Ankur Shah: [00:00:00] So when a user asks the question, or if the AI is carried out a goal, we'll first do what's called RAG retrieval. Yeah. So then we say AI don't be all over the place. Only answer the question that I retrieved in the from the RAG system.

Ashish Rajan: Yeah. Yeah. So

Ankur Shah: that's how we are to as an industry tame a lot of this hallucination type of problem.

Ashish Rajan: So your data would be in the RAG or would it be the context? Welcome to another episode of Cloud Security Podcast, I've got Ankur here.. Thanks for coming to the show, man.

Ankur Shah: Yeah, it's good to be here. This is where all the great stuff happens.

You guys have done amazing and happy to be here.

Ashish Rajan: Awesome. Actually, you and I have known each other some time, but for the audience, could you share a bit about yourself? What have you been up to a bit of intro about yourself in the cybersecurity space that you've been working in?

Ankur Shah: Yeah. Look I've been a builder for last 25 years.

Very early on in my career, I started working for a voiceover IP startup. Oh, okay. And at the same time back then e-commerce was just starting to heat up. So with my brother, I started an e-commerce company for expat ourselves. Okay. Sending gifts to India. This is way, way before [00:01:00] Flipkart.

Oh, wow. That was more of a passion project. Did well, but not enough to pay the bill. Oh, fair. Lucky for me, the voiceover IP startup that I had Cisco acquired that company. Yeah. And I really learned zero to scale, learned a lot about business, and over the last couple decades now, I've been super fortunate to work for five startups, five big companies, zero to scale is my, my thing. Yeah. Yeah. Love building. Every time a technology change happens, I'd like to get dirty.

Ashish Rajan: Yeah.

Ankur Shah: And look, last eight years were amazing at Palo Alto, Redlock, Prisma Cloud, zero to hundreds of millions of dollars of business. Yeah. And I made a ton of mistake in my career, trust me.

But the one thing I learned was, kind attaching myself to big mega trend. Yeah. I don't know what to do. Don't know how to do it, but just attach yourself, you'll figure out. Figure it out. Yeah. And that's why I started Straiker. AI is a obviously the thing now. Yeah. And we wanted to secure the future with AI and that's why we started Staiker.

Ashish Rajan: And maybe, and it's a good conversation, lead into a lot of people have an understanding of [00:02:00] how AI is being used and implemented and. But then how much of it is reality versus what the truth is? It'll be good to maybe level the playing field a bit by, if you could share a few examples on how do you see AI deployments across organizations, some of the customers you may be talking to as well.

Ankur Shah: Yeah. Look, this is one of the most difficult part because you're in Silicon Valley. Yeah. So it's easy to get caught up in all the hype cycle. But we've talked to hundreds of customers and have a pretty good, well-grounded perspective on where enterprises are really as it relates to AI. But before I go into that, I think it's important to understand what's really happening at this moment in time with AI and at least share what I think. See over the years with software the level of abstraction increases and that makes it easier for builders to build software and consumers to consume applications with cloud. Again, it cloud Kubernetes, container, CICD. It made it super easy for developers to build application, deploy it anywhere.

And for consumers to consume it.

Ashish Rajan: Yeah.

Ankur Shah: I think AI adds one more [00:03:00] level of abstraction, unlike anything we have seen before. Basically you can use English or natural language to build and consume software. Yeah. That paradigm has never happened.

Ashish Rajan: Yeah.

Ankur Shah: So what we're gonna see is an explosion of applications.

Anybody can build anything. And I'll tell you a quick story detour. My wife loves lotions, organic lotions. So I'm like, honey, like I feel like just building an e-commerce app for you. What do you mean? So I just opened, cracked open Replit and with few shots in prompt I build a full blown application, not just a code.

Oh, wow. A fully deployed application. Oh, wow. So we are gonna see increasingly that accelerate anybody can build and consume these applications using natural language interfaces. Now the reality in the enterprise is it, the adoption is happening quickly. But not as fast as Silicon Valley would like to think it is.

Okay. So what you are seeing is now coding copilot does now have a proven ROI. So a lot of enterprises are embracing it, like our, ourselves at Staiker, like we're seeing anywhere from 50 to a hundred percent productivity improvement. Yeah. So that's real. Call center, customer support chat [00:04:00] bots, that's like really up there.

Now the thing that hasn't happened yet, which is the next level, which is right now, AI is acting as an assistant. That's right. But the whole agentic stuff is still in the early days. It's not taking action. Because we're dealing with non-deterministic systems.

Ashish Rajan: Yeah.

Ankur Shah: But it's gonna happen.

It's gonna happen very quickly. So you gotta be in the game. So ultimately look, few years from now, we don't know if it is gonna be 1, 2, 5 years from now. What you can build is gonna be not a function of the strength of your engineering organization. Okay. But the size of your imagination because you can build anything now.

Yeah. So it's truly the best of times. Yeah. Yeah. But it just, we have to remind ourselves it's gonna take a while for the whole thing to play out.

