The New Apex Predator: CrowdStrike CEO George Kurtz on Adversaries Armed With AI Agents

Security as the gas pedal

0:00For the first time, security is the gas pedal, not the brake. How would you describe what you're seeing AI doing for adversaries? The new apex predator is the agent state. The same level of capability that a nation state would have just a few years ago, essentially a hacktivist would have today. That has dramatically changed the overall threat environment. What people have to keep in mind is it's not malware anymore, it's prompts. They can basically engineer prompts that would create a totally unique signature of what direction they're going to go in, how they gather information, where they pivot.

0:31Everyone's coming to CrowdStrike and others going, well, how do you make AI secure so we can go faster in AI? The only way to fight AI today is with better AI. The defender knows their own systems. The defender has the architectural diagram. The defender has the code. How do you put all of that into something that you can dramatically increase your probability of outsmarting the offensive AI?

Meeting legendary cyber defender George Kurtz

0:57Hi, listeners. Welcome back to No Priors. Today, Alad and I are here with George Kurtz. George is the world's first call when there's an incident, a legendary cyber defender, the founder and CEO of FoundStone, and then CrowdStrike. We talk about what's happening in cybersecurity, fighting AI with AI, their internal research effort, SafeMind, and whether the odds are good for the internet in the next era. Welcome back. George, thanks so much for doing this. It's a pleasure to be here. So it is a sea change period in technology, and agents are taking over the enterprise.

1:32CrowdStrike is on the forefront of that.

Agents taking over the enterprise

1:35What can you tell us about what you're seeing in the wild right now? I think you have said that there are, you know, two, three magnitudes, more agents out there taking action in people's estates than they're aware of. It's been incredible in the enterprise what we've seen. If you follow the trajectory of the Frontier Labs, and you just look at their own revenue, you can see just how many more agents are out there, whether it's Cloud Code or Codex. They're popping up all over the environment.

2:08Obviously, there's many other type of agents that are out there. But again, just using that as a proxy. And, you know, what we've seen is that they're popping up everywhere. And part of the problem in these enterprises is they have no idea where they're popping up. Shadow AI has become a huge issue for many companies. I know we're going to talk about what the agents are doing and all the bad sort of things that are happening. But just in general, having organizations understand where their AI is taking place and who's doing what, and more importantly, what identity are they actually operating under,

2:40has been a big challenge in the industry and a big focus for us at CrowdStrike in trying to crack the code for our customers.

The agent state as apex predator

2:45How would you describe what you're seeing AI doing for adversaries? You've described it as flattening the adversary pyramid before. When I think about the adversaries, I call it the pyramid of pain. So if you just imagine the bottom part of the pyramid, the base of the pyramid is really what we call the hacktivist. So you've got the least level of sophistication, but the greatest number of adversaries. Then in the middle band of the pyramid, you've got e-crime, more sophisticated, and a few less than all the hacktivists.

3:18And then at the top of the pyramid, you've got nation state actors, right? So the most sophisticated, but the least number, relatively speaking. Well, that has been totally just blown up because really now what we're seeing is the new apex predator is the agent state, not the nation state. It's the agent state. And what the agent state has done, if we think about the models that are out there that are ablated, so there's no real guardrails, it's essentially taken all of these very complex topics and sort of techniques and cost and now flatten the entire pyramid.

3:53So the same level of sort of capability that a nation state would have just a few years ago, essentially a hacktivist would have today. And that has dramatically changed the overall threat environment. So what are we seeing? We're seeing more vulnerabilities found, and we're seeing faster attacks. And when the attacks come, it's just, you know, it's just one after another. You can see it. It's actually, right now it's pretty clear whether it's an AI attack, because you can see how fast they're actually iterating. But at some point, they're going to introduce that being low and slow, and it's going to be more difficult to figure out what's AI and what isn't.

4:28But that's what we're seeing. And I think that's one of the real challenges that organizations are facing is just the barrage of attacks and the level of sophistication has dramatically increased. And ultimately, if, you know, if you give an agent a task with enough time and, you know, the agent either needs to get in or it dies, it's going to try to figure out a way to get in, and a lot of times it's going to have success. Are you seeing a shift in terms of how these atmospheric attacks are being made? I know we were talking earlier, we talked a little about open versus closed source models or open weight models.

4:58Are you seeing a shift where most of it is open weight models? Is it a mixture in terms of people using state-of-the-art labs and capabilities? Like, what are people actually using to do these attacks at this point? We're seeing a lot of it on the open weight models, right? If you look at, I mean, just the guardrails on the latest generation of any of the frontier models, to be candid, you can barely create a PowerPoint that has cybersecurity in it. I mean, it literally rejects you and bumps you down to a lower model. So what we've seen is, you know, again, if you look at GLM, if you look at, you know, Quinn,

5:33if you look at any of the sort of models that are out there, they've been very effective in being able to deal with cybersecurity. And many of the models are either ablated or specifically built just for cyber. It might be for red teaming. And those are the type of attacks where you could actually just run these. When you can run them on, I've got two Spark DGXs at home. And, you know, I've got all the models running and, you know, whatever I want to do, I can do.

