989: Security for Mythos-Era Agentic Risks, with Rubrik’s Anneka Gupta and Cal Al-Dhubaib

25:41Jon Krohn:Yeah, and those agents are famously friendly and helpful as much as they can be. Yes, let me reset that 2FA for you. Well, it's also interesting because if you think about, I'm sure a lot of listeners are Cloud Code users. If you think about using Cloud Code today, especially in enterprise context, I have it set up on my laptop. I go and fetch all of my API keys from all of the SaaS apps that I have access to. I try to give it the longest time period before they're going to revoke and circle my tokens because I don't want to have to go set them up again. I'm giving them the exact same permissions that I as a person have and setting up my tokens that way.

26:27And then I'm sticking those tokens on my laptop and they're sitting in a file on my laptop. So when you look at this whole system and how identity is architected in the agentic world, it's obviously still nascent because all of the tool sets are changing quite a bit. But the security protocols and the way that you should actually be architecting identity, authorization, and all of the other pieces are very, very rudimentary. And a lot of the things that we're all doing today are not things that you would allow when you're building enterprise applications. And so but there's this trade off between like, are you going to cut off people's productivity by saying, hey, they can't do these things?

27:09Or are you going to allow it and have the right monitoring and visibility in place in order to manage it? And of course, organizations want to do the latter. But the tooling hasn't been there in order for them to be able to do that.

27:24Jon Krohn:Really nicely said. There's a term that we haven't said yet that I hear a lot in the cybersecurity space, which is a zero trust world. What does that mean to be in a zero trust world? And what is operating in that zero trust world look like? So zero trust, I would say, is just a fancy word to say, assume breach. Assume that every device, every identity, every account has already been breached. What then? How do you architect a system such that basically for your crown jewels of data, for your crown jewels of infrastructure, the very least number of people have the ability to access and change that?

28:06And in an ideal world, that access is somewhat ephemeral so that there's no permanent access to things that are incredibly important. And that's like what zero trust means. It's been a big term used in cybersecurity more broadly as we thought about cyber attacks. And again, I walked through that anatomy of a cyber attack. It's like if someone compromises my credentials, I should have access to almost nothing. Now, the reality of an enterprise is that you can't have employees that have access to nothing. So you're doing this balanced trade-off of saying, okay, I know I have to give them access, but in that world, how am I gonna respond assuming that that credential or that user has already been breached?

28:48Jon Krohn:Nice, crystal clear. As with all of your explanations today, Annika, thank you. Cal, I've got a question for you that is specific to some talks you've been giving a LinkedIn learning course that you have coming out soon. So you've been developing something called the Trust Engineering Framework. you keynoted on it at something in March that sounds to me like, um, I grew up in Canada and somehow the name of this conference, I'd never heard of it before. It sounds like the name of like the, are the indigenous people that live in the North. It sounds like a town that they would have Irmak. Um, so you're speaking at Irmak in March and, uh, you've got a LinkedIn learning course coming up on this framework.

29:27Jon Krohn:You're keynoting at data summit, 2026 in Boston next Next week after, so the same week that this episode is coming out, right after ODS East in Boston, you're still there. And all of this is centered around this trust engineering framework you've been developing. So help us understand what trust engineering is all about. Yeah, so we have terms like AI governance, responsible AI, AI safety, ML ops. How does trust engineering relate to those? And is there a role called trust engineer? I think that there should be a role called trust engineer. But I'll take a step back and I'll say trust engineering is something that I've been playing around with over the last decade of my career.

30:08And for listeners who didn't catch the last episode, I spent 10 years designing machine learning and AI systems and leading teams that do that in heavily regulated environments. Healthcare, financial services, education, energy, places where there's a high cost of making mistakes and you're already building these models on top of sensitive data assets. and there's a whole set of nuances that you have to deal with. And the big aha for me really isn't that surprising, but it's consistent with the zero trust mindset. It's assume your models are going to mess up. You cannot control them to 100%. And so how then do you operate in the enterprise adopting and designing these AI tools and workflows in a way where you can actually manage the risk?