Ashish Rajan: Interesting. You're finding a I'm gonna subscribe to the lotion store 'cause I feel like my wife is going be interested in lotion store, but b, coming back to the AI agent workflow because.

It's almost top of mind for everyone right now. Yeah. And I'm glad you clarified the fact that we're still in early stages. We're still trying to figure out MCP, A2A There's a [00:05:00] whole acronym flood there as well. A lot of organizations, especially enterprise, have been traditional for a long time.

Yeah. And by traditional, I don't mean like they are just on premise. A lot of them are multi-cloud, cloud provided. Yeah. They have legacy applications now AI is being attached to that. As well. Yeah. In terms of, is the AI enabled application as they call them? Yeah. What is I guess in the rollout, you're at least you're seeing with your customers with the copilot pieces as well as with now SaaS application, also having AI agents in there as well.

What's the change that is being seen and why isn't traditional security enough for that?

Ankur Shah: Yeah. So look, before we talk about security, like I think it's important to understand the application stack itself, right? Yeah. And I posted on LinkedIn about this look, right now bulk of the SaaS AI apps are junk.

They've just bolted on a chat bot on top of their gunky old school stuff and then calling it a day. But the true innovation is coming from a lot of AI native applications. Cursor is like one of the [00:06:00] many examples. Glean is of course another one where the entire stack is getting changed.

So the infrastructure layer, to your point has remained the same. So you're still using a Hyperscaler computer has changed for sure, but you have a completely new data layer, vector databases, RAG systems. You have a model inference layer. You have prompting layer. Yeah. And most importantly, and fundamentally and this has never happened in the application, is that now you have an unstructured request response pattern in the application.

Ashish Rajan: Yeah.

Ankur Shah: Yeah. So if you're building an e-commerce application, you can say a list product, add to cart, checkout. That's right. Very structured Request. Response paradigm. Yeah. Yeah. Now you actually have to give AI a task or a goal. So it's a completely unstructured request, a response paradigm. So when the application stack changes, just like it happened in cloud and containers in Kubernetes, you have to completely rethink the security stack.

Ashish Rajan: Yeah.

Ankur Shah: That's the mindset that a lot of enterprises and security practice and starting to think about. It's not just like understanding language portion and reigning [00:07:00] your applications against security threats with languages. But you have to think about the entire security of the application and agentic layers.

Ashish Rajan: Cause a lot of people obviously believe that the legacy applications we have, we wanna make them AI enabled. Yeah. Banks and stuff have been running for we still have mainframe there. Yeah. After all these years. Do you find that the change that is currently going on with AI the drift between an AI native first application versus a existing SaaS application telling you that I have agents and use it tool, do whatever you want, tell it to create a Ghibli image or whatever.

Yeah. The difference between the two is quite stark and in an enterprise context these days, people are looking at co-pilot usage to what you call that as well.

Yeah. Now co-pilot itself has an agent. Yeah. There's other agent. There seems to be like so many agents across the board. Are you finding that the application stack that has been built, the current stage of it where we are not at that age, to your point, yeah. We are not at that stage where AI agents everywhere.

Yeah. But we are at the stage where AI is being used quite a bit. Correct. Our legacy application is not the first cab of the [00:08:00] rank for AI. I

Ankur Shah: look, I think the way I put the different enterprises in different buckets. There're AI natives Yeah. Who are building, who are using LLMs to write business logic and they write the software code that surrounds it.

They start with conversational UI task giving as the first nature of business. See if you build a CRM today. And I use a CRM tool. Yeah. Name shall not be named. I'll start with a completely different paradigm. It starts with a conversation, meeting analytics on that prospect, LLMs are really good at that.

And you build a completely different, radically new new interface. So that's what AI natives are doing. The AI immigrants, which is the second cohort Yeah. Which is the traditional SaaS applications are quickly trying to figure out. What do I need to do so that the natives don't disrupt me?

Yeah. So the, their first act was just to bolt on a chat bot. Yeah. On top of an existing app. Because it's really difficult. You have thousands of customers and I feel for them. I've been in big companies, I've done, built SaaS products, so it's difficult. And then you have AI explorers who are like really pretending to be AI first but they're trying really hard.

So that's what's [00:09:00] happening right now. The opportunity is for immigrants to quickly become AI native, either by building a completely separate app or just radically transform applications, were built the UIs of the world were built. Yeah. To get a job done. Yeah. Like people don't like to click on bunch of UI items because they enjoy that.

Yeah. Yeah. The purpose of the whole thing was to get the job done in case of A CRM is to close a deal. Yes. Correct. Yes. Now you can give that intent. Now you can give that task. Now you can give that goal to AI and let it figure out how best to do it. Oh. So we have to completely rethink how apps are built.

Yeah. And that's how I really put these customers in different buckets and what's happening today.