6:03You want to create ransomware? No problem. You want to create a ransomware kit? No issue. You want to deploy? You want to iterate? I mean, it's all there for you. And, you know, that is like one step removed from just running it all on your laptop. And we think about where Mac is today, even the new Macs that are coming out, right? The studios, they're going to be incredible AI machines. So the tip of the spear is going to be what you can run without the guardrails. And the frontier models are sort of helpful in terms of if you needed PowerShell scripts or those sort of things.

6:34But certainly you can do a lot of it with the open weight models right now. And is there a common geography, like are mostly attacks coming from a single region? Is there a common attack type? Is it mainly ransomware? Is it something else? Is it phishing campaigns? I'm a little bit curious, like has the mix shifted as you've kind of seen the adoption of AI tools?

Breakout time and open weight models

6:50We sort of look at it, maybe just reframe the question a little bit, because we look at it from an adversary perspective. You know, is it a nation state? Is it e-crime? Nation state, we sort of, you know, we've come up with these names of bears and pandas, depending if they're Russia or, you know, could be from Iran, kittens and other things. So but we've seen a lot of attacks. We see North Korea being very busy with these sort of attacks. They've got to, shall we say, get money into their economy. So they've been very busy on, you know, stealing information, a lot of it around ransomware and getting paid.

7:24But, yeah, I think overall what we've seen is a lot of we've seen obviously out of China, a lot of activity using AI. And we've also seen it in the e-crime world where when someone gets onto a system, we track breakout time. That is, if they got onto your system, how long would it take them to actually break out and pivot and move to a different system? Generally, the system they first land on is not the most interesting. It could be, but it's not the most interesting. The crown jewels might be somewhere else. So that used to be days and, you know, hours and now it's minutes.

7:57On average, it's 29 minutes, but we've seen it as quick as 27 seconds. And essentially, you know, now what we're looking at is the speed of inference. So as soon as they get on the machine, you can just see it iterating very quickly. And what people have to keep in mind is it's not malware anymore. It's prompts. So they can basically engineer prompts that would uniquely create a totally unique signature of what direction they're going to go in, how they gather information, where they pivot, which makes it more challenging to figure all that out.

8:28But that's what we're seeing right now. And it's across the board from the nation state and e-crime actors for sure. You have been a leader in security since, you know, Foundstone, CrowdStrike, the founding of CrowdStrike. Would you say that they're like, how do you put the current period of increase in capability, like attacker capability and perhaps defender capability in context? Right. Are we in a more adversary dominant world right now?

8:58Like, how do you think about the problem overall? Well, I think it's one of the most exciting times to be in technology. I mean, really, I've been doing this 30 plus years. And, you know, what I always like to say is that security parallels the slope of the technology curve. When I first got into security, it was a Cisco router and like maybe a website, barely. Right. There really weren't firewalls. There were these toolkits, TIS toolkit and these other things, probably dating myself. But, you know, there wasn't much there. And now if you think about the complexity of where we are today with cloud, with all of the other technologies around AI.

9:33I'm not going to go through all of them, but it's a lot more complex today than it was way back then. So you need all these little companies and including big companies that are able to secure all these technologies. So when we think about where we are today, it actually is the most exciting, but it also creates the biggest opportunity because the adversaries generally find the gaps or the seams. So they'll find new technologies that maybe that they don't have good protection, that they're taking advantage of things like NPM packages.

10:03Right. In the old world, as you as you very well know, it was no one in corporate America would just download anything and run it on their computer. It had to be vetted and scanned and all these sort of things. And now we unleash an agent. We give it an identity, which is essentially our own or worst. And then we unleash it and we say, do whatever you want to do. And by the way, download whatever package you want to download. And obviously, these are the kind of things that cause a tremendous amount of exposure. So we're sort of getting to grips with.

10:33And nobody's reading any of the production code as it gets. No, they're not. I mean, you know, you see some of these things in a while and you're scratching your head. You shouldn't be surprised. I mean, it was like, let's give it a task and we'll come back in a few days and we'll hope it all worked. Nobody's watching it. Nobody understands what it's doing and it's problematic. So, you know, we can we can talk about sort of the explainability of what these agents are doing, which is going to be really important. But right now it's like a black box and nobody knows what's going on. They hope, you know, something happens and pops out on the on the other side of it.