30:55And so trust engineering is a combination of two different fields. It is the combination of understanding human-centered design as it relates to AI systems. How can you manage user expectations and balance workflows between humans and machines according to level of risk or cost of making mistakes? And then you're pairing that human-centered design approach with the ability to configure the appropriate trust infrastructure and trust assurance. And think of that as your governance and human-led architecture of the trust infrastructure you have in place. And trust engineering is a framework of how do you unify all this?

31:35Because you can't just bolt on AI risk management after the fact. It has to start at the front. And I'll give you a very simple example. if anyone's been to San Francisco recently you've probably come across a Waymo and it can feel like a game of chicken when you're trying to go across the crosswalk does it see me is it gonna mow me down is it safe and I'm a runner every time in San Francisco I take a run along the Embarcadero and there's Waymo's galore they have this new feature that I actually really love as you're crossing the street a little cross man appears on that little circular cone on top of the Waymo And it's a clear signal saying, hi, human, I see you.

32:14It's now safe to walk. And I love that because that's such a great example of human-centered design and expectation management around an AI system that could otherwise cause meaningful harm. So trust engineering is unifying that with the trust infrastructure. Happy to nerd out a little bit more about that. But having the building blocks in place that allow you to determine what potential risks exist up front, to monitor the system and detect when it's misbehaving or behaving not according to your expectations. And then three, when a mistake does happen, what's your failback? What's your fall safe?

32:48And so it really is trust engineering is building off of what Anika so eloquently described as the zero trust mindset, but applied to AI systems. What I love about the trust engineering framework is that it's really bringing together people, process and technology, which is the framework that we always think about as business leaders as we're driving change throughout the organization. And it's only in thinking about all of these three holistically together that we can actually truly solve a problem. Because if you only solve for one piece, you're only solving for one leg of the stool. And you're not going to be able to actually build these products and processes in a way that are truly achieving the end means that you're trying to achieve.

33:32Jon Krohn:I mean, I've complimented you a bunch of times, Anika, on your speaking ability, but I think Cal did a pretty good job there, too. I think I've been too hard on him by joking that he's only been so busy for three weeks because I forget that the whole time running Pandita and then it further, he's doing AI consulting in industries like healthcare, like finance, where security is paramount. That's why we brought him onto the team. Well, I mean, you know, we were nerding a little bit out of a little bit about this before the call, but there's almost like this crossover moment happening where AI risk management is kind of reaching over into cybersecurity and I'm seeing the birth of a new discipline.

34:12And so I'm not a cybersecurity expert yet. I'm working on that, but I can already see where you can't really have one of these now without the other. So we're seeing the birth of a new breed here. It's so true. Even when we're going and having conversations around AI and security and operations of AI with large enterprises, it's really interesting to see that the people we're talking to, sometimes it's the CIO and their organization. Sometimes it's the CTO. Sometimes it's the CISO. Sometimes it's all three of them. Sometimes it's some chief data officer or chief AI officer. And the reality is, is that the lines are blurring because the problem space crosses all of these different disciplines.

34:56And it's not like you can just solve again for one discipline or the other. You have to think about it holistically because any sort of risk management you put in place could also potentially come at the expense of productivity. So how do you manage both of those together in tandem? them.

35:11Jon Krohn:Well, yeah, this does have me thinking I should be more concerned about security than I have been before this episode. There's probably a lot of listeners out there thinking about, you know, because you kind of, you know, I've gotten this far without any major issues personally or professionally. And so you kind of think, ah, it's going to be fine. But then when I'm using cloud code and I don't even know, you know, I'm not even really a software developer and I'm using cloud code to create production code, I should definitely be really on the ball with cybersecurity concerns. I mean, that's exactly why we're doing this.