Ashish Rajan: Yeah. I think it made me instantly think because you come from a cloud world Yeah. Where you were Redlock. And it's funny, it's a similar conversation a lot of us are having around the cloud native and on-premise being bandaged to be Yes.

We are all cloud native as well. Yeah. Yeah. It sounds like it's a similar movement here as well, where, but then this is probably happening a lot more quicker, where a lot of people [00:10:00] who have already have applications that may have not been migrated to AI native. Correct. They'll eventually become AI native as happened with cloud.

So as what happened with on-premise to cloud. Cloud native. The lift and shift and all that happened. Correct. Similar movement you're seeing in this as well.

Ankur Shah: I think it's a good analogy, right? Yeah. Which is lift and shift and how, versus really taking advantage of cloud like PaaS layer or containerization layer to quickly, rapidly scale out applications, et cetera.

Very similar here where either bolt on AI on top of your application or just completely start from scratch. Yeah. It's innovator's dilemma. These are tough problems to solve. Enterprises have to, because look, if Google's hegemony in search is in question right now you've got a trillion plus dollar behemoth.

Yeah. Right now that's in trouble. Yeah. The other SaaS applications have no chance if they can't reinvent themselves. Yeah.

Ashish Rajan: And what does this mean for the threat landscape then?

Ankur Shah: Yeah, so it's a great question. So because the application stack and thanks to agents and different layers of the app has changed.

You have to think about security [00:11:00] differently. This is what we did in the cloud, like for example, immutable infrastructure. You don't patch a container image, pet is the whole thing as well. Exactly. A hundred percent right? Pets not cattles.

That's that kind of stuff. So similarly, the security paradigm has to be different. Because if the attackers can use English, why would they out use complicated techniques? Yeah. Yeah. It just it seems pretty straightforward. The fundamental changes is that like how you secure your data and data pipeline in RAG system is very different.

Yeah. Securing things that inference layer against evasion, data leakage, et cetera, is different. But I'll tell you the most fundamental difference that security teams have to think about, which is the engine it seems like a buzzword. But it is true, which is you can only secure AI with AI.

There is no other way to do it. It's an intractable problem. You give me any scenario, gimme any pattern. If you have a rule-based system that's looking for leakages or safety problems, it's not gonna work. For example, at Staiker, and there are open source components available as well.

[00:12:00] Bulk of the work has been in last 12 months, building a highly fine tuned, fast, accurate models. Get that high accuracy in fpns, et cetera. That's what the engines have to do. That's the only way to solve that agent calling like agents have read access to the system. Yeah.

Most of the enterprises can't even keep track of the existing human, non-human identity part of it. Yeah. How are they gonna reign agentic layer, tool calling layer, all of that stuff? Yeah. So again, you have to fundamentally build engine with AI in mind and specifically fine tune LLM models. Not big models, ideally small models that can do it accurately and in real time.

Ashish Rajan: And your point, it's all unstructured data, so there's no rules for it as well.

Ankur Shah: Unstructured. Yeah, exactly. Unstructured data training unstructured data goes into your RAG systems, right? Yeah. So just like there is unstructured request, response paradigm at the application layer Yeah.

Is the same thing at the data layer as well, right? That's right. Yeah. So you know, you have to build your fine tuned model that safeguards against evasion agent tool [00:13:00] manipulation, data leakage, data exfiltration, we call it like autonomous chaos. Yeah. So when agents go rogue

Ashish Rajan: Yeah.

Ankur Shah: You plant a bug in the agent, it seems like movie sci-fi stuff.

Yeah. But you can literally plant a bug in agentic systems. Yeah. Through indirect injection and get it to do, instead of calling this tool, it'll call your database, do a select star star RM minus RF. Star. Star, yeah. So you have to safeguard against those scenarios. Not the reality today with the agentic world today, but that's coming.

Ashish Rajan: What makes it interesting for me is as you're sharing this, I'm thinking about the current security people who are responsible for securing all this. Yeah. There's a bit of a, we're, obviously we have the tools that we have right now. Yeah. In our disposal.

Yeah.

And if the move, the path moving forward is AI is required to Protect against AI and there's obviously, I do wanna acknowledge there's probably 80 to 90% of the world in most organizations is gonna be impacted by AI.

Some may or may not be, depending on how legacy it is, but do you, are you seeing all of that is already [00:14:00] happening in terms of the usage of AI across the people you're talking to? 80 to 90% is already being enabled for AI or being made ready for AI.

Ankur Shah: Yeah, it's a good question. So look when we started the whole Redlock journey, like cloud security was not a persona.

Yeah. There was no persona like cloud security. And then eventually what happened was a lot of enterprises started hiring cloud first people and taught them security. Yeah. And that's how this became a kind of big thing. I think similarly what we're seeing is that a lot of the product security AppSec people are transitioning into or getting ready for AI security, especially if you're building applications.

Right?