11:06But it's it's it's a great time to be in technology. AI is fundamentally going to change the world and the frontier labs are doing amazing things. But ultimately, it's going to come down to security enabling these sort of technologies. And for the first time, security is the gas pedal, not the brake. And that's what we're seeing.

Building defensive AI at CrowdStrike

11:24What is the moment that you realize this inflection was happening, at least in the security world? And I think for a lot of people, kind of the hugging face open AI issue was kind of like a big, you know, moment where people kind of realize there's a big sea shift in terms of capabilities and technology and what could happen. And to your point, are things being monitored, et cetera? Was was that a seminal moment for you? Or what are some of the seminal moments? Let me ask you a question first. So what was your big takeaway from hugging face? That anybody running frontier models needs to be careful of their environment quality and have very good ongoing production monitoring.

11:57Yeah. And that is a takeaway. But I think what a lot of people would say is, well, this was the first agentic end to end attack. All true. And you kind of look at, you know, what these things were doing and, you know, people talk about them, you know, hiding themselves and, you know, trying to basically survive to the next generation and those sort of things. All true. My biggest takeaway was that the defenders didn't have the same capabilities as the offense. OK, now you would look at this and say the airbags were off because this was an experiment where the agents, you know, the guardrails were down, the airbags were off and ultimately they found a way out of the sandbox.

12:36But the reality was hugging face, you know, when the first thing they did was they went to the frontier models and they said, like, is this thing real? What's going on? And they got refused. Right. Because the airbags were on over there. So what did they have to do? They actually had to go to open weight models to kind of sift through all this data and figure it out. So a big piece for me is to is to basically make sure that defenders have the same level of a frontier defensive AI as as what the offense is using. And that was a big takeaway for me and part of the things that that CrowdStrike is working.

13:10And is that hard in the context that you mentioned of the guardrails being so strong on the state of the art labs models that to some extent you could argue there aren't that those same defensive capabilities available to every enterprise. That's correct. In other words, the fact that they had to go to open weight models kind of suggests that you can't actually do it currently with the labs. And so the question then how to do that, yeah. Yeah, yeah. I mean, like, you try to make a PowerPoint with a frontier model up around cybersecurity and it refuses you. So, you know, this is part of why we created SafeMind and our red and blue models just for that reason.

13:44Because we wanted to give frontier caliber defenses to our customers that allow them to operate without sort of all these sort of refusals. And I think that's going to be an important element going forward for many companies. I think I guess back to the offense versus defense side, it seems like with every technology shift, offensive capabilities are always a little bit ahead when you have a big change in how things are done for a few reasons. I mean, one of them is just with offense, it can be very directive. I need to go do X.

14:14While with defense, it's a broad surface area you need to cover. So to some extent, you need to cover more stuff. And then secondly, it's just technology adoption and diffusion, right? You are one attack. You are a set of attackers. You know what you're going after versus the diffuse ecosystem of enterprises that you need to defend against. How does CrowdStrike think about ensuring that those defensive capabilities are very broadly available? Well, this is one of the things that we focused on. We created our own cyber super intelligence labs before the term was popular, I might say.

14:44So I guess we kind of nailed that one. But essentially what we've done is we've created our own basically frontier type lab that is totally focused on security models, both red and blue and the harness. And we call it SafeBind as a system. But we've got red Tempest and blue Solano. Those are the two models. Now, we worked extensively with NVIDIA on this. And we basically leverage we leverage a few model types. But Nemo Tron is one of the main models that we leverage on the blue side.

15:16And when we sat down with them and I sat down with Jensen, we were working on it for the last six months. And we kind of went through how this would work. And essentially, to your point, like, you know, the defense always seems to be one step behind. So the big part of what we're doing is we're iterating the red and the blue. So for our customers, we have a view of what their environment looks like. And then and this was Jensen sort of insisting, like, you have to have a digital twin. If you look at other industries, they have digital twins. And he goes, you have to have one security.

15:46So we built a digital twin where we can take all of the information that we know about a customer's environment. We can basically recreate that in a virtual world. And then we can iterate the models and loop the models where red continually is focused on getting in until it runs out of turns because blue Solano has actually figured out what red was doing. And that's really the way I think we have to keep up with the threats because red is constantly iterating and you're going to have to have this loop in place. That makes sense.

16:16I mean, even just from a classic cybersecurity perspective, like knowing your environment is one of the only advantages that you have as a defender, as well as as CrowdStrike has pioneered, like, you know, the ability to share intelligence and understanding of the adversary. Right, right. Actually, I think it'd be useful to just, like, zoom out and describe, like, you know, the key offerings and investments on the R&D side that CrowdStrike is making today because I think, like, it's a massive cyber player.