35:46It's kind of like we're not trying to be like the naysayers, but this is like ringing the alarm bells. We really need to start thinking about AI adoption differently. And resilience has to be a part of the conversation. And it has to be a part of the conversation up front. If you look at another revolution that happen in technology is the cloud revolution, public cloud, people moving to the public cloud. And a lot of, you know, when we've talked to organizations about cyber resilience, they've thought about bolting on cyber resilience after they've already made the move instead of up front. And that means that that level of effort to do so is so much higher.

36:21With AI, we can't afford to bolt it on after the fact. We have to do it up front. Otherwise, things are going to happen that are very bad. And that is that's super important for people to recognize.

36:33Jon Krohn:Right. Yeah. You've been speaking a lot in the past few minutes, both of you, about this intersection between AI and cybersecurity. I also, in my last question to Cal, I asked him, you know, I built on some things that he'd been doing at conferences. And so you, Annika, in the same way that Cal has come from AI risk into cybersecurity, you come from this long cybersecurity background, increasingly into AI, to wit, at RSAC, which is the biggest security conference. It was held in March, I think in the Bay Area. And at that conference, at RSAC, you announced something called SAGE, the Semantic AI Governance Engine, which built on a custom small language model, or SLM for short.

37:18Jon Krohn:We don't need any sharding on this podcast.

37:23And I don't think that's a word we even have to bleep out.

37:28Jon Krohn:It's just a fun one. But yes, a small language model at SLM called Sage, Semantic AI Governance Engine. And so you published a head-to-head between GPT 5.2, where your SLM processed messages five times faster, which shouldn't be surprising given that it's such a small model relative to GPT 5.2, but it was also higher accuracy on policy violation detection. So tell us more about this. Why did you go with a custom trained SLM rather than maybe fine tuning or distilling a frontier model? Yeah. So let's take a step back and talk about how we're approaching AI governance. When we think about the challenges that organizations are having with as they deploy AI and as they deploy, try to deploy AI safely, there are really three pillars of what we're trying to solve for.

38:21The first one is lack of visibility. I shared this stat before, like over 80 % of organizations feel like they lack visibility into what agents are running in their environment, what are those agents doing, what actions are they taking, et cetera. So that's the first pillar is you have to have that visibility. The second pillar is governance and control. So agents are going to be taking actions all the time. You need to be able to block the actions that you don't want it to take. You need to be able to monitor for high risk actions. Now, the challenge with agents is different than other traditional enterprise systems is that agents and models are inherently non-deterministic.

39:01You don't know what they're going to do. And therefore, you can't use a simple rule engine to actually monitor these agents. You have to use AI agents to govern the agents. And so that's where Sage comes in. And what we did is we fine-tuned an SLM because when we're talking about this policy enforcement layer, looking at all of the activity and all of the calls that are happening to the model and the responses back and being able to block it, there are three factors that we have to solve for. The first one is performance. Obviously, accuracy is super, super important. If we're not able to do this accurately, we can't build that trust with our customers that we're actually solving their problems for them.

39:47The second is cost. When we're talking about running this, there is a real cost associated with it. By running a fine-tuned SLM, we can manage the cost. And the third is latency, and that the response time has to be super fast. if we're going to block an action, we can't wait five seconds in order to decide whether to do that, especially when an agent may be chaining together so many multiple actions. And that would increase the response time, which would be untenable for the user. So when we take cost, performance, and latency together, we recognize the best way to solve this problem was by fine-tuning a small language model.

40:27And about a year ago, we acquired a company called Predibase that specialized in this area. At the time, we actually didn't know that it was going to evolve into this area or into what it is today as part of Rubric Agent Cloud and Sage. But we acquired them knowing that, hey, we have a lot of contacts into data and into identity, but we don't understand the models well enough. We don't understand fine tuning. And we recognize that bringing all these three together could be a magic combination. So now that's providing the foundation for SAGE, which is our governance pillar. And then the last piece is remediation, which is what happens when your policy doesn't catch everything?

41:09What happens when an agent inevitably makes a mistake? How do you actually rewind those actions? And that's agent rewind, where we're able to actually undo the malicious agent actions and restore your systems back to normal.

