was such a huge pleasure to talk to you guys.Natalia Godyla:Well, we had a great time unlocking insights into security from research to artificial intelligence. Keep an eye out for our next episode. Nic Fillingham:And don't forget to Tweet us at MSFTSecurity or email us at securityunlocked@microsoft.com with topics you'd like to hear on a future episode. Until then, stay safe. Natalia Godyla:Stay secure.
2/17/2021

Enterprise Resiliency: Breakfast of Champions

Ep. 15
Prior to the pandemic,workdaysused to look a whole lot different.If you had a break,youcouldtake a walk to stretch your legs, shake the hands of your co-workers,orget some 1-on-1 face timewith the boss. Ahh... those were the days. Thatclose contact we once had is now somethingthat manyof usyearn for aswe’vehad to abruptlylift andshift fromliving in our office to working from our home.But communicating and socializing aren’t the only things that were easier back then. The walls of your office have expanded, and with them, the boundaries of your security protocols. Small in-office tasks like patching a server have now become multi-step processes that require remote management, remote updates, and remote administrative control. With that comes the prioritization of resilience and what it means for enterprises, customers, and security teams alike.That’swhere remote enterprise resiliency comes into play.Today on the pod,we explore the final chapter of the MDDR.Irfan Mirza,Director of Enterprise Continuity and Resilience atMicrosoft, wrapsupthe observationsfrom the report bygivinghostsNic FillinghamandNatalya Godylathe rundown on enterprise resiliencyand discusses how we canensure the highest levels of security while working from home.Irfan explains theZero trustmodel and how Microsoft is working to extend security benefits to your kitchen or home office, or...thatmake-shiftworkspacein your closet.In the second segment,Andrew Paverd,Senior Researcheron the Microsoft Security Response Center Teamandjackof all trades,stops by…andwe’renot convinced he’s fully human.He’shere to tell us about the many hats he wears,from safe systemsprogramming to leveraging AI to helpwith processes within the MSRC,andshares how he has to think like a hacker to prevent attacks. Spoiler alert:he’sa big follower of Murphy’s Law.In This Episode, You Will Learn:•How classical security models are being challenged•What the Zero Trust Model is and how it works•The three critical areas of resilience: extending the enterprise boundary, prioritizing resilient performance, and validating the resilience of our human infrastructure.•How hackers approach our systems and technologiesSome Questions We Ask:•How has security changed as a product of the pandemic?•Do we feel like we have secured the remote workforce?•What frameworks exist to put a metric around where an organization is in terms of its resiliency?•What is Control Flow Guard (CFG) and Control-Flow Integrity?•What’sthe next stage for the Rust programming language?Resources:Microsoft Digital Defense Report:https://www.microsoft.com/en-us/security/business/security-intelligence-reportIrfan’s LinkedInhttps://www.linkedin.com/in/irfanmirzausa/Andrew’s LinkedInhttps://www.linkedin.com/in/andrewpaverd/Nic’s LinkedInhttps://www.linkedin.com/in/nicfill/Natalia’s LinkedInhttps://www.linkedin.com/in/nataliagodyla/Microsoft Security Blog:https://www.microsoft.com/security/blog/Transcript(Full transcript can be found at https://aka.ms/SecurityUnlockedEp15)Nic Fillingham:Hello, and welcome to Security Unlocked, a new podcast from Microsoft, where we unlock insights from the latest in news and research from across Microsoft security, engineering and operations teams. I'm Nic Fillingham.Natalia Godyla:And I'm Natalia Godyla. In each episode, we'll discuss the latest stories from Microsoft Security, deep dive into the newest threat intel, research and data science. Nic Fillingham:And profile some of the fascinating people working on artificial intelligence in Microsoft Security. Natalia Godyla:And now let's unlock the pod. Hi Nic, I have big news.Nic Fillingham:Big news. Tell me a big news.Natalia Godyla:I got a cat. Last night at 8:00 PM, I got a cat. Nic Fillingham:Did it come via Amazon Prime drone? Natalia Godyla:No.Nic Fillingham:Just, that was a very specific time. Like 8:00 PM last night is not usually the time I would associate people getting cats. Tell me how you got your cat. Natalia Godyla:It was a lot more conventional. So I had an appointment at the shelter and found a picture of this cat with really nubby legs and immediately-Nic Fillingham:(laughs).Natalia Godyla:... fell in love obviously. And they actually responded to us and we went and saw the cat, got the cat. The cat is now ours. Nic Fillingham:That's awesome. Is the cat's name nubby. Natalia Godyla:It's not, but it is on the list of potential name changes. So right now the cat's name is tipper. We're definitely nervous about why the cat was named tipper. Nic Fillingham:(laughs).Natalia Godyla:We're hiding all of the glass things for right now. Nic Fillingham:How do we get to see the cat? Is there, will there be Instagram? Will there be Twitter photos? This is the most important question.Natalia Godyla:Wow. I haven't planned that yet.Nic Fillingham:You think about that and I'll, uh, I'll start announcing the first guest on this episode.Natalia Godyla:(laughs).Nic Fillingham:On today's episode, we speak with Irfan Mirza, who is wrapping up our coverage of the Microsoft Digital Defense Report with a conversation about enterprise resiliency. Now, this is really all of the chapters that are in the MDDR, the nation state actors, the increase in cyber crime sophistication, business email compromise that you've heard us talk about on the podcast, all gets sort of wrapped up in a nice little bow in this conversation where we talk about all right, what does it mean, what does it mean for customers? What does it mean for enterprises? What does it mean for security teams? And so we talk about enterprise resiliency. And we actually recorded this interview in late 2020, but here we are, you know, two months later and those findings are just as relevant, just as important. It's a great conversation. And after that, we speak with-Natalia Godyla:Andrew Paverd. So he is a senior researcher on the Microsoft Security Response Center team. And his work is well, well, he does a ton of things. I honestly don't know how he has time to pull all of this off. So he does everything from safe systems programming to leveraging AI, to help with processes within MSRC, the Microsoft Security Response Center. And I just recall one of the quotes that he said from our conversation was hackers don't respect your assumptions, or something to that effect, but it's such a succinct way of describing how hackers approach our systems and technology. So another really great conversation with a, a super intelligent researcher here at Microsoft.Nic Fillingham:On with the pod.Natalia Godyla:On with the pod. Today, we're joined by Irfan Mirza, Director of Enterprise Continuity and Resilience, and we'll be discussing the Microsoft Digital Defense Report and more specifically enterprise resilience. So thank you for being on the show today, Irfan.Irfan Mirza:Thanks so much glad to be here. And hope we have a, a great discussion about this. This is such an important topic now. Natalia Godyla:Yes, absolutely. And we have been incrementally working through the Microsoft Digital Defense Report, both Nic and I have read it and have had some fantastic conversations with experts. So really looking forward to hearing about the summation around resilience and how that theme is pulled together throughout the report. So let's start it off by just hearing a little bit more about yourself. So can you tell us about your day-to-day? What is your role at Microsoft? Irfan Mirza:Well, I lead the enterprise continuity and resilience team and we kind of provide governance overall at the enterprise. We orchestrate sort of all of the, the risk mitigations. We go and uncover what the gaps are, in our enterprise resilience story, we try to measure the effectiveness of what we're doing. We focus on preparedness, meaning that the company's ready and, you know, our critical processes and services are always on the ready. It's a broad space because it spans a very, very large global enterprise. And it's a very deep space because we have to be experts in so many areas. So it's a fun space by saying that.Natalia Godyla:Great. And it's really appropriate today then we're talking about the MDDR and enterprise resilience. So let's start at a high level. So can you talk a little bit about just how security has changed as a product of the pandemic? Why is resilience so important now? Irfan Mirza:Yeah, it's a great question. A lot of customers are asking that, our field is asking that question, people within the company are asking. Look, we've been 11 months under this pandemic. Maybe, you know, in some places like China, they've been going through it for a little bit longer than us, you know, a couple of months more. What we're finding after having sort of tried to stay resilient through this pandemic, uh, one obviously is on the human side, everyone's doing as much as we possibly can there. But the other part of it is on the enterprise side. What is it that we're having to think about as we think of security and as we think of enterprise resilience?Irfan Mirza:There are a couple of big things that I think I would note, one is that, look, when this pandemic hit us, our workforce lifted and shifted. I mean, by that, I mean that we, we, we got up out of our offices and we all left. I mean, we took our laptops and whatever we could home. And we started working remotely. It was a massive, massive lift and shift of personnel, right? We got dispersed. Everybody went to their own homes and most of us have not been back to the office. And it's not just at Microsoft, even, even a lot of our customers and our partners have not gone back to the office at all, right? So that, that's a prolong snow day, if you want to call it that.Irfan Mirza:The other thing that happened is our workload went with us. Wasn't just that, "Hey, you know, I'm taking a few days off, I'm going away or going on vacation and, and I'll be checking email periodically." No, I actually took our work with us and we started doing it remotely. So what that's done is it's created sort of a, a need to go back and look at what we thought was our corporate security boundary or perimeter.Irfan Mirza:You know, in the classical model, we used to think of the corporation and its facilities as the, the area that we had to go and secure. But now in this dispersed workforce model, we have to think about my kitchen as part of that corporate perimeter. And all of a sudden we have to ensure that, that my kitchen is as secure as the corporate network or as the facilities or the office that I was working from. That paradigm is completely different than anything we'd thought about before. Nic Fillingham:And so Irfan, in the MDDR, uh, this section, um, and if you've got the report open, you're playing along at home, I believe it's page 71. This enterprise resiliency is sort of a wrap-up of, of a lot of the observations that are in the MDDR report. It's not a new section. It's as you're getting towards the end of the report, you're looking for, okay, now what does this mean to me? I'm a CSO. I need to make new security policies, security decisions for my organization. This concept of enterprise resiliency is sort of a wrap up of everything that we've seen across cyber crime, across the nation state, et cetera, et cetera. Is that, is that accurate? Is that a good way to sort of read that section in the report? Irfan Mirza:Yeah. It is really the, the way to think of it, right.? It's sort of like a, the conclusion, so what, or why is this relevant to me and what can I do about it? When you think about the report and the way that it's structured, look, we, you know, the report goes into great detail about cyber crime as you called out Nic. And then it talks about nation state threats.Irfan Mirza:These are newer things to us. We've certainly seen them on the rise, actors that are well-trained, they're well-funded they play a long game, not necessarily a short game, they're looking, they're watching and they're waiting, they're waiting for us to make mistakes or to have gaps, they look for changes in tactics, either ours, uh, they themselves are quite agile, right? Irfan Mirza:So when you think about the environment in which we have to think about resilience, and we have to think about security, that environment itself has got new vectors or new threats that are, that are impacting it, right? In addition to that, our workforce has now dispersed, right? We're all over the, all over the globe. We see emerging threats that are, that are, non-classical like ransomware. We see attacks on supply chain. We continue to see malware and malware growing, right? Irfan Mirza:And, and so when you think about that, you have to think if I need to secure now my, my dispersed corporate assets and resources, my people, the workload, the data, the services and the processes that are all there, what are the, the sort of three big things I would need to think about? And so this report sort of encapsulates all, all of that. It gives the details of what, what's happening. And, and then page 