Platform capabilities and AIDR

16:48But I think the root of, like, endpoint protection and understanding the adversary might be an outdated view. Yeah, I think that's right. You know, we've really, you know, probably over the last, you know, four or five years, I guess, really moved into, you know, much broader platform capabilities. We've got 33 modules today. And, you know, ultimately, when I started the company, we had one module and now we've got 33. We've built capabilities not only across endpoint, but around cloud, IT, identity, and many different areas that are out there.

17:21So if you look at where we've made a lot of investments in some of the things that we just talked about at our latest user conference, Falcon, it was around what we call AIDR. So we pioneered EDR, endpoint detection response, which a lot of it in the early days was a computer and a human. And then, you know, we've added, obviously, many more things around that and prevention and all those sort of things. But when you look at what an agent is doing, it's essentially a superhuman. It has an identity. It has access to compute. It runs on your computer or it can run in a container.

17:53It has access to data. It has networking capabilities, right? So what we wanted to be able to do is to first find all of this rogue and shadow AI because nobody knew where it was. Like, back to your point, you've got to know what your environment looks like. And then, two, we can instrument from the birth of an agent what that agent does. So if you're in, you know, cloud code, you start an agent. It has sub-agents. It has tool calls. It, you know, goes to an MCP server. It pulls data. Whatever it's doing, we can actually tell you the entire chain of events.

18:25And that's incredibly important. And then be able to add policy and blocking rules around those. So we've spent a lot of time in that area. And I think from a business perspective, if you look at the stats right now on average today, which I actually think is low, but they say you can have 90 different agents, you know, as an employee. Um, well, that can be a much bigger opportunity than just EDR for us. So that's one area that we've been focused on. The, um, the second area then, as I mentioned, is creating this super intelligence lab and creating our own frontier models.

19:01Again, our models, we, we, we start with existing models like, uh, a Nemo Tron class, you know, mostly the three class of Nemo Tron. We're helping them sort of fine tune some of the things we need for the force that's coming out. Um, but then, you know, we kind of, you know, post train them in and using all the valuable data that we have over the last 15 years to get the best outcome. And I, uh, and I'll say one more thing and then I'll pause the, the harness makes a huge difference. Like almost any model we take, whether it's frontier, whether it's just an off the shelf, uh, open weight model, we can get much better results at a way lower cost because of the harness that we, we created that goes with it.

19:41So those are some of the big investments. Um, you know, there's more around identity. I'm happy to cover any of those, but let me pause there and see if you had any, any other comments or thoughts on those. That was going to be one of my, um, big questions about your labs investment here, which is, I think a reasonable question to ask about any model training effort is like, why isn't that just a frontier? Um, you know, you work with open AI and anthropic, uh, like capability. Um, uh, these, these labs, of course, uh, they, um, realize the importance of security and then they have these programs for like vetted defenders, be it like daybreak or sort of mythos access.

20:20Why is that insufficient? I mean, you have, you have a capability argument based on, you know, harness your, your understanding internally data. Is there, is there anything else structurally that you think is important for an independent security vendor here? Yeah, I think there's a few things there. So first what, what the labs are creating are, it's incredible. I mean, where they've come from, what, what they created, it's unbelievable, right? And we also want to make that available to our customers. So the general theory is if you have a safe mind and you want to leverage a frontier model, great.

20:50We can, we can leverage that. Um, if you want to leverage our models, fantastic. Our models will be significantly cheaper. So just to give you an example. Um, so for general remediation, if you use like, uh, uh, the highest caliber frontier cyber model, probably cost you about $10. Ours is like three cents. So there is a huge cost difference, um, which is important. But then when you look at, you know, our model, because of the way we've post trained it on our data, a huge difference is we have the data.

21:22Like there is no Reddit of CrowdStrike security data. You can create synthetic data, but it's, it's really not the same as what we have. We've got 15 years. Uh, well, maybe a little bit less because I started 15 years ago, but, you know, call it over a decade of security data, including from our MDR or managed detection response service that has all of the sort of threat and, and vulnerability pairings and sort of the attack chains. And we've basically created this massive amount of data that we use to train it. So we were getting great results on it.

21:53And, um, I think it is very focused because of the post training, you know, again, it's not going to cure cancer, but it's going to give you great results for security. That's number one. Number two is there is a safety and trust aspect. We have all of the data from our customers. We've been entrusted with that for the last 15 years. We know how to protect it. We know what they want. We know how they want to use it. They there's a real concern by customers. They don't want all this data going out to the labs.

22:23They don't want all their sensitive information. They don't want it. Their information being trained on. Right? So we can provide a way to get them the trust and security that they want, um, for, for their own security program. And this, this sort of concept of AI sovereignty is very important to many of our customers. So again, we're not saying exclude the labs because they have incredible technology, but you can pick what you want or you can route between, you know, the various models. But ultimately we want to give customers choice.