41:23Jon Krohn:Wow. There was a term in there that you kind of glossed over that sounded really exciting to me and I need to hear more about, which is I think you described it as the rubric agent cloud. Mm hmm. What is that? So Rubric Agent Cloud is the platform that we are selling for AI operations and security. So it has these three pillars of being able to give you complete visibility into your agents, whether those agents are Microsoft Copilot agents, AWS Bedrock agents, Cloud Code agents, whatever agents you have, it's giving you that holistic visibility. is giving you the governance and control with Sage where you can define a policy in natural language saying like, hey, I don't want my agents giving financial advice or I don't want them sending emails out to my customers.

42:11However, you wanna express those policies and then Sage will help do the enforcement of those policies at runtime. And then there's the agent rewind piece which allows you to rewind any agent actions that were unintentional in your system to get your system back up and running. So that whole platform is what we call Rubric Agent Cloud. It's been really fun learning about this. And just to kind of like why I'm so excited about it. This is like, you know, new halo effect. But it's like the e-discovery of information assets but applied to AI agents. And so it's not just like, hey, manually add everything in here.

42:46But it's like, hey, discover every single agent that your users and human identities have spun off acting on their behalf. and then across all of them, it proactively checks permissions and policy violations that could exist. And so you kind of get a posture up front and nothing pops eyeballs more than showing a dashboard of like, hey, did you actually know that you have like X hundred number of agents and oh, by the way, these 50 of them have read write access to these four databases. Are you cool with that? Do you want that to happen?

43:17Jon Krohn:Yeah. Another great example and another great reason why I can see why you're so excited to be at Rubrical. There was rewinding a moment back to what Annika was talking about. I'm going to get into something called Agent Rewind. But before we get into Agent Rewind, there's something that I want to highlight really quickly, which is that, Annika, when you were reeling off kind of mainstream agents that people might be using, I think it's so interesting and telling that you listed a Microsoft one, You listed AWS, Anthropic. Opening AI didn't even come up, which is just two years ago. Who would have thought?

43:54Jon Krohn:And obviously, they are still big players in this space. But you can reel off three of the top agentic platforms or tools that people might be using and not mention them without somebody batting an eyelid. Yeah. Interesting. I think it just goes to show how fast the landscape is changing. And a year from now, who knows who we'll be talking about, right? It's always easy to say, extrapolate, oh, the people that are winning today are the ones that are going to win a year from now. I think with AI, that's less certain than ever before in the history of technology, which is both exciting and incredibly scary at the same time.

44:34Well, it's been interesting to watch and see who is winning the race on enterprise adoption. And so these companies are also clearly aligning themselves around whether this is for workplace tools or it's more consumer focused. And so I think in the next couple of years to come, we'll start to see even more of that differentiation and specialization. And I'd be willing to bet just based off of some of the product news these companies are making, we'll see ones that start to align around very specific industries.

45:01Jon Krohn:It makes so much sense. Yeah, right now the big players that we think of are diversified across all kinds of enterprises. is, but it's such a fast-growing space that it can't stay like that forever. So yeah, now rewinding to Agent Rewind, much like the rubric Agent Cloud that really caught my attention. This Agent Rewind functionality sounds really interesting to me, and it's one of the more conceptually interesting things. I think you're shipping, you know, of all the interesting things you're doing, that idea of being able to undo what AI agents have done, it sounds, you know, almost counter intuitive that this is possible because agents do things that touch the world.

45:42Jon Krohn:They send emails, execute API calls against third-party software platforms. They write to databases that you don't own. They could even place trades in some cases. So what kinds of things can, like, are there things that can't be rewound, rewinded? I'm not exactly sure what the past one is. Yes. It's a great question. So when we think about Agent Rewind, it really harkens back to our legacy and our strength in cyber recovery and being able to recover different kinds of systems. So where does Agent Rewind work great and where can we rewind actions? Okay, you dropped a production database table.