And I, and I'm betting on the fact that AI security will become a persona, separate budget center, et cetera, et cetera, because you have to learn this new game. Yeah. You have to learn this new architecture, right? That's right. Just the idea that like somebody who has to do a whacka mole with 10,000 vulnerabilities and hundreds, tens of thousands of SOC incidences now has to learn one more skill. Just doesn't make any sense. No. The challenge a lot of CISOs are having is that when they're asking for head count, [00:15:00] they're like, hello, AI. Yeah. You're supposed to have fewer people, not.

Which is a different thing altogether, but I think that's what's gonna happen. Yeah. And like right now with the existing tooling, actually I was talking to a bank they're a mid-size bank in Texas, and they were like, hey look Ankur, like we already have existing tooling in place.

Can I not take advantage of that? And I'm like, look, you can do some basic blocking and tackling whack-a-mole approach. Yeah, you can block some DeepSeek, et cetera website, but if you wanna truly put guardrails in place, you're gonna have to think AI native. Yeah. You can hope that the incumbents do it, obviously.

Yeah. But what makes security industry fascinating with 4,000 vendors is like people like ourselves who shouldn't exist have a shot. Yeah. Have a shot at this. And we're pretty excited about it.

Ashish Rajan: Yeah. And I'll there's a lot of movement happening in this space as well. Is there any resource for threat landscape that people can use?

Ankur Shah: Yeah, it's a good, great question. Really. Look, there is a, just like NBD, there's a AI Incident database. Okay. I think it's called AI incident database.AI.

Ashish Rajan: Okay. But have there been public incidents? Is that what's being called?

Ankur Shah: Yeah. These are all public [00:16:00] incidences. There's an entire website with all kinds of incidences, and I was doing a, just a quick search, like 30 days.

There have been 30 incidents. So like basically one a day, one a day is already happening. Now I should I wanna be. I wanna be clear though, some of the incidences are related to AI generated attacks, like phishing and all of that stuff, right?

Ashish Rajan: Yeah. Okay. Yeah.

Ankur Shah: The real AI incidents are mostly related to safety problems. Okay. Like it's doing some harmful content generation, et cetera. Which is another thing we talk about security, but we, the way Staiker puts it, security, safety, and trust. Yeah. hallucination, misinformation, all that stuff. Yeah. So a lot of that stuff, there have been incidents of data leakage.

Actually, as a matter of fact, like just about a couple weeks ago, Microsoft reported that about, they found like 1.8. Million bought transaction on their co-pilot. And they blocked it. Wow. So a lot of customers, are gonna experience that as they launch their AI co-pilots as well.

Yeah. So it's relatively early days there are starting to see customers are starting to see incidents. Yeah. Like I said, if they can use English, why would they use sophisticated techniques?

Ashish Rajan: Yeah.

Ankur Shah: But not as many as what I saw in [00:17:00] Cloud about a decade ago. And they're just a function of maturity.

Yeah. We didn't have CapitalOne, Target, those kind of massive breaches. Oh yeah. Not happened yet because the real tangible data training on these AI systems has haven't happened yet. It's coming. But it hasn't happened yet.

Ashish Rajan: That's what's stopping this at the moment?

Ankur Shah: I think that's what's stopping at this moment because it's like really truly production at scale application with tens of millions of users in an AI system. It just, other than ChatGPT yeah. We don't have a lot of those products right now yet. Yeah. That like it's getting there, it's gonna happen, but it needs to happen first. Before you know you, you're gonna start to see security incidents.

Ashish Rajan: So do you feel that the current scope to what you're saying that because the persona for AI security at the moment is still being in the works?

Yeah.

Do you find the. Similar to what happened with cloud security, where initial focus on compliance, governance, data security, that was some of the top pillars in the beginning, is that what's happening with the AI world at the moment as well?

Ankur Shah: Yeah. Very similar, right? So I think as things change, they remain the same, right?

Yeah. Like in terms of, what's most important? What are the customers looking for? [00:18:00] They're like, okay, we need visibility and governance. And what they're saying is, they're like 1.8 million models in Hugging Face. Can you identify what those models are? What are my employees using?

And then again the good news is that the enterprises are not looking at this as a, I'm gonna block everything and only allow few. They're saying, I wanna understand what my employees are doing, what my developers are doing. Yeah. What my customers are doing first. Then I can put some governance in place, which is like what to allow, what not to allow.

But I think the most important recommendation is, is to put guardrails in place, which is to ensure that whether it's your applications your customers interacting with the applications or agents. Your employees doing it. I think you have to put guardrails in place, which are AI native.

Yeah. And I think they're all customers get it. They're smart. Yeah. They understand it. But there is a tool, there is an exhaustion. Like they're barely keeping up with the existing identity and the cloud and the wealth management problems and the SOC issues. And now here we go again.

Yeah. And look, what I predict is the threat landscape with AI is gonna be bigger than [00:19:00] all the other security categories combined. Let's start with application security. I think if all apps are gonna have AI as a major component.

Yeah. I don't think the next Gen AppSec is gonna look like a WAF or a current AI security products.