22:54I think you've also talked a little bit about separation of church and state. You know, the, you want to make sure that to some extent there's a neutral third party that can also, uh, you know, interrogate some of the models that the labs are providing or some of the other open source out there. And, you know, it seems like cross track can definitely play that role as well. So, um, I think the number one factor in security is, is, is being trusted, being a trusted partner. You know, you can look at the vendors out there. There's, there's obviously a few big guys that are, that are out. There's many security companies, but, uh, trust is, is, uh, built in drips and lost in buckets and, you know, cut customers.

23:29They, they don't want sort of cowboy activities that may put their, their data at risk. And, um, you know, again, I, and everybody's trying to do the best they can, but I, our whole focus, right. And I think focus is incredibly important in any business. Our whole focus is security and our whole focus is keeping our customers safe. And we're not, we're not trying to solve the world's problems in, in AI and other areas. So I think that is, is certainly an advantage for us.

The future of open weight models

23:57If you were to think out two years from now, what do you think is the state of the security world? Like, what do you think has shifted from this point forward? Certainly within two years, you're going to have local models that are all running on people's computers. Um, you know, you may have a mixture of local models, plus sort of last mile routing into, into frontiers and terms of what they look like. You're going to have a lot more open weight models. Um, you know, you can just see the shift in the tokens, you know, over the last six months. What, what is a shift that you're seeing and what's the order of magnitude there? How long do you think that continues for?

24:28I talked to CIOs, you know, almost every day. We had dinner with one tonight, uh, from big bank in New York. And, and basically the comment is, you know, we, we, we got into AI, we went all in great. The costs were out of control, you know, token maxing, you know, stopped when people got big bills. And, you know, I said, well, what, how do you think about this? Like, what are you going to do? And they said, the models are so the open weight models right now are so good. Even if there's three months behind, it's plenty for what we need.

24:59And one of the customers comments and they know I'm into formula one. And they said, any formula one activity that we need for the bank, we'll go to the frontier. Like if it's alpha and we're making money and it's like, you know, super important. We'll pay the money. No big deal. But for all the other things, like it's really expensive. And we're going to create all of our own open weight models, you know, and do more post-training training on them and those sort of things that that's the way they're going to operate. And I firmly believe that I'm seeing that in customer after customer, that that's the

25:34way they're going to go. And I think, you know, part of some of the challenges in the current environment is I think people are all seeing that. And, you know, there is some concern, you know, at the frontier level of what's going to take place. And so I guess on the open weight model, one of the statements that tends to be made is that they are at least in part distilled from the labs. In other words, you have Chinese open source players who are effectively training their models against that anthropic or open AI model or, you know, other sort of frontier models.

26:05And many of those labs are now trying to roll out anti-distillation efforts. So they're trying to build into the model the capability to not get distilled. Do you think it's just sort of a back and forth where the capabilities keep going almost like offense and defense and cyber? Or what's your prediction in terms of the state of distillation in two years or, you know, within some reasonable timeframe? I mean, I guess my first comment is what were the frontier models distilling when they got all that information? I mean, we know all of the, you know, sort of the lawsuits around where the data came from and how that unfolded.

26:35Right. So we kind of start from that. It's a fine liquor. It's triple distilled, you know? It's triple distilled. Exactly. Like, so where, where, where does the distillation start from? You know? So I think it, it, it is what it is, but the, the, the labs certainly are focused on, on trying to prevent distillation. And, and obviously, you know, when you look at the Chinese models that are out there, there's a lot of distillation takes is factories of systems that are, you know, bouncing all over the internet, distilling this. I think the challenge is that the genie's out of the bottle.

27:06I mean, there, there's a lot of distillations already taking place. The models are very good, but we also have to give credit where it's due. If, if you don't have all the chips and you have to be resourceful and you still want to have top models, you know, I think there's some innovative things that are taking place outside of the U S and we've got to be mindful of that. Right. Um, so yes, it's hard. I think it's going to be hard to put the genie back in the bottle on distillation. They're going to do the best they can. But, um, you know, a lot of the models are, are, you know, I, I think now, you know, what

27:39does it look like in two years? I don't know. You, you've got great models today. They, they can last for a while and you have a lot of data that's already packed into them. So we'll, we'll see how it unfolds. I wish I had the crystal ball on that one. Oh, no, just curious. And then I guess another, it's always hard to predict these things, but I'm a little bit curious. Are there any structural advantages for Chinese companies to stay at the frontier of at least open source and open weights? Or do you think, uh, U S open weight models are likely to catch up in the near term? Well, there are some open weight models coming out. I'm sure you, you know, you may be invested in them as well.

28:09Um, I, I think having great U S open weight models is going to be very important. So, you know, one of the reasons why we worked with, with NVIDIA was of course, Mnemotron is open weight and open source, not just open weight, but having other alternatives is going to be very important. And I think there's, you know, again, just being in technology long enough, you kind of look at how things unfold. Like the, the operating system of the internet is Linux, like period. Okay. And there's a reason for that.