46:20We can bring back that production database table. You changed something in Salesforce that broke the relationship between your opportunities and accounts in Salesforce. far as, okay, we can fix that for you. You made some change to your identity systems that gave people a bunch of access that they shouldn't have. We can fix that. So those are the kinds of actions that we're able to rewind. Now, if you've sent an email to a customer and it's already arrived at the customer, there's not really too much that we can do. We can do about those kinds of situations and those will require, and that's where we think the layer of defense with Sage is really the right approach of saying, hey, define upfront some of those high risk activities.

47:05Like I don't want it to be making, I don't want agents to be making trades for me. I don't want it to be sending emails to my customers. Define those things, those kinds of behaviors upfront because the best way to prevent those is to actually be at runtime, looking at what is actually, the agent's actually doing it and stopping there. But now if you talk about, hey, a agent is making a change to a system, which is actually really hard to put at runtime because a lot of changes are legitimate and only some changes are illegitimate. And you need a lot more context to understand what was legitimate and not legitimate.

47:41And that's hard to do at runtime. So that's where Agent Rewind is going to be important. And when I think about like Rubric agent cloud and overall like enterprise strategies overall, we need to think about the multiple layers of defense. And it is four or five layers of defense that are really going to protect your organization. You can kind of think of it as like, you're going to layer like different Swiss cheese layers together. And hopefully, the combination of all of your Swiss cheese slices means that you actually have a block that isn't, there aren't any holes in. That's the approach that one has to take in this AI world.

48:22Jon Krohn:I love that. We're going to have to turn that into an animated short. Look out for that on YouTube because that is such a fun analogy with the Swiss cheese. I love it. Fantastic. Yeah. So I think I understand Agent Rewind. It makes perfect sense to me. I'd like some kind of guarantee that if an agent sends an email that it wasn't supposed to send and my client has read it, that Rubric will personally guarantee that Will Smith and Tommy Lee Jones will show up at their home and erase their memory. I'll put that on the roadmap. Yeah, exactly. Perfect. Perfect. I haven't seen Tommy Lee Jones in much lately.

48:59Jon Krohn:He's probably looking for something to do. But all joking aside, it seems like this Agent Rewind ends up being able to be effective in far more use cases than we might imagine because of the way that rubric has its preemptive recovery engine. Is that right? Yes. We're able to, I mean, the basic thing is that you have to be able to trace what are the activities that the, that happened, what were the changes that actually happened to the data. So the same preemptive recovery engine that we're using to trace what happens during a cyber attack, we're using as well with agent Rewind. Fantastic. All right.

49:37Jon Krohn:So now getting into my final technical question for both of you, for all of our data scientists and ML engineers and software developers listening, many of whom will be asked in the future to either evaluate AI-assisted security tooling or harden their own AI systems. Someone's going to knock on their door or slack them and say, you know, it's great that you're using cloud code or these other kinds of tools to generate so much code, but what are we doing about security? So how do our listeners evaluate AI models today when we know that benchmarks have so many inherent issues? This is such a great question.

50:19There's a paper that came out earlier this year that was looking at whether or not, and it's a very interesting design, whether or not you could actually train a large language model to pass a benchmark test while still failing every single real world test case. And so they actually effectively proved that you can train a model in such a way that it's so good at gamifying the exam that actually hides how terrible it really is. And so that signaled to me from the market is we've reached the point where benchmarks, while useful, are not sufficient and they're only one frame of reference. And so there's some great websites you can dig into, model evaluation, like artificial analysis that look at this by combining multiple different benchmarks, but they're very hard to read or hard to trust given some of these limitations with benchmarks.

51:14What I find missing is there's actually not a lot of benchmarks out there that focus on the cyber resilience or safety scoring of these models as applied to test cases. And I can track down the name of this one resource, but there's this one benchmark hub that I've liked, and it's Trust Hub or Trust LM Hub. And what they do is they actually run jailbreaking attempts on each of the models. And so in addition to saying, you know, this is how it's doing on MMLU, for example, it'll show you this has a 99 % success rate at blocking jailbreaking attempts. And so my call to action here is we really need to think about more standards of how we evaluate the safety of these models when operating with enterprise environments measured against actions like over-permissioning, exhaustively searching for information and data assets that are out of scope, And so we need new tools and resources out there to think about models in this context.