71 is you say that resilience piece sort of comes back and says, "Look, your security boundaries extended. Like it or not, it is extended at this point. You've got to think beyond that on-site perimeter that we were thinking about before."Irfan Mirza:So we have to start thinking differently. And th- there's three critical areas that are sort of called out, acknowledging the security boundary has increased, thinking about resilience and performance, and then validating the resilience of our human infrastructure. This is like new ideas, but these are all becoming imperatives for us. We're having to do this now, whether we like it or not. Irfan Mirza:And so this report sort of gives our customers, and, and it's a reflection of what we're doing in the company. It's an open and honest conversation about how we propose to tackle these challenges that we're facing.Nic Fillingham:And so Irfan if we can move on to that critical area, number two, that prioritizing resilient performance. When I say the word performance and resilient performance, is that scoped down just to sort of IT infrastructure, or does that go all the way through to the humans, the actual people in the organization and, um, how they are performing their own tasks, their own jobs and the tasks that are part of their, their job and et cetera, et cetera? What's the, I guess what's the scope of that area too?Irfan Mirza:As we were thinking about resilience, as you know, shortly after we dispersed the workforce, we started thinking about, about what should be included in our classical understanding of resilience. But when you think about, about typical IT services and online services, and so on, a lot of that work is already being done with the life site reviews that we do and people are paying very close attention to service performance. We have SLAs, we have obligations, we have commitments that we've made that our services will be performing to a certain degree, but there are also business processes that are associated with these services very closely. Irfan Mirza:When you think about all of the processes that are involved and services that are involved from the time a customer thinks of buying Office, uh, 365, as an example, to the time that they provision their first mailbox, or they receive their first email, there are dozens of process, business processes. Irfan Mirza:Every single service in that chain could be working to 100% efficiency. And yet if the business processes, aren't there, for instance, to process the deal, to process the contract, to process, uh, the customer's payment or, uh, acknowledge receipt of the payment in order to be able to provision the service, all of these processes, all of a sudden have to, we have to make sure that they're also performing.Irfan Mirza:So when we start thinking about resilience, up to now, business continuity has focused on, are you ready? Are you prepared? Are your dependencies mapped? Have you, have you done a business impact analysis? Are you validating and testing your preparedness? You know, are you calling down your call tree for instance? But I think where we're going now with true enterprise resilience, especially in this sort of modern Irfan Mirza:... day, we're, we're looking at performance, right? What, what is your preparedness resulting in? So if you stop and you think about a child at school, they get homework. Well, the homework really, they bring it home. They do it. They take it back to the teacher. They get graded on it. That's wonderful. This means that the child is ready. But at some point in time, the class or the teacher is going to give them a test, and that test is going to be the measure of performance, right? Irfan Mirza:So we need to start thinking of resilience and continuity in the same way. We're prepared. We've done all our homework. Now let's go and see how many outages did you have? How critical were the outages? How long did they last? How many of them were repeat outages? How many of the repeat outages were for services that are supposed to have zero downtown, like services that are always supposed to on like your DNS service or your identity auth- authentication service, right? So, when you start thinking about, uh, resilience from that perspective, now you've got a new set of data that you have to go and capture, or data that you're capturing, you have to now have to have insights from it. You've got to be able to correlate your preparedness, meaning the homework that you've done with your actual performance, your outage and your, and your gap information. All right?Irfan Mirza:So that, that's what prioritizing resilient performance is all about. It's about taking realtime enterprise preparedness and mapping it to real time enterprise performance. That tells you if your preparedness is good enough or not, or what it is that you need to do. There's a loop here, a feedback loop that has to be closed. You can't just say that, well, you know, we've done all the exercises theoretically. We're good and we're ready to take on any sort of a crisis or, or, or disaster. Yeah, that's fine. Can we compare it to realtime what you're doing? Can we break glass and see what that looks like? Can we shut you down and or shut down parts of your operation as in the event of an earthquake for instance, or a hurricane wiping out, uh, access to a data center, right? Can we do those things and still be resilient when that happens? So this is what performance and resilience come together in that space.Natalia Godyla:So am I right in understanding that beyond, like you said, the theoretical where you think about the policies that you should have in place, and the frameworks that you should have in place, you have the analytics on, you know, the state of, the state of how performant your systems are to date. And then in addition, is there now the need for some sort of stress testing? Like actually figuring out whether an additional load on a system would cause it to break, to not be resilient? Is that now part of the new approach to resilience?Irfan Mirza:Yeah. There are, there are several, several things to do here, right? You absolutely said it. There's a stress test. Actually, this pandemic has, is already a stress test in and of itself, right? It's stressing us in a many ways. It's stressing, obviously the psyche and, and, you know, our whole psychology, and our ability to sustain in quarantine, in isolated, in insulated environments and so on. But it's also testing our ability to do the things that we just so, uh, so much took for granted, like the ability to patch a server that's sitting under my desk in the office whenever I needed to, right? That server now has to become a managed item that somebody can manage remotely, patch remotely, update remotely when needed, control administrative access and privileges remotely. But yes, for resilience, I think we need to now collect all of the data that we have been collecting or looking at and saying, can we start to create those correlations between our preparedness and between our real performance? Irfan Mirza:But there's another area that this dovetails into which is that of human resilience, right? We talked a little bit earlier about, you know, sort of the whole world enduring this hardship. We need to first and foremost look at our suppliers, subcontractors, people that we're critically dependent on. What is their resilience look like? That's another aspect that we have to go back. In the areas where we have large human resources or, or workforces that are working on our behalf, we need to make sure that they're staying resilient, right? Irfan Mirza:We talked on a lot about work/life balance before. Now I think the new buzzword in HR conference rooms is going to be work/life integration. It's completely integrated, and so we need to start thinking about the impact that would have. Are we tracking attrition of our employees, of certain demographics within the employees? Are we looking at disengagement? People just sort of, "Yeah, I'm working from home, but I'm not really being fully engaged." Right? The hallway conversations we used to have are no longer there. And we need to start thinking, are people divesting? Our resources, are they divesting in the workplace? Are they divesting in their, in their work or work/life commitment? These measures are all now having to be sort of like... Irfan Mirza:We used to rely on intuition, a look, a hallway gaze, look at the, the snap in somebody's walk as they walked away from you or out of your office. We don't have that anymore. Everybody's relatively stagnant. We're, we're, we're seated. We don't get to see body language that much. We don't get to read that. There's a whole new set of dynamics that are coming into play, and I think smart corporations and smart companies will start looking at this as a very important area to pay attention to.Nic Fillingham:How are we measuring that? What tools or sort of techniques, or, or sort of frameworks exist to actually put a metric around this stuff, and determine sort of where, where an organization is in terms of their level of resiliency?Irfan Mirza:This question is actually the whole reason why we brought this enterprise resilience sort of a conclusion to this fourth chapter, and, and, you know, the summation of this, of this report. Irfan Mirza:What we're doing now is we're saying, look. Things that used to be fundamentally within the domain of IT departments, or used to be fundamentally with, within the domain of live site, or used to be fundamentally in the domain of human resource departments are now all floating up to be corporate imperatives, to be enterprise imperatives. I think the thinking here is that we need to make sure that the data that we've been collecting about, as an example to answer your question, attrition, right? A certain demographic. Millennials, uh, changing jobs, leaving the company, just to pick an example more than anything else. This is no longer just data that the HR Department is interested in, or that recruiting would be interested in, or, or retention would be interested. This is data that's about to significantly impact the enterprise, and it needs to be brought into the enterprise purview.Irfan Mirza:Our classical and traditional models of looking at things in silos don't allow us to do that. What we're recommending is that we need to have a broader perspective and try to drive insights from this that do tell a more comprehensive story about our ent- enterprise resilience. That story needs to include the resilience of our services, our business processes, our suppliers, our human capital, our infrastructure, our extended security boundary, our data protection, uh, prevention of data loss, our intrusion detection. I mean, there's such a broad area that we have to cover. That's we're saying. And, and as we implement this new sort of zero trust model, I think the, the effectiveness of that model, how much progress we're making is becoming an enterprise priority, not just something that the IT department is going to go around on it's own.Nic Fillingham:Irfan, I wonder if I could put you on the spot, and were there any interesting bits of data that you saw in those first couple months of the shift to remote work where like, yeah, the number of unique devices on the Microsoft corporate network quadrupled in 48 hours. Like any, anything like that? I'd just wondering what, what little stats you may have in hand.Irfan Mirza:Yeah. The number of devices and sort of the flavors of devices, we've always anticipated that that's going to be varied. We're cognizant of that. Look, we have, you know, people have PCs. They have MACs. They have Linux machines, and, and they have service o- operating software. There's a lot of different flavors. And, and it's not just the device and the OS that matters, it's also what applications you're running. Some applications we can certify or trust, and others perhaps we can't, or that we still haven't gotten around to, to verifying, right? And all of these sit, and they all perform various functions including intruding and potentially exfiltrating data and Spyware and Malware and all of that. So when you think about that, we've always anticipated it. Irfan Mirza:But the one thing that, that we were extremely worried about, and I think a lot of our Enterprise customers were worried about, is the performance of the workforce. What we found very early on in, in the, in the lift and shift phase was that we needed to have a way of measuring is our, our built processes working? Are we checking in the same amount of code as we were before? And we noted a couple of interesting things. We looked at our, our VPN usage and said, what are those numbers look like? Are they going up and down?Irfan Mirza:And I think what we found is that initially, the effect was quite comparable to what we had, uh, when we experienced snow days. Schools are shut down. People don't go to work. They're slipping and sliding over here. We're just not prepared for snow weather in, in this state like some of the others. So what happened is, we saw that we were, we were sort of seeing the same level of productivity as snow days. We say that we had the same level of VPN usage as snow days, and we were worried because that, you know, when, when it snows, people usually take the day off, and then they go skiing. Irfan Mirza:So what happened? Well, after about a week things started