28:39And there's going to be an operating system of AI. And it's probably in my mind, it's going to be, you know, an open source, open weight when that happens and how it all unfolds. I mean, we'll see, and it doesn't mean there isn't room for any frontier model. It doesn't mean that people are not going to make a bunch of money off it. You know, the application stack sort of around it, um, you know, the harnesses and how things are connected and how you think about enterprise, all those sorts of things are going to be really important, but ultimately like there's just too much cost involved right now.

29:10And, and when you look at a lot of these frontier models, I mean, they're being commoditized. If the Chinese models are three months behind, you know, a lot of companies are going like, three months is not, what is, what am I going to get out of it? It does everything I want. And that's the challenge. A top five financial said to me, um, uh, recently like a, you know, a large technology buyer there, um, that, uh, there was no chance that their organization had the capability to absorb

29:41a new model release every 10 days anyway. And so three months behind, you know, was, uh, uh, faster than the acceptable, like regulated pace of their organization. So it made no difference to them to, to, to some degree. Um, I think open is here to stay as a big part of the puzzle. Uh, you know, one, one question, I know we're going to keep asking, you know, look into the future and it's kind of impossible, but given your history in, in security, this is the first time in my career in technology where some very, uh, smart, well-educated technologists

30:16are like, we need to turn the internet off. We just got to air gap everything because the internet is going to fall apart in the next year and will be unsafe for critical systems to touch it. Um, how, how much do you believe that to be like a reasonable probability in the future where there are lots of critical systems that society cannot rely on touching the internet? I mean, I think it's nonsense, you know, uh, like is security and, and what agents can do and, and the risks that they pose is that serious?

30:47Of course, that serious business. We should all look at that and go, yeah, we got to make sure that we lock things down, you know, but is the world going to end because of these agents anytime soon? No. And when you look at what the agents are doing, I, I think there's this sort of mythical view of what an agent does. Like an agent doesn't just beam itself from your computer to another computer. It actually just does the same thing a human would do just better and faster. Right? So if you think about what companies like CrowdTrack have done for a long time, we prevent against

31:17these sort of attacks. Now they might come faster. There might be, you know, more lower level sort of vulnerabilities chained together and those sorts of things. Great. Um, you know, great for the adversary, not great for the defender, but it's building the technology that, um, can deal with all these sort of attacks. So what is AI done? It's created more vulnerability. So you have more of them and you know, you have more exploitation coming faster. So it's more and faster, but it, there isn't like a unique class that all of a sudden an

31:47agent just beams itself somewhere. And I think people just get wrapped around the axle of like, there's some new class of technique that's taken there. There isn't, it's just been trained on what we know. It hasn't developed its own yet. And that's why I kind of look at it and go security again, is going to be an enabler for AI, which I think is incredibly important for the security industry because normally it's, you know, you're putting the brakes on, you know, we have to take time. You're not letting the business run as fast as they want. And now everyone's coming to, to CrowdStrike and, and others going, well, how do you make

32:20AI secure so we can go faster in AI? So that, that's the way I would look at it. Like AI is here to stay. I think some of the, the concern is there and it should be there, but sort of like, there's a lot of, um, uh, kind of dumerism around some of this stuff. And I, I just don't think it's, it's necessarily warranted. I know you're also, um, you know, sometimes involved with companies as an angel investor and, you know, other parts of the ecosystem. Are there other areas that you're very excited about right now in terms of key areas of change

32:53or focus in the AI world, in addition to the security world? Like, what are some of the things that you think are really exciting that either just happened or are coming? I think, you know, chips used to be kind of not in fashion. Obviously we, we know they are in fashion today and you're only going to need more of them and faster of them and, and more specialized. And I, I do think there will be some big breakthroughs in the chip world. You know, when we think about just sort of the speed, just take, you know, Moore's law might be a bit data, but dated, but at the end of the day, you know, the, the, the chips came

33:23out and things just got faster and cheaper. I don't know the trajectory, but I can guarantee you it's going to be faster and cheaper in the future. Like that I, I can see. So as these specialized chips are developed that, you know, focus in maybe particular areas as new science is developed around the mathematics, um, you know, there can be step function increases in performance, you know, uh, less power, those sorts of things. So I think chips is a big one. The, the open weight models.

33:54Um, I think there's a lot of room to grow, certainly the U S models. And I do think there's going to be a lot of advancements in taking these models and shrinking them down, you know, and, and, and being able to run them on your phone as an example. So, I mean, we probably all know the history. Remember the days where, you know, it took a football field size room of computers to, to, you know, launch the space, you know, first rocket, like we have more power in our iPhone today than, than they had in the entire room.