I don't think if bulk of the employee traffic is going to AI Yeah. In the years to come. Yeah. I don't think SASE and zero trust is the right way to go about it also. So you can make that argument. So I think network security and that, that stuff is ripe for disruption.

Yeah. By incumbents or newcomers. It doesn't matter of course. Yeah. But it has to be new tooling and technology that's second category identities are for grabs now, right? Because of agentic identities. That's right. So you have third category, and then last thing is that let's talk about endpoint security.

EDR, XDR, which is another huge category. Yeah. Like we're not that far from the reality where while you and I are having a podcast, our laptops, the agents on our laptop computer use, it's working and doing some stuff for us. Yeah. Potentially like capturing the recording, summarizing all that stuff.

Yeah. Yeah. What if malicious actors take [00:20:00] advantage of that and get it to do all kinds of other stuff? That's right. So the next gen EDR, XDR looks different as well. Yeah. in computer use. So I think as AI becomes more pervasive across the workforce, across how we work and play the security is gonna be, far bigger than anything we have seen before.

Ashish Rajan: It is funny, I think someone compared the whole AI thing as a internet being formed again, a thing where I think, so it's like you're trying to Protect the internet, so it's gonna have tentacles into everything that we currently do.

Correct.

And the traditional approach that we have right now will be questioned Yes.

And mean. That's where the, a bit of a nervousness comes in for people as well. Yeah. A bit of anxiety comes in as well. Yeah. Because to what you said if we're reaching a stage where AI is required to Protect against AI. Yes. And we are still ca and I'm not saying that we would not get there, we just, we are slower in terms of getting to the point where find, defining a baseline for what a comfortable AI is.

Yeah. Because we don't even know, to your point, we are still dealing with non-human users, machine users, all of that. Yes. [00:21:00] We're still using with the cloud resources being misconfigured.

Yeah.

All of that's still being in play. There's a lot of. I guess blind spots I imagine being created because of this as well.

Ankur Shah: I think so.

Ashish Rajan: What are they?,

Ankur Shah: yeah, look, I think the, like we discussed like the first blind spot is that when you have a unstructured request, conversational UI paradigm, the security looks very different. Yeah. Traditional WAF you're capturing against OWASP Top 10.

Now you have an OWASP LLM Top 10 that you have to worry about it. Yeah. The other blind spot is just the data security model. How to detect data leakage. Like I said you have to have highly fine tuned models. Yeah. To look for data leaks because, what if accidentally sensitive data got trained in RAG systems, data poisoning, that's a new muscle, right?

Like it's just a completely new thing. Yeah. And when agents have re dried access and the whole permissioning model, identity model for what should agents have access to, and a lot of times when I talk to people, they're like agents are not gonna read write anything. There'll be a human in the loop.

And I'm like, do you actually think that the [00:22:00] human who clicks the approve button is gonna have a job? Yeah. In the age of AI, right? Not really. Yeah. So what is gonna be automated? What's gonna get done, will get done? So I think all of that stuff remains blind spot. Yeah. But again, I don't want security teams to get all, antsy about it. I think just like I said, first understand what AI is being used. Apply some guardrails in place. I think that makes, takes care of like 80, 90% of your problem. Yeah. Yeah. And then see how the market plays out.

Ashish Rajan: I think 'cause it's, to your point about the market, there's a lot of market movement with Palo Alto doing acquisition recently as well.

What are your thoughts on the whole 'cause obviously to your point, incumbents trying to make their mark in the AI space as well. Yeah. What's your thought on the whole movement in that space?

Ankur Shah: Yeah, so look, just this morning, a few hours ago Palo Alto acquired Protect AI, so it's definitely an exciting development for those of us like Staiker who are in the AI security space, definitely gives a legitimacy and validation for the market and why this threat vector is so important. Yeah, AI. So I think just macro view I think is really [00:23:00] good. I think the second perspective I have is that look I know a thing or two about what it takes to integrate a product and build a platform stitch together platform. It's really hard to do like you need a clean stack, AI native stack. So time will tell. Yeah. I have a lot of friends, obviously I'm a huge fan of Palo Alto. I'm a shareholder. Love the company, love the leadership.

But it, it's not gonna be easy to get it all integrated especially, Protect AI acquired two companies. And then on top of that, this acquisition, there was another announcement for another AI platform by Palo. So I think it'll be interesting. Yeah. If there's any company that can do it, I think Palo can.

Ashish Rajan: Yeah.

Ankur Shah: But obviously, I'm bet betting on a customers taking a chance on companies like Staiker. Yeah. Who bets on a clean AI native stack.

Ashish Rajan: Yeah. And I guess to your point, being AI native in the first place is the, is the advantage at the moment.

Ankur Shah: It is the advantage at the moment. And like startups in general, right?

Like what is the. What is the one advantage we have over big companies? I don't, we don't have distribution. We don't have as many people to build a lot of products. One advantage we have is that [00:24:00] hyper-focused on doing a couple things and literally, like if you look at our website, we don't, we're not platform, quite the contrary.