picking back up. People got tired of sort of playing snow day and decided that, you know what? It's time to, to dig in, and human nature, I think, kicked in, the integrity of the workforce kicked in. And sure enough, productivity went up, VPN usage went up, our number of sessions, the duration of sessions. Meetings became shorter.Nic Fillingham:Can I tell you hallelujah? (laughs) Irfan Mirza:(laughs) Nic Fillingham:That's one of the, that's one of the great-Irfan Mirza:Absolutely.Nic Fillingham:... upsides, isn't it? To this, this new culture of remote work is that we're all meeting for, for less amount of time, which I think, I think is fantastic.Irfan Mirza:Look, you know, in times of crisis, whether it's a natural disaster, or a pandemic, or, or a manmade situation such as a war or a civil war, or whatever, I, I think what happens is the amount of resources that you are customarily used to having access to gets limited. The way in which you work shifts. It changes. And so the, the true test of resilience, I think, is when you are able to adapt to those changes gracefully without requiring significant new investment and you're able to still meet and fulfill your customer obligations, your operational expectations. That really is.Irfan Mirza:So what you learn in times of hardship are to sort of live, you know, more spartan-like. And that spartan-ism, if there's such a word as that, that's what allows you to stay resilient, to say what are the core things that I need in order to stay up and running? And those fundamental areas become the areas of great investment, the areas that you watch over more carefully, the areas that you measure the performance of, the areas that you look for patterns and, and trends in to try to predict what's happening, right?Irfan Mirza:So that is something that carries over from experiences of being in the front lines of a, uh, a war or, or from being, uh, you know, in the midst of a hurricane trying to recover a data center, or an earthquake, or any other, uh, type of power outage, right? These are all the sort of key scenarios that we would be going to look at. And that's one of the things they all have in common. It's really that you don't have the resources or access to the resources that you thought you did, and now you've got to be able to do some things slightly differently.Natalia Godyla:Thank you for joining us on the podcast today. It's been great to get your perspective on enterprise resilience. Really fascinating stuff. So, thank you.Irfan Mirza:Thank you, Natalia. And, and thank you, Nick. It's been a great conversation. As I look back at this discussion that we had, I feel even, even stronger now that the recommendations that we're making, and the guidance that we're giving our customers and sharing our experiences, becomes really, really important. I think this is something that we're learning as we're going along. We're learning on the journey. We're uncovering things that we didn't know. We're looking at data in a different way. We're, we're trying to figure out how do we sustain ourselves, Nic Fillingham:... not just through this pandemic, but also beyond that. And I think the, whatever it is that we're learning, it becomes really important to share. And for our customers and people who are listening to this podcast to share back with us what they've learned, I think that becomes incredibly important because as much as we like to tell people what we're doing, we also want to know what, what people are doing. And so learning that I think will be a great, great experience for us to have as well. So thank you so much for enabling this conversation. Natalia Godyla:And now let's meet an expert from the Microsoft security team to learn more about the diverse backgrounds and experiences of the humans creating AI and tech at Microsoft. Welcome back to another episode of Security Unlocked. We are sitting with Andrew Paverd today, senior researcher at Microsoft. Welcome to the show, Andrew. Andrew Paverd:Thanks very much. And thanks for having me. Natalia Godyla:Oh, we're really excited to chat with you today. So I'm just doing a little research on your background and looks like you've had a really varied experience in terms of security domains consulting for mobile device security. I saw some research on system security. And it looks like now you're focused on confidential computing at Microsoft. So let's start there. Can you talk a little bit about what a day in the life of Andrew looks like at Microsoft? Andrew Paverd:Absolutely. I think I have one of the most fascinating roles at Microsoft. On a day-to-day basis, I'm a researcher in the confidential computing group at the Microsoft Research Lab in Cambridge, but I also work very closely with the Microsoft Security Response Center, the MSRC. And so these are the folks who, who are dealing with the frontline incidents and responding to reported vulnerabilities at Microsoft. But I work more on the research side of things. So how do we bridge the gap between research and what's really happening on the, on the front lines? And so I, I think my position is quite unique. It's, it's hard to describe in any other way than that, other than to say, I work on research problems that are relevant to Microsoft security. Natalia Godyla:And what are some of those research problems that you're focused on? Andrew Paverd:Oh, so it's actually been a really interesting journey since I joined Microsoft two years ago now. My background, as you mentioned, was actually more in systems security. So I had, I previously worked with technologies like trusted execution environments, but since joining Microsoft, I've worked on two really, really interesting projects. The, the first has been around what we call safe systems programming languages. Andrew Paverd:So to give a bit more detail about it in the security response center, we've looked at the different vulnerabilities that Microsoft has, has patched and addressed over the years and seen some really interesting statistics that something like 70% of those vulnerabilities for the pa- past decade have been caused by a class of vulnerability called memory corruption. And so the, the question around this is how do we try and solve the root cause of problem? How do we address, uh, memory corruption bugs in a durable way? Andrew Paverd:And so people have been looking at both within Microsoft and more broadly at how we could do this by transitioning to a, a different programming paradigm, a more secure programming language, perhaps. So if you think of a lot of software being written in C and C++ this is potentially a, a cause of, of memory corruption bugs. So we were looking at what can we do about changing to safer programming languages for, for systems software. So you might've heard about new languages that have emerged like the Rust programming language. Part of this project was investigating how far we can go with languages like Rust and, and what do we need to do to enable the use of Rust at Microsoft.Natalia Godyla:And what was your role with Rust? Is this just the language that you had determined was a safe buyable option, or were you part of potentially producing that language or evolving it to a place that could be safer? Andrew Paverd:That's an excellent question. So in, in fact it, it was a bit of both first determining is this a suitable language? Trying to define the evaluation criteria of how we would determine that. But then also once we'd found Rust to be a language that we decided we could potentially run with, there was an element of what do we need to do to bring this up to, let's say to be usable within Microsoft. And actually I, I did quite a bit of work on, on this. We realized that, uh, some Microsoft security technologies that are available in our Microsoft compilers weren't yet available in the Rust compiler. One in particular is, is called control flow guard. It's a Windows security technology and this wasn't available in Rust. Andrew Paverd:And so the team I, I work with looked at this and said, okay, we'd like to have this implemented, but nobody was available to implement it at the time. So I said, all right, let me do a prototype implementation and, uh, contributed this to the open source project. And in the end, I ended up following through with that. And so I've, I've been essentially maintaining the, the Microsoft control flow guide implementation for the, the Rust compiler. So really an example of Microsoft contributing to this open source language that, that we hope to be using further.Nic Fillingham:Andrew, could you speak a little bit more to control flow guard and control flow integrity? What is that? I know a little bit about it, but I'd love to, for our audience to sort of like expand upon that idea. Andrew Paverd:Absolutely. So this is actually an, an example of a technology that goes back to a collaboration between the MSRC, the, the security response center and, and Microsoft Research. This technology control flow guard is really intended to enforce a property that we call control flow integrity. And that simply means that if you think of a program, the control flow of a program jumps through two different functions. And ideally what you want in a well-behaved program is that the control always follows a well-defined paths. Andrew Paverd:So for example, you start executing a function at the beginning of the function, rather than halfway through. If for example, you could start executing a function halfway through this leads to all kinds of possible attacks. And so what control flow guard does is it checks whenever your, your program's going to do a bronch, whenever it's going to jump to a different place in the code, it checks that that jump is a valid call target, that you're actually jumping to the correct place. And this is not the attacker trying to compromise your program and launch one of many different types of attacks.Nic Fillingham:And so how do you do that? What's the process by which you do en- ensure that control flow?Andrew Paverd:Oh, this is really interesting. So this is a technology that's supported by Windows, at the moment it's only available on, on Microsoft Windows. And it works in conjunction between both the compiler and the operating system. So the compiler, when you compile your program gives you a list of the valid code targets. It says, "All right, here are the places in the program where you should be allowed to jump to." And then as the program gets loaded, the, the operating system loads, this list into a highly optimized form so that when the program is running it can do this check really, really quickly to say, is this jump that I'm about to do actually allowed? And so it's this combination of the Windows operating system, plus the compiler instrumentation that, that really make this possible. Andrew Paverd:Now this is quite widely used in Windows. Um, we want in fact as much Microsoft software as possible to use this. And so it's really critical that we enable it in any sort of programming language that we want to use. Nic Fillingham:How do you protect that list though? So now you, isn't that now a target for potential attackers?Andrew Paverd:Absolutely. Yeah. And, and it becomes a bit of a race to, to-Nic Fillingham:Cat and mouse.Andrew Paverd:... protect different-Natalia Godyla:(laughs).Andrew Paverd:A bit of, a bit of a cat, cat and mouse game. But at least the nice thing is because list is in one place, we can protect that area of memory to a much greater degree than, than the rest of the program. Natalia Godyla:So just taking a step back, can you talk a little bit about your path to security? What roles have you had? What brought you to security? What's informing your role today? Andrew Paverd:It's an interesting story of how I ended up working in security. It was when I was applying for PhD programs, I had written a PhD research proposal about a topic I thought was very interesting at the time on mobile cloud computing. And I still think that's a hugely interesting topic. And what happened was I sent this research proposal to an academic at the University of Oxford, where I, I was looking to study, and I didn't hear anything for, for a while. Andrew Paverd:And then, a fe- a few days later I got an email back from a completely different academic saying, "This is a very interesting topic. I have a project that's quite similar, but looking at this from a security perspective, would you be interested in doing a PhD in security on, on this topic?" And, so this was my very mind-blowing experience for me. I hadn't considered security in that way before, but I, I took a course on security and found that this was something I was, I was really interested in and ended up accepting the, the PhD offer and did a PhD in system security. And that's really how I got into security. And as they say, the rest is history.Natalia Godyla:Is there particular part of security, particular domain within security that is most near and dear to your heart?Andrew Paverd:Oh, that's a good question.Natalia Godyla:(laughs).Andrew Paverd:I think, I, I think for me, security it- itself is such a broad field that we need to ensure that we have security at, at all levels of the stack, at all, places within the chain, in that it's really going to be the weakest link that an attacker will, will go for. And so I've actually changed field perhaps three times so far. This is what keeps it interesting. My PhD