52:19And so I have less of a solution here and more of a call to action.

52:22Jon Krohn:Oh, makes sense. And yeah, some great ideas there around looking beyond benchmarks, including in the security space, when we're thinking about what kinds of AI models we should be using. The jailbreaking point that you made there, we could do a whole episode on for sure. Absolutely. Final question for both of you. I tend to be long-winded with my questions, but this one is going to be quite terse, which is simply, what questions should we be asking in a zero-trust world? I don't know. Whoever wants to go first. Yeah, sure. I mean, I think one is like, how do I get visibility into my AI agents and what are they doing within my organization?

53:04How do I have the right governance in place to be able to control what these agents can't and can't do? And how am I going to respond and recover when AI agents inevitably make mistakes? Listen, I know there's a lot of ML engineers and data scientists and engineers on the call or watching this. I know there's a lot of engineers watching this. I am sure many of you are having this struggle with your AI governance teams where they're saying, hey, I have this risk that I'm concerned about. What happens if this person uses your application in this way and is able to get access to this information or do this thing?

53:46That's like in the nature of security professionals. And I think as an engineer, what you can do is you can go ask the question back, well, what do you need to see to feel more confident that you have the visibility that you need to monitor what is happening in our system? What do you need to do? Like, what are the kinds of policies that you need to enforce in order to, again, feel comfortable that the highest risk areas are being managed appropriately and blocked appropriately. And then what kinds of resilience strategies you need in place to ensure that there's business uptime. And I think by asking those questions back, you can have a better discussion versus being in this mode where you're like, well, OK, I don't know how to answer that question.

54:31You tell me. You have to be able to progress that conversation forward. And that's where I see a lot of organizations getting stuck today is in this deadlock of security has all these risks. Engineers are like, I need to build this thing. And they aren't able to come together and solve. I am so 1 ,000 % aligned. And the only thing that I would even add to that is just further articulating this enablement and literacy as a part of what enterprises need to do. And nobody loves sitting through their annual privacy training. And I actually just finished my annual cyber training in my last month at Further before doing my new annual cyber training when joining the rubric.

55:10So I am especially burnt out by the excessive privacy and data security training when that's what I teach to many people. But we sit through it and it really gives you this sense of obligation. This is what can go wrong when you take these actions. This is why your duty matters. Taking these actions could have an impact on our customers, our reputation, your job. And so we do a great job at that from a data security and information protection perspective at the enterprise level. I don't yet see widespread AI literacy training that brings that same level of awareness and understanding of what your obligation is as a worker who now has access to these tools and truly the impact of your larger blast radius.

55:54And the second thing I would add to that is really acknowledging that we need cross-functional professionals. I was so thrilled to learn at Rubrik that we actually have a dedicated AI attorney. And this is an individual who really started off in that position of like asking a lot of questions like, hey, what's our risk exposure? How are we dealing with this? Then he started attending conferences that really focused on upskilling and meeting other lawyers that are actually dealing with the same issue. And I think the same is going to be true. You're going to have attorneys that need to make that crossover.

56:27You need our listeners here who are primarily data scientists and engineers that need to start getting better versed in risk management language. And I think the evolution of knowledge work in the enterprise that's AI enabled is by default cross-functional.

56:42Jon Krohn:I like how it sounded like you were going to add something really short onto the end there, but we got a really comprehensive answer anyway, but it was valuable. I'm not, I, I don't regret that you went into that detailed response. Um, so this has been a fantastic episode. I really enjoyed having you back on the show, Cal, obviously, and Anika, my goodness, so good. Uh, loved having you on for your first appearance and hopefully not your last. um i don't know cal may have prepared you for this i was actually supposed to prepare you for this before we started recording so i'm gonna i'm gonna pick on cal something that i always ask my guests is for a book recommendation and so cal should have been prepared i'm ready i'm ready so cal you go first and that gives out a little some time in case you needed it i am i i this just got launched and so there's a big plug to reed blackman um but his new book the ethical AI nightmare challenge.