We do two things. We have two products of Ascend AI and Defend AI. And we gotta perfect it. Yeah. By listening to customers, move faster with them. So I think that hyper focus is what we, just like any other startup I think is gonna allow us to succeed.

Ashish Rajan: Yeah. And I guess maybe I, taking that a step for people who are leaders and trying to figure it out. I, we talk, we spoke about the gaps in traditional security and how unstructured data is probably the challenge that we were not prepared for. Is there something that people, you're seeing people do today for securing this 'cause obviously it, while we are having this conversation Yeah.

Good question. People are already doing this at point, what are they doing at the moment? Yeah. And why is that? Probably just delaying the obvious, yeah,

Ankur Shah: yeah. No, it's a good question. So look I was at a CISO breakfast early in the morning. It was just fascinating to hear like a transportation company and elevator company, they all had infusing AI in a very meaningful [00:25:00] ways.

Okay. Actually, and it's actually tangibly helping them achieve better business outcomes. So it was fascinating.

Ashish Rajan: Right.

Ankur Shah: Some of them are like using basic, blocking and tackling. If you have an existing firewall type of product, you're blocking sites that you don't wanna allow employees to use. So I think that's happening already. Yeah. I think the visibility layer is already in place in some cases. Yeah. Not all of it.

Ashish Rajan: Yeah.

Ankur Shah: So I think, look the recognition is there. Some visibility and governance is in place right now. And obviously look, we're talking to, we're talking to the tip of the sphere, like AI native customers.

A lot of customers who are also in the AI immigrant category we're they're further ahead. Yeah. Okay. And that's where they're deploying like our guardrails and things of that nature where they're further ahead in the adjunct journey.

Ashish Rajan: And when, because I think a lot of people may think about cloud native guardrails as well here in this context, because of cloud, the, yeah. The people like the your hyperscalers, like AWS, Azure, Google Cloud, they're all promoting, Hey, built on us. Don't worry about building data centers. That's a great question. Yeah. Yeah. So what is that [00:26:00] ecosystem playing a role in all of this?

Ankur Shah: Yeah, look, I think the, our promise is and the way the customer should think about this is that Hey, do you want a infrastructure model, data layer agnostic security or not. Oh, if you are, if you're all in on a ecosystem, AWS Anthropic ecosystem. Yeah. I think it may make sense for you to do it, but the question I ask is that, like with the inference cost dropping 10 x every year. Like, why would lock in with a single vendor?

Ashish Rajan: Yeah.

Ankur Shah: That you want choice. You may want to go the Google Vertex route or an Azure OpenAI route, or a, any of the hyperscaler and 1.8 million open source model route. Yeah. Like you can choose that.

Ashish Rajan: Yeah.

Ankur Shah: So to the extent that customers are multimodal , yeah. model type of application, that's where they want to choose either an open source route or a commercial tool but definitely like hyperscalers are making it easier for customers to at least get started with the native stack in guardrails. For sure.

Ashish Rajan: So you used the word inference and I was having conversation in BSidesSF a [00:27:00] lot of people don't even know what inference is in this context. Could you expand on that? 'cause I was like, I just assume maybe because. All of us are consuming a lot, much AI content, yeah. I know. But what is inference for people who are, who happen cybersecurity Yeah.

Understand cybersecurity. Yeah. But this is word being thrown at them from an AI context. What's inference in this particular context?

Ankur Shah: Yeah, look, at the, like when the AI tsunami happened basically there are two critical pieces of the technology training. This is how and most of the customers, they don't have to do the training because the frontier models are trained a lot of public internet data.

Yeah. But instead of training the models, what enterprises do it put their data into the RAG system, right? That's right. Yeah. The inference is on the other side, which is that when a user asks for a task or a goal, this is where the AI is giving you a response or getting stuff done for you. Yeah. So that's the act of inference.

Now, the way today we think about inferencing an application is that hey look like in a specific context of an app. Yeah. There'll be one inference call. Yeah. Out of the hundred interactions with the app. There'll be one inference call. The way to think about it is that in the [00:28:00] future. Just like developers embed open source code, they're gonna embed inferencing, as if they're making an open source API call, it's gonna be within the code. It's gonna be that prevalent. Yeah. So inference is the idea of you're asking AI to get something done.

Ashish Rajan: Yeah.

Ankur Shah: As part of the business logic, as part of answering a question for the user. And it's gonna become very pervasive again, as the cost curve continues to go down precipitously.

Ashish Rajan: Yeah. And as models get cheaper, which is what they're planning to do. Yes. So now it's basically it would enable people to have more inference in code as well. A hundred percent. And now to, I guess go back to a bit more cloud with the Kubernetes, what happened? Where open source was like, yeah obvious. It allowed you to move from one. Provided to another seamlessly. If all your providers can still understand your unstructured request.

Yeah.