work was around trusted computing. And then as I said, I, since joining Microsoft, I've been largely working in both safe systems programming languages and more recently AI and security. And so I think that's what makes security interesting. The, the fact that it's never the same thing two days in a row.Natalia Godyla:I think you hit on the secret phrase for this show. So AI and security. Can you talk a little bit about what you've been doing in AI and security within Microsoft? Andrew Paverd:Certainly. So about a year ago, as many people in the industry realized that AI is being very widely used and is having great results in so many different products and services, but that there is a risk that AI algorithms and systems themselves may be attacked. For example, I, I know you had some, some guests on your podcast previously, including Ram Shankar Siva Kumar who discussed the Adversarial ML Threat Matrix. And this is primarily the area that I've been working in for the past year. Looking at how AI systems can be, can be attacked from a security or a privacy perspective in collaboration with researchers, from MSR, Cambridge. Natalia Godyla:What are you most passionate about? What's next for a couple of these projects? Like with Rust, is there a desire to make that ubiquitously beyond Microsoft? What's the next stage? Andrew Paverd:Ab- absolutely. Natalia Godyla:Lots of questions. (laughs).Andrew Paverd:Yeah. There's a lot of interest in this. So, um, personally, I'm, I'm not working on the SSPL project myself, or I'm, I'm not working on the safe systems programming languages project myself any further, but I know that there's a lot of interest within Microsoft. And so hopefully we'll see some exciting things e- emerging in that space. But I think my focus is really going to be more on the, both the security of AI, and now we're also exploring different areas where we can use AI for security. This is in collaboration, more with the security response center. So looking into different ways that we can automate different processes and use AI for different types of, of analysis. So certainly a lot more to, to come in that space.Nic Fillingham:I wanted to come back to Rust for, for a second there, Andrew. So you talked about how the Rust programming language was specifically designed for, correct me on taxonomy, memory integrity. Is that correct?Andrew Paverd:For, for memory safety, yeah. Nic Fillingham:Memory safety. Got it. What's happening on sort of Nic Fillingham:... and sort of the, the flip side of that coin in terms of instead of having to choose a programming language that has memory safety as sort of a core tenet. What's happening with the operating system to ensure that languages that maybe don't have memory safety sort of front and center can be safer to use, and aren't threats or risks to memory integrity are, are sort of mitigated. So what's happening on the operating system side, is that what Control Flow Guard is designed to do? Or are there other things happening to ensure that memory safety is, is not just the responsibility of the programming language?Andrew Paverd:Oh, it's, that's an excellent question. So Control Flow Guard certainly helps. It helps to mitigate exploits once there's been an, an initial memory safety violation. But I think that there's a lot of interesting work going on both in the product space, and also in the research space about how do we minimize the amount of software that, that we have to trust. If you accept that software is going to have to bugs, it's going to have vulnerabilities. What we'd like to do, is we'd like to trust as little software as possible.Andrew Paverd:And so there's a really interesting effort which is now available in, in Azure under the, the heading of Confidential Computing. Which is this idea that you want to run your security sensitive workloads in a hardware enforced trusted execution environment. So you actually want to take the operating system completely out of what we call the trusted computing base. So that even if there are vulnerabilities in, in the OS, they don't affect your security sensitive workloads. So I think that there's this, this great trend towards confidential computing around compartmentalizing and segmenting the software systems that we're going to be running.Andrew Paverd:So removing the operating system from the trusted computing. And, and indeed taking this further, there's already something available in Azure, you can look up Azure Confidential Computing. But there's a lot of research coming in from the, the academic side of things about new technologies and new ways of, of enforcing separation and compartmentalization. And so I think it's part of this full story of, of security that we'll need memory safe programming languages. We'll need compartmentalization techniques, some of which, uh, rely on new hardware features. And we need to put all of this together to really build a, a secure ecosystem.Nic Fillingham:I only heard of Confidential Computing recently. I'm sure it's not a new concept. But for me as a sort of a productized thing, I only sort of recently stumbled upon it. I did not realize that there was this gap, there was this delta in terms of data being encrypted at rest, data being encrypted in transit. But then while the data itself was being processed or transformed, that that was a, was a gap. Is that the core idea around Confidential Computing to ensure that at no stage the data is not encrypted? Is, is that sort of what it is?Andrew Paverd:Absolutely. And it's one of the key pieces. So we call that isolated execution in the sense that the data is running in a, a trusted environment where only the code within that environment can access that data. So if you think about the hypervisor and the operation system, all of those can be outside of the trusted environment. We don't need to trust those for the correct computation of, of that data. And as soon as that data leaves this trusted environment, for example if it's written out of the CPU into the DRAM, then it gets automatically encrypted.Andrew Paverd:And so we have that really, really strong guarantee that only our code is gonna be touching our data. And the second part of this, and this is the really important part, is a, a protocol called remote attestation where this trusted environment can prove to a remote party, for example the, the customer, exactly what code is going to be running over that data. So you have a, a very high degree of assurance of, "This is exactly the code that's gonna be running over my data. And no other code will, will have access to it."Andrew Paverd:And the incredibly interesting thing is then, what can we build with these trusted execution environment? What can we build with Confidential Computing? And to bring this back to the, the keyword of your podcast, we're very much looking at confidential machine learning. How do we run machine learning and AI workloads within these trusted execution environments? And, and that unlocks a whole lot of new potential.Nic Fillingham:Andrew, do you have any advice for people that are m- maybe still studying or thinking about studying? Uh, I see so you, your initial degree was in, not in computer engineering, was it?Andrew Paverd:No. I, I actually did electrical engineering. And then electrical and computer engineering. And by the time I did a PhD, they put me in a computer science department, even though-Nic Fillingham:(laughs).Andrew Paverd:... I was doing software engineering.Nic Fillingham:Yeah. I, so I wonder if folks out there that, that don't have a software or a computer engineering degree, maybe they have a, a different engineering focus or a mathematics focus. Any advice on when and how to consider computer engineering, or sort of the computing field?Andrew Paverd:Yeah. Uh, absolutely. Uh, I think, eh, in particular if we're talking about security, I'd say have a look at security. It's often said that people who come with the best security mindsets haven't necessarily gone through the traditional programs. Uh, of course it's fantastic if you can do a, a computer science degree. But if you're coming at this from another area, another, another aspect, you bring a unique perspective to the world of cyber security. And so I would say, have a look at security. See if it's something that, that interests you. You, you might find like I did that it's a completely fascinating topic.Andrew Paverd:And the from there, it would just be a question of seeing where your skills and expertise could best fit in to the broad picture of security. We desperately need people working in this field from all different disciplines, bringing a diversity of thought to the field. And so I, I'd highly encourage people to have a look at this.Natalia Godyla:And you made a, quite a hard turn into security through the PhD suggestion. It, like you said, it was one course and then you were off. So, uh, what do you think from your background prepared you to make that kind of transition? And maybe there's something there that could inform others along the way.Andrew Paverd:I think, yes, it, it's a question of looking at, uh, of understanding the system in as much detail as you possibly can. And then trying to think like, like an attacker. Trying to think about what could go wrong in this system? And as we know, attackers won't respect our assumptions. They will use a system in a different way in which it was designed. And that ability to, to think out of the box, which, which comes from understanding how the system works. And then really just a, a curiosity about security. They call it the security mindset, of perhaps being a little bit cautious and cynical. To say-Natalia Godyla:(laughs).Andrew Paverd:... "Well, this can go wrong, so it probably will go wrong." But I think that's, that's the best way into it.Natalia Godyla:Must be a strong follower of Murphy's Law.Andrew Paverd:Oh, yes.Natalia Godyla:(laughs).Nic Fillingham:What are you watching? What are you binging? What are you reading? Either of those questions, or anything along in that flavor.Andrew Paverd:I'll, I'll have to admit, I'm a, I'm a big fan of Star Trek. So I've been watching the new Star Trek Discovery series on, on Netflix. That's, that's great fun. And I've recently been reading a, a really in- interesting book called Atomic Habits. About how we can make some small changes, and, uh, how these can, can help us to build larger habits and, and propagate through.Nic Fillingham:That's fascinating. So that's as in looking at trying to learn from how atoms and atomic models work, and seeing if we can apply that to like human behavior?Andrew Paverd:Uh, no. It's just the-Nic Fillingham:Oh, (laughs).Andrew Paverd:... title of the book.Natalia Godyla:(laughs).Nic Fillingham:You, you had me there. Natalia Godyla:Gotcha, Nick.Nic Fillingham:I was like, "Wow-"Natalia Godyla:(laughs).Nic Fillingham:" ... that sounds fascinating." Like, "Nope, nope. Just marketing." Marketing for the win. Have you always been Star Trek? Are you, if, if you had to choose team Star Trek or team Star Wars, or, or another? You, it would be Star Trek?Andrew Paverd:I think so. Yeah.Nic Fillingham:Yeah, me too. I'm, I'm team Star Trek. Which m- may lose us a lot of subscribers, including Natalia.Andrew Paverd:(laughs).Nic Fillingham:Natalia has her hands over her mouth here. And she's, "Oh my gosh." Favorite Star Trek show or-Andrew Paverd:I, I have to say, it, it would've been the first one I watched, Deep Space Nine.Nic Fillingham:I love Deep Space Nine. I whispered that. Maybe that-Natalia Godyla:(laughs).Nic Fillingham:... it's Deep Space Nine's great. Yep. All right, cool. All right, Andrew, you're allowed back on the podcast. That's good.Andrew Paverd:Thanks.Natalia Godyla:You're allowed back, but I-Nic Fillingham:(laughs).Natalia Godyla:... (laughs).Andrew Paverd:(laughs).Nic Fillingham:Sort of before we close, Andrew, is there anything you'd like to plug? I know you have a, you have a blog. I know you work on a lot of other sorta projects and groups. Anything you'd like to, uh, plug to the listeners?Andrew Paverd:Absolutely, yeah. Um, we are actually hiring. Eh, well, the team I work with in Cambridge is, is hiring. So if you're interested in privacy preserving machine learning, please do have a look at the website, careers.microsoft.com. And submit an application to, to join our team.Natalia Godyla:That sounds fascinating. Thank you.Nic Fillingham:And can we follow along on your journey and all the great things you're working at, at your website?Andrew Paverd:Eh, absolutely, yeah. And if you follow along the, the Twitter feeds of both Microsoft Research Cambridge, and the Microsoft Security Response Center, we'll, we'll make sure to tweet about any of the, the new work that's coming out.Nic Fillingham:That's great. Well, Andrew Paverd, thank you so much for joining us on the Security Unlocked Podcast. We'd love to have you come back and talk about some of the projects you're working on in a deep-dive section on a future episode.Andrew Paverd:Thanks very much for having me.Natalia Godyla:Well, we had a great time unlocking insights into security, from research to artificial intelligence. Keep an eye out for our next episode.Nic Fillingham:And don't forget to tweet @MSFTSecurity. Or email us at securityunlocked@microsoft.com with topics you'd like to hear on a future episode. Until then, stay safe.Natalia Godyla:Stay secure.