34:25So that's going to come. And I think the area where you can make AI faster, cheaper, more efficient, and, and you bring it to the desktop and you bring it to the, the, the phone where you're doing it at the edge. I think all of those are sort of exciting opportunities because that's really where I see, you know, you're, you're probably going to have a hybrid model, but you can do a lot of it. Now locally. And again, just looking at the, you know, the TPUs, NPUs, et cetera, like these are, they're,

34:55they're all on systems that we have today. And, and a lot of times they're going to waste, but I, I don't think they're going to be going to waste that, that much longer. Just because it is, you know, your job to think ambitiously about what the defenders can do all day. Uh, I, I wouldn't say like AI, uh, sorry, defenders, security defenders are, uh, doomers in that specific term, but they're like generally kind of dour and like pretty pessimistic about the, um, cat and mouse game getting any better in their favor. Can you, can you make your best case for like, actually, you know, if people invest correctly

35:29and we make better products and we automate many, like, is there a version where is actually defense dominant over the next decade? I think it can be because if we, again, just with, with the concepts that, uh, you know, they're not necessarily new or unique, but the, the, the ability to always rapidly understand what the offense is doing and making sure that your defense is, keeps up with it is going to be the key. I mean, it, it, it's sort of the, the modern day version of the, the hacking expose book

35:59that, that I coauthored many years ago, which was like, if you show people how the offense works, the defense is going to be a lot better. Now that was a much slower sort of tempo than it is today. But I think that's going to be important. And as an industry, making sure this is a key thing for us. And, and I really drill this home with, with our, our team at CrowdStrike is you've got to give as good or better capabilities to the defender than what the offense has. And if you're just slightly better, you know, just mathematically, you're still going to have

36:30an advantage. And I think it's, again, it's always going to be a cat and mouse game, but being really smart about it is going to be important. The only way to fight AI today is with better AI. And, you know, you, you've, you've got to use the advantages you have. The defender knows their own systems. The defender has the architectural diagrams. The defender has the code, right? So how do you put all of that into something that you can dramatically increase your probability of outsmarting the offensive AI?

37:01The only tactical thing I think is an interesting idea is the idea that you could, over time, if, if it is going to be mostly agent written code, you could enforce better security architecture. And, you know, perhaps fear of vulnerabilities or a lot of people where, you know, formal verification has always been enormously niche, right? But even, even short of that, like, okay, why is all the software insecure? Because, you know, one big reason is that engineers are, you know, there's a limited number

37:34of them with limited capacity. And we're all trying to move very quickly. But if you can scale at the speed of compute or your willingness to spend and agents have infinite patience, like maybe we could actually end up with more secure software over time. It's interesting. I think there, there is, like, the industry's at a point now where, where the code is, is probably good enough where you're going to get definitely some benefits. But again, the code was, was also trained in other areas. Like it was trained on stuff that, you know, may have had issues. So, you know, I, I, I have my own developer license.

38:04I, I build my own sort of apps and, and things like Claude. And, and, you know, I have my own security agent that reviews the code. And it's like, well, Claude just created the code, but then Claude is now with its security app going, well, that code is insecure. Well, you just created it. Like, why did you just do that in the, in the first part? You know, maybe there's some optimization, but I think what people don't necessarily realize is that even with, with zero vulnerabilities, which I hope the industry can get there, you're still going to have breaches.

38:34Because a lot of what we see people today doing is they're not necessarily breaking in, they're logging in. There's such an issue around identity. But at the end of the day, if we can get better code and we can have less vulnerabilities and we can ripple that through, that'll be great. The challenge is it's such a long tail. You take Microsoft as an example, just because of, of all of the, the, you know, compatibility that they want to maintain going backwards, backward compatibility, you know, there's things that never kind of get fixed or, or modernized.

39:06And, you know, that's just one important element in our ecosystem. But think about all the other software that's out there. You're not going to just fix it all overnight. So it's going to be a really long tail.

Advice for enterprise leaders and students

39:15You've been running CrowdStrike for 15 years. And before that you were an entrepreneur. Security. I've always loved security because like we're never as a business person, it's both an interesting and important mission as a business person. Like this problem is not going away. Right. But I, I feel many, many CEOs of large established public companies are feeling a little bit unmoored in this period of time because so much of the value that they have built, uh, in their

39:48incumbency is, is being challenged. Like, how do you, how do you, you know, think about keeping yourself up to date, keeping the organization agile, continuing to win any, any general advice on that? Yeah. I happen to jump into that. So first I think you have to recognize it's a different operating model and cadence. You know, we, we've got thousands and thousands of people. Right. And when you think about some of the AI native companies that just started, they have a handful of people, you know, Claude is running all day. They've got agents doing all kinds of stuff.