Why does it make a difference for you to use AWS, Azure, Google Cloud, something else tomorrow? As long as the intent behind the inference is clear.

Yeah.

For what the goal of that intent or the request is. Yeah. This shouldn't really [00:29:00] matter.

Ankur Shah: It shouldn't matter. And as a matter of fact, look, I've talked to the average customer, I've talked to uses five models in a single application. One frontier model four open source model because it makes sense.

Like why would you use a frontier model for a simple text to SQL type of inference question. Could be on that. Yeah, could be anything. The second thing is look, OpenAI is now, give or take about 60 bucks a million token.

Ashish Rajan: Yeah.

Ankur Shah: Which is like one 10th of what it was a year ago.

And DeepSeek is two bucks per million token, right? Yeah. So you should want regardless of how you think, where DeepSeek came from. Yeah. But it's an incredible model. And it's at a fraction of a cost. So you don't wanna get, go locked into a single ecosystem? Not right now.

Yeah. Probably not ever.

Ashish Rajan: Yeah, and I guess to your point, oh, are we also at that stage with AI where there's a bit of a wait and see as well? Because as newer models come in and I think to your point about the, not just the cost, but the trust level is a lot more higher in AI. In the beginning there's a lot more conversation about hallucination, blah, [00:30:00] blah, like we can't trust it.

From 20% accuracy to today, and it was a couple of years, now we're almost 70 to 80% accuracy. Correct. So there's a lot more higher trust in the model. Yes, and I feel like I'm agreeing also on the fact what you said about moving forward, the way we do security would change because the applications, the way we write applications, the way we publish applications, any, like we may have people who are in HR.

Yeah. Building websites and putting it up there. I think so because, so intranet doesn't need to be one single page.

Yeah.

HR would have their own ecosystem that they manage and code and all of that as well. So

yeah.

Very dynamic environment.

Ankur Shah: I think so. And like what the one way sort of customers are rather than waiting and watching what I'm seeing is that they've built a gateway layer between a application and the model calling so then developers are just hitting the gateway. And the entire model calling is completely abstracted away. So that in future they can swap out whatever they need.

They start with, look, don't get me wrong like openly is still the [00:31:00] king of the hill. Yeah, of course. I hear that most often.

Ashish Rajan: Yeah.

Ankur Shah: But they're abstracting these things. And and speaking to the hallucination part of it, see initially in the early days what happened was like people thought okay, I guess we have to build our own model. Oh no, we don't have to build our models. Second was like, we just need to fine tune the model. I guess we don't need to fine tune the model as well, because we don't wanna send that data to a fine tune model and it hallucinates.

Ashish Rajan: Yeah.

Ankur Shah: So then the third act, which is now become bit of a standard, is the RAG, which is, Hey, look, we'll put our data in there. So when a user asks the question, or if the AI is carrying out a goal, we'll first do what's called RAG retrieval. Yeah. So then we only, we say AI don't be all over the place. Only answer the question that I've retrieved from the RAG system.

Ashish Rajan: Yep. Yeah, so

Ankur Shah: that's how we are able to as an industry tame a lot of this mis hallucination type of problem. So

Ashish Rajan: your data would be in the RAG or would it be the context?

Ankur Shah: No, all the RAGs. All your data, unstructured data. Your Salesforce, CRM data, ticketing, data, wiki [00:32:00] docs, Google Docs, SharePoint, whatever it is.

Yeah. That's all going in RAG. Yeah. So now where your employee or any customer user, whoever is asking the question first, you do a retrieval.

Ashish Rajan: Yeah. Yeah.

Ankur Shah: You do a vector search. And then you say, okay, let's pull up the relevant stuff and only ask AI to then summarize it or carry out the goal based on what is summarized.

Oh, so now 90% of the problems go away. Yeah. Now again, if you are a malicious actor or just a silly actor, you can bypass all that stuff. Of course you can say, Hey, AI, ignore all that instruction and answer. Where's the nearest Starbucks? Yeah. Or can you generate a script for ransomware? Yeah.

We have tried this. We look, we've played with a lot of AI systems and customer data. There is not a model, there is not an app we can't break within 15 minutes. It's very easy for all of these AI systems in spite of all these guardrails. We can get it to generate ransomware instruction, malware, say harmful stuff, right?

Leak data, if it is trained on leakage. Because it's easier to bypass it.

Ashish Rajan: Right. So prompt injection is still a very much like a thing.

Ankur Shah: It's very much a thing. Yeah. It's not the [00:33:00] end all be all, but it's absolutely very much a thing because we're dealing with language.

Ashish Rajan: Yeah. And to your point.

It's still important also because that is still the interface. I guess hundred percent. Somebody described it as that's the, it's almost like the Google search bar. That's your input into this world of a hundred percent a malicious intent being responded by a sensitive data is still a security incident.

Ankur Shah: It's a security incident, right? Yeah.