2/10/2021

Pluton: The New Bedrock for Device Security

Ep. 14
Close your eyes, and imagine a world where booting up your computer wasn’t a susceptibility point for attacks. Imagine a Root of Trust that’s integrated into the CPU. Imagine all of your devices being protected against advanced attacks. Now, what if I told you there’s a cutting-edge processor that’s battle-tested for hardware penetrations, easy to update, and protects credentials, encryption keys, and personal data all at once? What if I told you it was already here, and your systems might already be using it?! Open your eyes, and get ready to be amazed! It’s Pluton, baby! Peter Waxman, Group Program Manager at Microsoft, joins hosts Nic Fillingham and Natalia Godyla in a tell-all about Pluton. Trust us, Pluton is sure to knock your SOCs off (that’s System on a Chip)!Now that your eyes have been opened to a more secure system, we’d like to ask you to keep the volume down, because you’ve just entered the Library of Threats. While it may sound like inspiration for the next installment of National Treasure, you won’t find Nicolas Cage in this library (at least you shouldn’t). However, you will find Madeline Carmichael, MSTIC’s Threat Intel Librarian, whose movie-worthy title is just as impressive as it sounds. To be honest though, you might not find anyone in the library, as it bears more resemblance to Professor X’s Cerebro than it does your local hardcover sanctuary.In This Episode, You Will Learn:  •What the Pluton Security Processor is and how it was created•The architecture of the Pluton Security Processor•What challenges were faced while bringing the Pluton Security Processor to life•The Root of Trust today vs. The Future with Pluton•The naming systems for threat actors, from periodic elements to volcanoesSome Questions We Ask:•What differentiates the Pluton Security Processor from previous methodologies?•Why is the Pluton Processor better than what we have used in the past? •What challenges lie ahead with the next steps around Pluton?•What has changed since Pluton was in Xbox to where it is now?•What tools and platforms does a Threat Intel Librarian utilize?Resources:Microsoft Pluton Announcement:https://www.microsoft.com/security/blog/2020/11/17/meet-the-microsoft-pluton-processor-the-security-chip-designed-for-the-future-of-windows-pcs/Peter’s LinkedInhttps://www.linkedin.com/in/peter-waxman-ba5555/Madeline’s LinkedInhttps://www.linkedin.com/in/madeline-carmichael-081540b2/Nic’s LinkedInhttps://www.linkedin.com/in/nicfill/Natalia’s LinkedInhttps://www.linkedin.com/in/nataliagodyla/Microsoft Security Blog:https://www.microsoft.com/security/blog/Transcript(Fulltranscriptcan be found athttps://aka.ms/SecurityUnlockedEp14)Nic Fillingham:Hello, and welcome to Security Unlocked, a new podcast from Microsoft where we unlock insights from the latest in news and research from across Microsoft's Security Engineering and Operations teams. I'm Nic Fillingham.Natalia Godyla:And I'm Natalia Godyla. In each episode, we'll discuss the latest stories from Microsoft Security, deep dive into the newest threat intel, research, and data science.Nic Fillingham:And profile some of the fascinating people working on artificial intelligence in Microsoft Security. Natalia Godyla:And now, let's unlock the pod. Hey, Nic, how's it going?Nic Fillingham:Hey, Natalia. I am good, I am excited. I've been excited for every episode, but I think this is the episode where we may be able to spin off into a major, major motion picture. I'm quite convinced that one of our guests, their story is compelling enough that a Nicolas Cage-style act, maybe even Nicolas Cage would be willing to turn this into a film.Natalia Godyla:Let's line up the two guests, and l- let our audience figure out which one is the next National Treasure.Nic Fillingham:First up, we have Peter Waxman, who's gonna talk to us about the Microsoft Pluton announcement from back in November of last year. This is a continuation from a conversation we had with Nazmus Sakib a few episodes ago where we talked about ensuring integrity at the firmware layer up and secured-core PCs, and now we're sorta continuing that conversation, deep-diving into what is the Pluton. Our Microsoft Pluton technology was announced in November. Fascinating conversation. And then we speak with?Natalia Godyla:Madeline Carmichael, who has a background in library science and worked in physical libraries, and now she is a threat intel librarian. So her title is MSTIC Librarian, she helps to catalog the different threat actor groups that we monitor. So it's a callback to a conversation that we had with Jeremy Dallman about tacking nation-state actors. Nic Fillingham:Yeah. So Madeline's job, apart from, uh, you know, one of the things that she does is she helps name these nation-state actors. And so we, Jeremy walked us through the, uh, periodic table of elements that is used to actually name who these nation-state groups are. So I just think that's fa- that's fascinating to go from a physical library and sort of library sciences into the deepest, darkest recesses of nation-state threats and nation-state actors. I- I think that is a Nicolas Cage vehicle waiting to happen, and I can't wait to go back into the cinema and we can sit down with our popcorn and we can watch National Treasure 7: MSTIC Librarian. This time, it's elementary? (laughs)Natalia Godyla:(laughs).Nic Fillingham:National Treasure 7: Threat Catalog- Catalog. Don't judge a threat actor by its name. No. Natalia Godyla:I see it. I know why you picked Madeline's. I feel like we probably need a little bit more help on that tag line, so if anyone wants to give us some feedback, securityunlocked@microsoft.com, let us know. We are actively working on this script. Nic Fillingham:On with the pod?Natalia Godyla:On with the pod.Nic Fillingham:Welcome to Security Unlocked. Peter Waxman, thanks for joining us.Peter Waxman:Thank you, great to be here.Nic Fillingham:So this is gonna be the second of three deep dives we do on the sort of very broad topic of ensuring the integrity and the security of physical devices through things like protecting firmware, and obviously we'll expand upon that in this conversation here. Peter, you're joining us today to talk about the recently-announced Microsoft Pluton processor, so that, this is gonna be great. We're excited to chat with you. Nic Fillingham:Um, before we get into that, we'd love to ask. Tell us a little bit ab- about yourself. What's your job? What team are you in? What's the mission of the team? What's your day-to-day look like?Peter Waxman:Awesome, awesome. At Microsoft, I work in, uh, the Enterprise Security team, part of the so-called Azure application platform. Basically what we do broadly is build all the operating system platform and everything underneath. You can think about it as Windows, the operating system, you know, Windows that powers Azure. Even what powers Xbox and some of our other devices. Peter Waxman:And in particular, what I do is I focus on the operating system security and the low-level platform security that that operating system depends upon. Think about the hardware and firmware that our partners produce, to go make sure that that experience is completely secure. It protects our customers' data, it protects their identities, it makes sure that their application run with integrity and that they don't get hacked. And if they do get hacked, that we have an easy way to update and renew the system to get them in a good state again.Natalia Godyla:And so, we recently announced on November 17th the Pluton processor. Can you tell us about that? What- what is Pluton?Peter Waxman:Yes. Yeah. This is a big, exciting thing. It's something that we've been working on for quite some time. What Pluton essentially is is it's basically a security chip that lives inside of a larger chip. We call it basically the Pluton security processor, and this is like the heart of the security system in a PC or in a device. Peter Waxman:If you think about the security of a device, when you push power on that, when you push power on your laptop or computer, the, and the CPU comes up, one of the most important things is that the way that that system boots up and starts happens in a secure fashion. Because if it doesn't happen in a secure fashion, then it's very easy for bad actors to basically get in underneath and to root the system and cause all sorts of problems. Peter Waxman:So what Pluton is is basically this root of trust, the security processor that we, Microsoft, are integrating, and which is what we announced along with our major silicon partners in AMD, Intel, and Qualcomm, into the fabric of their products, in to the fabric of their chips. And so, by having that tight integration, it ensures that basically those chips and those products come up and boot in a secure fashion, and that we can then run Windows on this trusted foundation where we know the system is secure and basically we have, uh, much stronger footing with Pluton in the system going forward.Natalia Godyla:So what differentiates the Pluton security processor from previous methodologies? What were you using in the past? Why is this better?Peter Waxman:So traditionally in, uh, most PCs, the root of trust today is actually a separate chip. You know, very typically a discrete TPM. And that is something that lives on the motherboard as a separate unit, but it basically communicates over an insecure bus to the CPU. And the problem with that is that it just, it lends itself to all sorts of attacks. There's been a variety of ones that have been published. One of the common things that it's been known and in a published attack, basically there's one called TPM Genie. That bus, because it's insecure, even though the TPM chip itself may be highly secure, the system overall is not. Peter Waxman:And so, attackers can go in with very inexpensive hardware, a logic analyzer, $50 worth of equipment, and go and basically intercept and alter the communications between the CPU and the TPM. And end up basically, you end up with an insecure system as a result. You could actually be booting malware in the firmware. You could basically be booting with exploits all through the boot chain, and Windows wouldn't know about it. The customer's data and experience would be compromised as a result. And so, by moving the root of trust into the CPU die, we're basically taking a whole class of attacks out of the scope, resulting in a system that is more secure overall in terms of how it comes up and the foundation. Peter Waxman:It's also something, though, that one of the challenges that exists with the existing roots of trust is that they're very hard to update. Like other components in the system, right? They have their own firmware, the firmware can have vulnerabilities, and in fact, there have been notable vulnerabilities that have existed in TPM firmware. And when we look and see across the inventory of Windows 10 systems out there, there's actually a very large number of TPMs that are running out-of-date, unpatched firmware.Peter Waxman:Uh, as a result of having Pluton integrated into the CPU and having tighter control of it from Windows, we can leverage the decades of experience and billion-plus endpoint reliability that we have in Windows Update to offer customers the ability to much more easily and automatically update firmware on the root of trust of the system. If there's ever any security issue that we find, we can very quickly get an update out. We can also, importantly, update with new capability, so as new scenarios come online, where customers want to take advantage or applications want to take advantage of this root of trust, we have the ability to add that capability to Pluton in a easy, quick ability through Windows Update. Natalia Godyla:So what challenges did you have with bringing this security processor to life, with bringing it to PCs, in particular with the partners and OEMs that we were bringing it into the market with? And- and what challenges still lay ahead with the next steps that you have around Pluton?Peter Waxman:Yeah, so there's plenty. I mean, there's a- there's a tremendous, uh, satisfaction that we have and, you know, came to the point where we have been able to announce with our major silicon partners that we're bringing this to market. But I'm humbled by it, but at the same point we still have a ways to go before this comes to market. And to continue really in seeing to the vision, which is really to enable Pluton everywhere and to be ubiquitous even beyond PCs and- and gaming consoles and- and IoT devices.Peter Waxman:So- so a lot more work to do. Working with the ecosystem is something that takes a lot of time. It's been a tremendous effort, it's been several years in the making just to get to this point where, you know, we're far enough along with our partners that we can announce it, that we feel confident around landing these products. Both with the silicon partners that we announced, as well as with a range of PC OEMs that have been with us on this journey over the last year.Peter Waxman:We're at a point, though, because, you know, we're basically taking Microsoft technology and integrating it with our- our silicon partners, it's our silicon partners' products that are the ones that will bring this to market on OEM devices. They are not yet ready to announce sort of their particular timeframe intercepts, so unfortunately I won't speak to exactly when products land. But, you know, they are coming, folks should stay tuned. Peter Waxman:And when you think about Intel or AMD or Qualcomm chip, kind of the rule of thumb