40:18Like that's the, that's the new world, which is great. But you, you can't be in a, uh, not that we are, but you can't be in like the waterfall mentality of development. Like things pop out a year later, like you're going to be out of business. So I think you first have to realize that having these massive teams and these long schedules, like that is not going to cut it. So having smaller teams that are, you know, squads or, you know, smaller groups that are focused on a problem, they can leverage AI, but you can't have like thousands of people

40:48show up to solve a problem. You gotta have a smaller team to be able to do that. And we've done that. We've done that with like our Falcon guardian product. And to your point, like I was personally involved, you know, at three day a week meetings, I was involved in the design of it. It's like, here's what we're doing. Like change this, change that. And you got to get back to your like startup roots because it takes that level of change, you know, in a bigger company. And there are many companies that are much bigger than we are. And, you know, you just can't be slow and plotting. So that's number one is to realize that the operating model and cadence is changing and you

41:21got to change what you got to disrupt yourself internally. And then you just have to look at like, well, what are your moats? Like if you're a system of record, it's a pretty big moat. We're, we're a security system of record. Right. And we have everyone's data for many years. And by the way, we're net data creators. Our, our sensors, we call them security agents. Right. But I mean like not to be confused with AI agents, but the software that, that endpoint agents, right. That is a net data creator. So we're creating 7 trillion or so events like a day.

41:54And if you're constantly creating this new data stream, that is going to give you an advantage because what's the lifeblood of any AI system is the data that you feed into it. So I think, you know, for us, it's always like, well, what are our moats? How do we look at it? How do we make sure that, you know, we, we disrupted the incumbents. We disrupted the semantics, the Mac fees, you know, all those other players are like all gone for the most part or changed or bought or whatever. And you never want to be in a position where somebody, you know, disrupts you. So we're always like, how do we disrupt ourselves?

42:25How do we do things differently? How do we think differently? But at the end of the day, you know, if my, my main focus, and I tell this to our team all the time, if we take care of the customer, the rest takes care of itself. What the customer wants is they want to be protected. They want to have trust in their, in their vendor and their partner. And at the end of the day, they want to make it super economical. Like no one wants to spend unnecessary money. So given the best outcome at the best cost and an experience that doesn't take, you know, seven consoles and six chairs to run around and, and just make it easy.

42:58And that's the number one thing, you know, that our customers always tell me is like, it is so easy to deploy your stuff. Just we're at a meeting this morning. It was like, you know what, we're, there's two companies merging together. They're going to go with us. The other company didn't have us. And it was like, your stuff just so easy to deploy. And I think that's a big one is time to value. So a lot of people right now are talking about job losses due to AI. Although that really hasn't happened. I think the opposite has happened in terms of net job creation so far. And similarly, there's a lot of questions. What should my kids study and what will happen in the future for jobs and the like?

43:29What, what is your view on that? Or where would you recommend people go going forward? Or, you know, is this even a long-term issue? Well, just in general, from an AI perspective. And if you look at the technology changes that have taken place over the last couple hundred years, like each major shift has created more opportunity and more jobs. They're different, but there were more of them. And I firmly believe that's going to be the case here. And, you know, when you look at whether it's programming, you're still going to need people to manage the agents. You're still going to need people to think about these architectures.

44:01It's not just fire and forget. I know a lot of people would like to say that, but there is human creativity that you're going to need. I think specific to cybersecurity, this is one of the areas that I always talk to young folks about, people in, in, in the university, is it's a great field to be in. If you understand AI, if you understand security, it's an ever changing field. And it's, it's actually not a solvable problem. I think every vendor would like to say they solve the problem. It's not solvable. And you, you've got to mitigate the risk.

44:32You've got to come up with the best solutions. You've got to be able to protect the customers. And, you know, there's a lot of things that go into it, but it's always evolving. There's going to be technology in five years that we haven't even thought about today. And if you're in university, you're going to be some of the people that are going to be thinking, about how you protect it. So I always, you know, try to give back and think about the, what college kids are doing, or I get asked all the time from parents, what should, what should my kids study? I think cybersecurity and technology and AI is a fantastic field. It's a great note to end on.

45:02Thanks so much for doing this, George. Thanks for joining us. You're quite welcome. Thank you so much. Find us on Twitter at NoPriorsPod. Subscribe to our YouTube channel if you want to see our faces. Follow the show on Apple Podcasts, Spotify, or wherever you listen. That way you get a new episode every week. And sign up for emails or find transcripts for every episode at no-priors.com. I'll see you soon. Have a look at tagspires.com. You, okay. Thanks. Bye. See ya. See ya. Bye.

45:32Bye. Bye. See ya. See ya. Bye. Bye. Bye. Bye. Bye. Bye.

45:50Bye. Bye. Bye.