57:41And it is waiting for me at home as soon as I get back. But it's this premise of our measures to manage AI risk are outdated. And it's like we had house cats with old models of AI and we have house cat manuals. And now we have tigers in our house and we're trying to use this house cat manual and update it. And that's just not working. So it's a totally different way about thinking about managing the emergent risk of AI systems.

58:05Jon Krohn:Great analogy. The second best in this episode after the Swiss cheese. I'm going to give like a sci-fi fantasy recommendation because that's mostly what I read these days. Love it. I am going to recommend Dungeon Crawler Carl, which is a seven book series. Eighth book is coming out in May. And there's a fun rogue AI in there. So that's the connection back to AI. But highly recommend it. It's very entertaining if you need something that's lighthearted and is it going to cause you stress? That sounds so great. I think my book might cause some stress. And it's nonfiction is the worst part of yours, Gal.

58:43Jon Krohn:It's stressful and nonfiction. So it's like real world stress. You can't escape it when you put it down. Nice. Thank you for that, Annika. I really do need something like that. And it's so hard to find. Nice. All right. And then the very last thing is how should people follow you after the episode? Gal, you can go first. I am very active on LinkedIn. And if you are excited by trust engineering, you will get no shortage of content. But if any of this was interesting, please reach out. I love to chat. Nice. And Annika? Same. You can find me on LinkedIn. I'm there all the time. Feel free to DM me if you have questions or if you want to chat.

59:22And thanks for listening.

59:24Jon Krohn:Fantastic. Yes. Thank you both so much for taking the time out of your very busy schedules while traveling to get this breaking news out at this very important time. Thank you so much. and hopefully we'll have you on again sometime soon. Thank you. Thanks for having us. Wow, I sure loved that episode. I trust you did too. In it, Anika Gupta and Cal Aldubabe covered how Anthropic's mythos model can be pointed at an open source code repo and autonomously surface every vulnerability inside it and how Anthropic themselves estimate mythos class capabilities will reach other labs within six to 18 months with open-weight versions likely to follow.

1:00:00Jon Krohn:We talked about how Rubrik's Agent Cloud delivers three pillars of resilience in this new agentic AI era, visibility into every agent in your environment, governance and runtime control through the SAGE small language model, and remediation through Agent Rewind. We also talked about why the next wave of knowledge work is inherently cross-functional with AI attorneys, security pros, and data scientists all needing shared literacy in AI risk. As always, you can get all those show notes, including the transcript for this episode, the video recording, any materials we mentioned on the show, the URLs for Annika and Cal's social media profiles, as well as my own at superdatascience.com slash 989.

1:00:43Jon Krohn:Thanks, of course, to everyone on the Super Data Science podcast team. Our podcast manager, Sonja Breivich, media editor, Mario Pombo, partnerships manager, Natalie Zajski, researcher, Serge Bassis, writer, Dr. Zahra Karche, and our founder, Kirill Aramanco. Thanks to all of them for producing another stellar episode for us today for enabling that super team to create this free podcast for you. We are deeply grateful to our sponsors. You can support the show by checking out our sponsors links, which are in the show notes. And if you'd ever like to sponsor the show, you can get the details on how by making your way to johnkrone.com slash podcast.

1:01:18Jon Krohn:Otherwise, please help us out by sharing this podcast with folks that would like to listen to it and learn all about cybersecurity and AI. Review the podcast on your favorite podcasting app or on YouTube. Subscribe, obviously, if you're not already a subscriber. But most importantly, I hope you'll just keep on tuning in. I'm so grateful to have you listening, and I hope I can continue to make episodes you love for years and years to come. Until next time, keep on rocking it out there. And I'm looking forward to enjoying another round of the Super Data Science Podcast with you very soon.