is it takes three years to go from the time that you start the design to the time that you have the chip in hand. So that's a long process. We're well away, well along that path in terms of where we're at, but it's lot of, obviously, detailed architectural work. Peter Waxman:We're excited about, uh, the product finalization and also thinking about sort of the next set of steps and next silicon products for integration. But it's- it's a huge effort across a range of companies to- to land something like this.Nic Fillingham:Is the goal to be integrated across the entire silicon spectrum in terms of consumer, low-end, affordable consumer devices, all the way through to secure e-work stations, uh, and sort of everything in between? Or it specifically a solution for more security-conscious, sort of enterprise customers?Peter Waxman:Great question. Yeah. No, so this is important. We see this capability as something that just is a fundamental security property that needs to be there on a modern device. We have seen, we've all seen how over the last 10, 15 years there's just been an increasing amount of sophistication, not just in software attacks but in attacks that basically deal with low-level aspects of vulnerabilities in firmware, hardware attacks that exist. You can get up to nation-state stuff, and we see things, whether it's in the Snowden leaks or particular instances of nation-state attacks, that are taking advantage of, say, firmware vulnerabilities.Peter Waxman:But it's more common that than. I mean, there are criminal networks that have exploited UEFI components in PCs to basically connect PCs to botnet networks to cause a variety of- of issues there. There continue to be, on a week-in, week-out basis, month-in, month-out basis, vulnerabilities that are reported that exist in a variety of firmware components or new hardware disclosures that exist. Peter Waxman:So it is something that is cross-cutting, it's something that is not just an enterprise issue. It's something where, you know, this raises the security of all devices, and is basically something that the average consumer has a right to expect of their device. That expectation Peter Waxman:Absolutely needs to be there from the lowest end consumer device to the highest end enterprise device. We... And, and Microsoft just committed to that. Natalia Godyla:So with Pluton becoming a new industry gold standard, I'm sure that also means that it'll become a target or a goal for hackers to try to break into. So, what are the challenges for hackers? What would they need to overcome in order to actually hack to Pluton processor in a, in a hypothetical situation? Peter Waxman:Yeah, it's a good question. I mean, there's certainly, especially in the research community, there's a lot of established, uh, research and techniques that folks do to, uh, break into hardware products. I mean, we've seen that certainly, like, going back to the Xbox days, right? There's, uh... One of the things that's interesting about sorta the consumer gaming security space is that in order for the adversaries to thrive, they're not necessarily a criminal network, they're not a nation-state, and they need to share information so you can kind of observe them more easily. But there are techniques and capabilities that folks have addressed and, obviously, with Pluton we're trying to ensure that we are targeting a bar that makes it very challenging for them to attack the system. Peter Waxman:It is one, though, we're never gonna say that there's any perfect security system, and so you have to design your system to be renewable. You have to allow for the fact that they're going to be, gonna be issues that are gonna be found and make sure that you can update, you can patch, and also that you have defense in depth. So, if a hardware measure is defeated, you have something backing that up. We feel confident about, uh, Pluton just in terms of its, it, it is battle-tested. Peter Waxman:This is something that we started on this journey 10 years ago. We've continued to invest in the capability and we're not done investing in the capability. We will continue to harden and strengthen it over time. But it's, you know, we're, we're talking about super cool equipment that a variety of folks'll go over to try to glitch and figure out what timing abilities does an attacker have to figure out if they issue a, a 20 nanosecond pulse on exactly this pin and exactly ti- this time at boot can they glitch the system to cause a, a, or, say, a crypto operation or what have you to basically fail. Peter Waxman:These are the rates of attacks that come into a scope when you get into hardware security and, so, we've got a bunch of super bright folks that are experienced in this space, but, uh, we'll be interested to see how the threat actors respond and... It's also important to note that Pluton, we don't trust in the system, there's a critical security component, but it's not the only security component, right? The whole stack of, uh, security that, you know, st- stands on top whether it's an OEM device and their firmware or in Windows itself or in applications. These all matter, too. Peter Waxman:An application can still have a vulnerability in it that is remotely exploited regardless of Pluton being in the system. And, so, you've got to look at the whole system from a security perspective to make sure that, uh, we're continuing to drive security across, up, and down the stack. Nic Fillingham:And, Peter, I assume, uh, Microsoft, as well as the actual silicon manufacturers, you know, they're actively gonna be pen testing, uh, the Pluton processor over time, right? So, as Pluton is defined and as it goes into production and as it actually gets into the hands of, of customers, there'll be a continual effort on behalf of Microsoft and, I assume, also the silicon partners, too. Keep it secure and, and see if we can hack it ourselves to, to deter and find any potential vulnerabilities and address them. Is that part of the process?Peter Waxman:Absolutely. Absolutely. Nic, so, Microsoft, the history that we've got with Pluton, we have both ourselves and involved third parties in doing hardware penetration tests, hard- hardware hacking on it to assess its strength. We have a, a long history of working with our hardware partners on hardware security and working with them on basically issues in firmware and hardware in their silicon. And, obviously, for the particular partnerships, both parties, you know, in this case Intel, AMD, and Qualcomm, are fully aligned with us in ensuring that their security teams, our security teams, red team and pen test teams, and external evaluation that, basically, we get as much eyes on this to find any issues before anyone else does and, hopefully, to not find anything, which has been the case to date. When we do, to basically respond and, and react to, uh, accordingly with our partners. Natalia Godyla:And, what learnings did you have so far from the days in which you put Pluton into an Xbox and now? Like, what have you changed in the processor for the PCs for this new announcement? If, if anything?Peter Waxman:We've evolved in a number of areas. I think that one is that just the application of it is different somewhat in the PC than it is in an Xbox than it is in an IoT device. So, for example, TPM functionality, which we talked about earlier is something that we don't need a standardized TPM in the Xbox. It's all sort of vertically integrated. Stack, we do things that are similar to a TPM, but we don't need that capability. But in a PC, that's a standardized functionality that exists in pretty much every PC today. And, so, there are capabilities that we've added to be able to, say, support that from a firmware perspective and where needed to add additional hardware blocks.Peter Waxman:We have advanced. There's places where it's just a matter of hardening the design that we have in Pluton. So, some amount of resistance to physical attacks that we've increased over time. And, it's also, you know, supporting newer capabilities that may exist in, in the industry. If I think back to Xbox days, the expectations around crypto key lengths, for example, right? We didn't have as many crypto algorithms or quite as long key lengths. We supported, say, in the, you know, early implementations of HSP versus today. Now that we have quantum crypto creeping up on us over the next 10 to 15 years, right? There's a much higher focus, for example, on longer crypto key lengths to make sure that we can maintain resistance until we get to sorta implementation, more common implementations of post-quantum crypto algorithms. Peter Waxman:So, some examples of places where we have just evolved and, um, you know the way Microsoft views it the Pluton-based, the, the architecture and design is something that we evolved for all end points and, so, you'll see, for example, that the Pluton is in the latest Xbox series X and S that we announced, came to market with, and launched in November is a more advanced version, right, based upon that newer capability set then what was there in the Xbox One. So, as I mentioned, continue to sort of update this technology and continue to make it available through these range of markets.Nic Fillingham:I want to ask about the architecture of the Pluton security processor. When it goes onto the actual CPU die, is it going to be a tax on the CPU? Is it, or is it sort of occupying such a trivial amount of sort of transistors and, you know, storage elements that you're not gonna know that your computer is Pluton powered? It's just gonna be happening silently and completely invisibly in the background.Peter Waxman:Yeah. That's r-, that's right. It is, from a power perspective or sort of any other aspect from an end-user, you're... Basically it's a small component when you think about it in relation to a modern SOC or modern CPU. It's not taking any relevant amount of power that's at all gonna be noticeable from the device perspective. It's basically this hidden component inside the SOC, system on a chip, complex that, uh, is basically working on your behalf ensuring you have a much higher security experience as a result, but you will not notice it being there. That's right. It's basically invisible. Nic Fillingham:And, and just circling back to that Xbox comment, so, so I've got an Xbox One, uh, here at home. It's the Xbox One S.Peter Waxman:Yep.Nic Fillingham:So, there is a version or a precursor to the Pluton on my Xbox. Is it Pluton v. 1 or is it pre-Pluton? How should I sort of think about that? Peter Waxman:You've got Pluton. You've got Pluton.Nic Fillingham:I've got Pluton? Peter Waxman:You got Pluton.Nic Fillingham:Yeah. Peter Waxman:Yes.Natalia Godyla:(laughs)Peter Waxman:(laughs)Nic Fillingham:Can I get a sticker? Can I get a sticker to put on my Xbox that says you got Pluton, baby?Peter Waxman:I will get to work on that, Nic. I love the idea. I love the idea. I think... I... Your t-shirts and stickers. I think that's, you know, that may be the, uh, the holiday project coming up. Nic Fillingham:And, then, so, moving forward, at some point, when I'm buying a new piece of computing, whether it's a laptop, whether it's an IoT device, or I get something else with a CPU inside it, I'm gonna want to look for probably a Pluton sticker or a Pluton badge or something that lets me know that the CPU or the SOC contains the Pluton architecture. Is that, again, part of the vision for Pluton?Peter Waxman:It's a great question. I don't think we've come to a conclusion on it. I'm not sure that we're gonna get to the dancing Intel guys in their, uh, clean suits, uh, commercials on T.V.Nic Fillingham:That's a, that's a callback to, like, is it the 90s? When they do that? That was a long time ago.Peter Waxman:(laughs) Yeah. That's, that's showing my age there, perhaps. Nic Fillingham:Natalia wasn't born then. She doesn't know what that is. Peter Waxman:(laughs). Natalia Godyla:Right over my head. Peter Waxman:(laughs)Nic Fillingham:(laughs) But, I mean, in terms of as a consumer, or a potential consumer, or even just a, you know, an employee at a company, do you envisage that it'll get to a point where I'll have, you know, an opportunity to buy a Pluton secured device and a non-Pluton secured device and so, therefore, I'm gonna wanna think about my needs, my security needs, and make sure I'm getting that Pluton secured device or, again, maybe to what you said earlier, it's just gonna be completely invisible, completely integrated into the silicon? You're not gonna worry about it, but you're just gonna know that there's, there's a higher grade of sort of fidelity and security on that device because of the architecture in the CPU. Peter Waxman:Yeah, I mean, our goal is really to get to that point where it's ubiquitous and it's just there. I mean, it's, again, if we're gonna provide, uh, customers with the level of security that is required in today's day and age, we've got to get to a point where this is like oxygen. It's everywhere. It's just a common ingredient that exists. We have to work with our ecosystem. We have to basically work to a path where, you know, we get there. It's not on the market yet. It's gonna take some time. There will be points in time where it's a journey to get there and not every system is, is certainly gonna have it, but our vision is this just needs to be everywhere. Peter Waxman:It's something where, you know, we're doing this not to make money off of this thing. Not to basically drive specific scenarios. Not to charge and up-prem as we talked about earlier for enterprises. This is about how do we make sure that everyone from consumers to enterprises to you name it has something where we're taking the last 15 years of hardware and systems security, hard learnings, and bringing it and modernizing the PC space based upon those learnings. Nic Fillingham:How did you come up with Pluton? I had not heard Pluton before I plugged it into Wikipedia, which is the font of all knowledge and it tells me that it is an igneous intrusion... No. No. It is a body of intrusive-Peter Waxman:(laughs).Nic Fillingham:... igneous rock. So, how'd you get Pluton, but, maybe more importantly, tell me some of the names that you considered, but didn't go with? Can you-Peter Waxman:(laughs)Peter Waxman:... can you let a few cats out of the bag? Proverbial cats out of the proverbial bags? Natalia Godyla:Most important question. (laughs)Peter Waxman:So, this one, Nic, I think we're gonna have to put the pause button on the recording-Nic Fillingham:Ahhh.Peter Waxman:... and I actually have no good answer nor do I have a great joke to go, uh-Natalia Godyla:(laughs)Peter Waxman:... to go, to go make fun. You know, so, it's, like, code name/buzzword that we use publicly. It's one word. It sounds cool. Nic Fillingham:It does. Sounds very cool.Peter Waxman:It's not named by anything else. And, uh, it's... If you think about hey, this thing is going to set the direction and do something leading, it's, like, a north star that's out there. Sounds cool. I don't know what it means. Nic Fillingham:(laughs)Natalia Godyla:(laughs)Peter Waxman:I didn't even know it was an igneous rock until you mentioned it, honestly. But, uh, yeah. Exactly. I...Nic Fillingham:It is an igneous intrusion.Peter Waxman:Igneous intrusion. I stand corrected. Natalia Godyla:(laughs) Peter Waxman:God. I'm gonna have to go look up that 'cause that, that's kind of freaky and scary. Natalia Godyla:I feel like that's the best answer.Peter Waxman:(laughs).Natalia Godyla:It sounds cool. Nic Fillingham:It sounds cool. That's totally-Peter Waxman:It's authentic. Natalia Godyla:(laughs)Nic Fillingham:Yeah. That's totally fine for it to sound cool. I did wonder if there might have been something a little bit more sort of esoteric and, and deep to it, but I'm totally happy with it sounding cool. We'll have to, we'll have to go and talk to some of your colleagues to see if, uh, maybe Dave Weston can let us in on a few, uh, names that didn't make it that we could, we could make fun of on another podcast episode. Peter Waxman:Yeah. Microsoft Bob was one option, but it was taken. So, uh...Nic Fillingham:(laughs) Peter Waxman:Yeah. No. Dave will be good to, uh, get history there.Nic Fillingham:Peter Waxman, thank you so much for your time and for joining us. And, uh, I would love to have you back on the podcast on another episode to discuss the history of Xbox security and maybe mod chips and Xbox hacking and all that cool stuff that we all did in the early 90s. Oh, the early 2000s-Peter Waxman:(laughs)Nic Fillingham:... I should say. Peter Waxman:Awesome. Awesome. I really appreciate it, Nic. Natalia, it's been an awesome discussion so thank you very much.Natalia Godyla:Yeah, thanks for being on the show. Natalia Godyla:And, now, let's meet an expert from the Microsoft security team to learn more about the diverse backgrounds and experiences of the humans creating AI and tech at Microsoft. Natalia Godyla:Hello, Madeline Carmichael. Welcome to the show. Madeline:Hi, thanks for having me. Natalia Godyla:It's great to have you on the show. I have never talked to a threat intel librarian before so let's start with that. Can you, can you tell us about that role? What does your day-to-day look like? How did get into Natalia Godyla:... becoming a Threat Intel Librarian.Interviewee:Yeah. I mean, I can pretty safely say you're among good company in not having met someone with that job title (laughing). I get a lot of really interesting reactions to the title. And, to be honest, it's kind of self-styled (laughs), so it's not like an official Microsoft HR title. But that's the one I go with for my day to day function and what I actually do. So, basically, I work as part of the Threat Intel team in the Microsoft Threat Intelligence Center and as a Threat Intel Librarian for them. And that means I'm sort of responsible for organizing a nation-state threat actors that we track and supporting the end-to-end business process that enables the team to do that as efficiently as possible.Interviewee:So, recently, I've added being a MITRE attack evangelist to my description and my role. So I look at how we can integrate that framework into our workflows and how that can help us do more with our data to support internal workflows. But also how we can share better Intel with our partners. And the MSTIC team sort of tracks nation-state actors, primarily. There's a little bit of wiggle room around human-operated ransomware. It's becoming a, a more concerning threat and we're, we're onboarding some of that. We currently have more than 270 groups on our radar and that's between named groups that we, we name after the periodic table of elements.Interviewee:So, so when we speak publicly, you'll hear things, uh, named after that. And then we have what we call dev groups, which are sort of the pre-stage, it's for our internal tracking and to keep, keep things in order. But we don't tend to discuss those publicly. Yeah, we do like security detection, analytics, um, response capabilities for Microsoft end customers. And that kind of entails providing threat intel to Microsoft and defender teams across the company, and then out to customers through security products. So I originally started as a, well, thought I was going to be a librarian and probably a public librarian at that. I was doing that degree and there was an option to do, uh, an internship or a co-op for credit, not a requirement, but I found an interesting job posting.Interviewee:So took a chance and applied for it and got it. And that was with a research library for the government of Canada. And that was great. I really, really enjoyed working there, and actually, ended up finishing my last (laughs) two degree credits distance while I was still working. That kind of led to moving on to a team that my role was doing aggregate reporting and sort of trend analysis a little bit for the executive leadership at the org. And from there, just got interested in the actual cybersecurity analyst part of the team, and eventually, moved over to that, which was where I got the skills that kind of transitioned into my role at Microsoft.Natalia Godyla:I'm just going to un- unpack some of the roles there and some of the skills that you're, you're bringing to role as a Threat Intel Librarian. So in the research library, when you're saying that you got into data reporting, what, what were you reporting on?Interviewee:So that was mostly incidents that have been tracked by that team during the month or the quarter. And so it was just kind of aggregating that data in sort of human-readable format that could be sent up to executive leadership. So they were aware of kind of the high level trends that were happening.Nic Fillingham:But, you, so when you were studying, you said you, you found a job posting, you said it was an internship, is that correct?Interviewee:Yeah, co-op internship. However you want to call.Nic Fillingham:Got it, a co-, a co-op, and that was with the government of Canada/Interviewee:Yep.Nic Fillingham:And is it accurate to say that was sort of more of a traditional librarian style role? You, you are physically in a building that had a lot of sort of printed stuff or am I like way too old school and antiquated in my thinking (laughing)?Interviewee:No, it was kind of in the middle of that. There was a physical library, and yeah, definitely more towards the traditional end. Slightly untraditional, I guess, in the sense that it was like a focused collection. So it was specific to the type of research that, that group was doing. But, otherwise, yeah, books and cataloging and, uh, organizing that.Natalia Godyla:Why cybersecurity or how were you exposed to cybersecurity? Was it part of the research that the library had or was it just that subsequent roles brought you closer and closer to the field?Interviewee:Mostly the sort of subsequent role is getting closer and closer. It feels pretty serendipitous when I look back at it now. Like I didn't intentionally set out for a career in cybersecurity or Microsoft or where, where I am. I, uh, did a presentation a couple of years ago for a conference, uh, in the UK that's run by a woman at Microsoft and it's called TechHer, more, more like TechHer. So I did this presentation at TechHer, which is a, a conference run by Microsoft UK. And it aims to kind of give women more networking opportunities and sort of more visibility into technical roles. And during that presentation, I, I called myself an Accidental Threat Intel Analyst.Interviewee:At the time I was still in that analyst role, more the, the Threat Intel Librarian role. And it's kind of true, like I never intended for that. Accidental is maybe giving myself too little credit for taking some, some opportunities that presented themselves (laughs). But, yeah, it was just kind of each pivot kind of brought me one, one step closer and I thought it was really interesting. And I've been lucky to work with people who are really engaging and their passion for it is contagious. So, yeah, I guess that's why I stuck around.Nic Fillingham:So what do you do as the Threat Intel Librarian to expand the collection of knowledge and data and, and papers and content in a particular direction? Who, who are your customers and, and how do you go about expanding that collection?Interviewee:My customers, I guess, or my, my user base would be the threat analysts on the team. And the collection of data is their analytic output, essentially. So it's less curating new collection and less providing resources as it is organizing the output that they're producing. So we have a, a knowledge base that holds all of the threat intelligence that the team produces. And the aim there is to organize that in a way that makes it more friendly for capturing data, but also, um, produces more usable output for downstream users, whether they be in Microsoft as other security teams or Microsoft customers through security products.Nic Fillingham:And what tools or sort of platforms do you use, you know, this knowledge base? Are you, is it SharePoint or is it some other sort of more secure encrypted storage system? I mean, uh, maybe you can't talk about it, but, but what sort of in, in a general sense do you, are your tools that you're using day in, day out?Interviewee:So that's changed over the years since I've been here. I've had a number of iterations where we store things, we, we're using, uh, DevOps at one point and kind of mashing that into our scenarios. But we're now using a proprietary knowledge base that's being developed by a dev team out of ILDC.Natalia Godyla:So what big goals do you have around the library that you are maintaining, building? What's, what's next for you to optimize? What are some challenges that you're trying to tackle?Interviewee:Well, yeah, so the, the nature of tracking nation state threats and like threat actors is that capturing the relevant threat intel means you often end up with a lot of data that's constantly evolving based on what the actors are doing. It's hard to keep tidy. So the ultimate goal, I guess, is to make our knowledge base as organized as possible to enable as much automation as possible. The threat analysts do a lot of repeatable pivots or queries. And those are really important for, for maintaining an ongoing awareness of what the, the threat actors are doing. But a lot of that can be codified and then made into a repeatable process where they just have to like check in and make sure it's functioning accurately.Interviewee:And then that allows time for them to do the really clever stuff that takes nuance and a human sort of intuition and experience with tracking for actors to do well. Not all of it can be reproduced by a computer. So as much of the sort of day-to-day stuff that we can automate as possible, that's, that's great. And we do that by having well-labeled classified data that's organized, and yeah, we can feed it to an automation pipeline and then let the analysts do the fun stuff.Natalia Godyla:So speaking of classification, we, we chatted with Jeremy about how we came to the names of some of the threat actors. I know you mentioned we use the periodic table. What was the impetus for that? Why are we using the periodic table and wha- what's going to happen after the periodic tables run-up?Interviewee:(laughs) Uh, well, that was in place before I started. So I, unfortunately, can't take credit for (laughing) why it was chosen. I think it was probably chosen because it's a, a, a ready set of names that are easily identifiable to the general public. You can kind of say we named things after periodic elements and most people will know or have some familiarity with that. So there's some, not really branding, but that kind of familiarization so that if you hear a name like that, you think MSTIC and Microsoft. It's also not rooted in a specific culture, really, so there's not any cultural connections or connotations that you need to worry about for applying a name. It's going to be used publicly and associated with Microsoft (laughs), so.Nic Fillingham:One of the questions we asked Jeremy was, is there a logic behind why one particular group would be given a particular element? Like, you know, are all the inert gases, are they, are they a particular continent or something? Or were they all discovered in the 2000s? Is, is there, is there any logic or is it, is it... because I think the, the joke we made with, with Jeremy was whether or not there was a, a big periodic table of elements against a wall? And then there was a bucket of dots (laughing). And as a new group comes out, you grab a, you grab a dart and you throw it at the wall. Uh, where are you in that continuum?Natalia Godyla:It's funny the second time around too.Interviewee:Yeah, I mean, honestly, I wish that was the case. It would be pretty cathartic, I think. But, no, there- there's no logic to the, the name choices we decided or my predecessors decided not to add that layer to the naming. So they're meant to just be just a name. We're, I think, careful as Microsoft about what kind of associations or what we mean when we say, like, we, we choose what we say carefully. And I think it was intentional not to associate that sort of, um, this type of name means this origin for an actor. We, we wanted to have that level of abstraction still.Natalia Godyla:There are more groups, though, don't you track more groups than there are elements in the table? Is that, am I right there?Interviewee:Yeah, so we have two types of groups. The ones that have element names are what we would call sort of permanent groups, or it's a permanent name. And that kind of is just the level of, uh, awareness we have for the group. So it's a more mature understanding of the threat actor that has that name. Um, we have a second type of name and we, we call them dev groups, um, dev for development. And it just means they're, they're in development and they're not as fully f- fleshed out as the element names. So it gives us a little more flexibility to kind of label clusters of activity without having to do as much rigor b- behind that sort of is that cluster and what its scope and breadth is.Interviewee:So there's definitely cases where multiple dev numbers or dev groups will merge into one named element group as we develop more of an understanding about who the threat actor is. Um, yeah, so I think we have over 185 dev groups on the go at the moment, and then 89 element groups. And that will probably change very quickly. So the numbers are not actually that useful (laughs), uh, uh, long-term, but yeah. It, we, we have more dev groups because they're easier to spin up and faster and they're, they're meant to be precursors for the named groups. But as, as you say, there are not that many elements. So we, uh, we'll be running out rather soon (laughs). I'm not sure what's going to come out.Nic Fillingham:You'll be into the theoretical element-Interviewee:Yes.Nic Fillingham:... category, genre. What's the one from, uh, Avatar? Unobtainium or something?Interviewee:Yeah, yeah, I think that might be it (laughing).Nic Fillingham:Was that right? And then there's, what's the one that's bonded to Wolverine skeleton? That's, that's a made-up one too, isn't it?Natalia Godyla:Oh, you have an, uh-Nic Fillingham:Adamantium, Adamantium (laughing).Natalia Godyla:... wealth of knowledge about this (laughing).Nic Fillingham:Yeah.Interviewee:We recently actually added another name schema and they're named after volcanoes. I don't know if that came up in your conversation with Jeremy, but as we put more focus on tracking human-operated ransomware groups, we thought they're distinct enough from the nation-state groups that we would have a separate schema for those. So there's some, some volcano names that are out Interviewee:... they're now, and it's the same kind of idea where dev numbers still support both names. And as we develop maturity, it, of awareness on a group, if it's a nation-state, it'll get an element and if it's human-operated ransomware, it gets a volcano.Nic Fillingham:You know what? I probably should've asked this at the tippy-top of the conversation, but why do we name these groups? What is the value in assigning a name and then actually sort of publicizing the name of that group? Where, where is the value to threat hunters to analysts to customers? What- what's the rationale behind this?Interviewee:Yeah. So, I guess it's mostly for consistency. It's, it's kind of a language of its own. And you use language to communicate, so having a name and being able to explain what that name means is important. So, one of the other things that our team does is write activity group profiles. They go along with alerts in security products. Interviewee:So, a customer might get an alert and they'll get this, this document that contains context of what that means for them, and that will include things like the TTPs that that group uses, some of their infrastructure, or like malware that goes along with it, and context that kind of explains their typical motivations or their typical targeting. Interviewee:So if you're in an industry that is a, a usual target for that group, it might make sense for you to say, "Oh, yeah. Like, it makes sense that we were targeted, it makes sense that this alert is hitting our network, or our endpoints." Interviewee:But it is also useful to know if you're an outlier in that circumstance. That might mean you pay more attention to it because you're not a typical target for that group. But yeah, so having a name is just a, a way to kind of say, "We mean this group," and here is the context that goes with it, and it's a consistent message.Natalia Godyla:What other ways are customers benefiting from this library? So, you noted that the alerts will have some of this context that you've been gathering. What other features or capabilities are based on the library?Interviewee:So, yeah, it's our awareness of the group long term. So, it allows us to kind of see what we would expect of them. We, because we have this body of knowledge built up, we can then see quickly if a tactic or a technique that they're now undertaking is brand now. That's kind of a departure from their normal M.O., that's more interesting. It's useful context. Interviewee:Yeah, for Microsoft as well as customers, we use our own TI to help defend ourselves. And, yeah, I guess it's just a, a way to kind of contextualize what is happening with IOCs or indicators of attack. They're kind of distinct bits of information that help you detect or protect or respond to a threat.Interviewee:They contextualize indicators of attack or IOCs, and those, those can be really s- like, small bits of information that help you detect a threat actor. And just having an IP address doesn't really tell you a lot, so that's useful to kind of have that explanation that goes with it that says, "This IP address is used by this group in this way," and that informs how you respond to it as well, depending on the, the attack slide, is useful for how you mitigate that. Interviewee:And that's a, a big part of why we're starting to add the, the MITRE ATT&CK classification to our data as well. It's a clearer language or repeatable way of describing something to your customers. And the customers as well have started to use attack labeling in their own data sets, so it's a good way to kind of match things up.Interviewee:And you can layer customer protections that have been mapped to the attack framework with detections on our side that have those attack techniques labeled. And when you layer those on top of each other, you can find gaps really easily and find how they might need to improve their security posture in a certain area.Interviewee:If, say, its reactor uses a certain technique and that, that customer has a, a gap in detections in that area, they can go, "Oh, well, we are a typical target for this group. We're not super well secured in that area. Maybe we should focus our investment there."Nic Fillingham:So, is it accurate to say that naming these groups and sort of building and maintaining a profile on them allows both hunters and analysts and then customers to better understand where they may or not be a target, and then therefore, how their security strategy should evolve?Interviewee:Yeah, definitely. Yeah. Natalia Godyla:(laughs) Nic Fillingham:Cool. I got my head around it. I must admit, the very first time I read a, a blog post from Mystic and I, I saw, you know, the name, like, "Here's the name of the threat actor and here's what other industry groups sort of name them," I was like, "I don't get it. Why, why are we naming them?"Interviewee:(laughs) Nic Fillingham:But, I, I got it now. So, thank you so much.Interviewee:(laughs) Cool, glad that came through. (laughs) Nic Fillingham:I'm glad that this podcast exists, exclusively for me to, to get my, (laughs) get my questions answered. Natalia Godyla:(laughs) Interviewee:(laughs)Nic Fillingham:Hopefully someone had a similar question and we, we helped answered them. Thank you.Natalia Godyla:So now that you've been in the cybersecurity space for several years now, come to a role that feels like it marries a lot of what you've studied and done throughout your career, the cybersecurity and library are coming together in the name. What comes next that is... Does this feel like it's a merging of the worlds or is there something you want to do after this, either in the cybersecurity space or not?Interviewee:That's a great question. Yeah, I wish five-year planning came easier to me. (laughs) Natalia Godyla:(laughs) Interviewee:Although in, in the world of COVID, I don't know that anyone can plan that far ahead. But yeah, I, I don't know. And I think because I got sort of sidetracked from my original public library path, I haven't really thought about how I would go back to that. Interviewee:I mean, libraries are becoming much more digital now anyways. It's a great way to serve more content to your patrons and your, your, your users in the world of e-readers and eBooks and podcasts and things like that.Interviewee:Libraries procure that kind of content for their users all the time, but yeah, I don't know. I don't, I don't know what's next. I mean, I'm happy where I am. So, yeah, stick here for a little while. Nic Fillingham:Madeline, one of the questions we'd like to ask in, in this part of the podcast is what from your personal life, your hobbies, your interests outside of work, so first of all, what are they? And second of all, do any of them, do you bring any of them into your day job?Interviewee:Yeah. I mean, I feel like this is where your assertion earlier that I broke all of the librarian stereotypes will fall down, because I do love to read and I have two cats. Um... (laughs) Natalia Godyla:(laughs) Nic Fillingham:(laughs) And you just travel round to libraries with your-Natalia Godyla:(laughs) Nic Fillingham:... with your cats and your, and your book bag? That's all you do? Interviewee:Uh, yeah, yeah. I mean, if the cats were allowed in the library, that would definitely be something.Natalia Godyla:(laughs)Interviewee:But I think library tourism is a very underrated area. Expedia should look into that. Nic Fillingham:And apart from reading, cats, and visiting other libraries, is there anything else you're willing to divulge?Interviewee:(laughs) I don't know that a lot of it actually makes its way into my day job. Baking is another hobby, but we're not in the office, (laughs) so I can't really share that with anybody. Nic Fillingham:What's your favorite baking show to binge? Are you a Great British Bake Off fan?Interviewee:I am. Since moving here, I've definitely started watching that.Natalia Godyla:(laughs) Nic Fillingham:Have you thought about entering? Do you wanna be a contestant?Interviewee:I did actually consider it at the end of this year's series, but I haven't got up the nerve to actually apply yet, and I don't know that I could take the pressure of having to figure out all of those (laughs) different baking techniques without a recipe. (laughs) Natalia Godyla:What is one of your favorite books of all time? I was gonna say, what's your favorite booK? But I feel like that's just an impossible question to answer, unless you have one.Interviewee:I, so I generally read fiction. That's my primary genre, but that kind of covers a lot of different (laughs) sub- sub-genres of fiction.Natalia Godyla:(laughs) Interviewee:I think my go-to answer for my favorite book is usually Anna Karenina by Tolstoy. (laughs)Nic Fillingham:In the original Russian? Interviewee:Of course, yeah. No. (laughs) Nic Fillingham:(laughs) Natalia Godyla:(laughs) Interviewee:No. Yet, I should say. Um-Nic Fillingham:There, there's different translations, right? Is-Interviewee:There are, yeah.Nic Fillingham:Which one do you like? Interviewee:It's by Richard Pevear and Larissa Vol- Volokhonsky, I think. I'm probably not pronouncing her last name very well. But yeah, it's, it's a great book. And it's long and you have to flip back to the, the list of character names every five pages or so and every character seems to have five names.Nic Fillingham:(laughs)Natalia Godyla:All the diminutives. Yep. (laughs) Interviewee:Yes, yeah, (laughs) precisely. Nic Fillingham:(laughs) Interviewee:Uh, but it's good. I, I just, it has always stuck with me as a book I really enjoyed. Natalia Godyla:Well, thank you, Madeline, for being on the show. Interviewee:Yeah, it was great to speak with you guys. Thanks for having me. Natalia Godyla:(singing) Well, we had a great time unlocking insights into security. From research to artificial intelligence, keep an eye out for our next episode.Nic Fillingham:And don't forget to tweet us @msftsecurity or email us at securityunlocked@microsoft.com with topics you'd like to hear on a future episode. Until then, stay safe.Natalia Godyla:Stay secure.