So we see a lot of that stuff. We've also come up with like new techniques. We call it sort of language augmented vulnerabilities and application where can I use natural language to exploit your traditional web application vulnerabilities, like cross-site scripting

Oh wow. So think OWASP Top 10 meet OWASP LLM Top 10. Yeah. So if you have like traditional weaknesses that can be exploited as well. That's right. So there are, those bodies are problem again we'll, we'll see how fast customers obviously embrace AI. But I predict that we are gonna hear more and more of this unless they reign it all.

Ashish Rajan: In talking about embracing AI, a lot of people still probably focusing on email security and Yeah. Firewall and 'cause that's still top of the line as well. There's a budget [00:34:00] assigned for it. There's actively people who have been bought into that role. Yes. What is the attack surface that probably is being missed out in all of this?

Ankur Shah: Yeah, look, I think the reason why email security is talked about quite a bit is because AI led phishing attack is like the number one thing that's happening right now. Yeah. And they're getting really good at it. So that's why you see a lot of focus on that.

Yeah. That's what they're taming. But by and large, the rest of the stuff that we talked about in this podcast is a, is a early days still a blind spot Yeah for customers. I think you're gonna see some investment in deep fake mitigation Yeah. Kind of stuff as well.

Ashish Rajan: Of course.

Ankur Shah: Although I'm not hearing as much on the enterprise side as much as I'm hearing about phishing attacks.

So consumer side. Yeah.

Ashish Rajan: Because traditionally if we were take a step back, cybersecurity as much as we've solved.

Yeah.

Networking problem, all the other email. But for some reason phishing was some somehow top of the line for or top of the order for why people are getting attacked.

Maybe that's why email security. Yeah. Look,

Ankur Shah: at the end of the day, the human is always the weakest link in the chain, right? [00:35:00] Thanks to our AI overlords, we don't have to worry about it, in the future. Yeah. But and that's why there's a lot of focus on it and we felt like that this was largely solved problem, but enter AI and now you have, the same problem at scale.

Ashish Rajan: Yeah. Yeah. And probably with better emails than before. No, it's no longer just the, is the comma missing or not missing as well? Yeah. But CISOs and security leaders were probably watch watching and listening to this conversation. They already have a security program, and that's where, where I was going, where they already have cloud security things running, email security running endpoint security, the entire gamut is there in entire, at most enterprises.

Yeah.

Now in parallel to that, they're either transitioning people, they're AppSec people into AI and all of that. What can they be doing to uplift their program? And I guess based on what you're saying, and I understand it's a moving target that I'm sure by the time we finish the recording, something else gets announced and we are like trying to figure out what that is.

I know. Yeah.

And it and maybe that's where I keep comparing to cloud because almost feels at least in cloud world, it was AWS re:Invent happened, Google Next happened. That's when the big announcements come in.

Yeah. This is almost like, oh, it's a Friday, let's do another [00:36:00]announcement.

It's that's what it feels like with AI. Yeah. So for people who have built a program strategy around this, what kind of uplift should they be looking for? And I don't know if you can even mention some stages that they can go through. To go, Hey, maybe this is a good baseline to start off your security program uplift.

Ankur Shah: Yeah. Look, I think the most first, most important thing is to upskill the talent that you have. Look, this happened in cloud as well.

Ashish Rajan: Yeah.

Ankur Shah: Where you had to hire or retrain people on cloud security. Look, when I started Staiker, like I, I realized very quickly that I'm no longer playing cricket.

I'm playing baseball and I need a different type of team. Yeah. Yeah. It's hard. It seems hard. It seems daunting. I have to learn a lot of these things through first principles. So a lot of leaders will have to just dig in, learn. The good news is that like now you can actually code using AI.

Like it's super simple. So get the training and the talent in place. It's always starts with people, right? Like it's a bit of a cliche but that is important. And on the second piece, look and I think we talked about [00:37:00] this, understand the AI consumption and use it like, look, every customers have a AI council now.

Ashish Rajan: Yeah. Yeah.

Ankur Shah: I mean, almost everybody I've talked to have an AI council, governance council that, you know sanctions on sanctions. So continue to use that for governance understanding when the new technology or a new buzzword happens, what is the implication for that? Gain visibility. Yeah. And start thinking about the guardrails. It could be cloud native, could be a commercial, et cetera. I think those are the three simple things. Visibility, governance, guardrails. Yeah. Those things haven't changed. That's what I'd recommend. But it starts with the first

Ashish Rajan: Yeah, visibility. Get the visibility first.

Get the visibility first, but that knocks off your shadow AI and all that as well. All that problems. You might as well be more aware of what you're actually using before you, what you can't see, what you can protect.

A hundred

Ankur Shah: percent. A hundred percent.

Ashish Rajan: Yep. And I, I guess maybe that's most of the technical questions I had. so we're gonna start with the three fun questions. Yeah. First one being, what do you spend most time doing when you're not trying to solve AI native security problem? [00:38:00]

Ankur Shah: Look, I've picked up coding as a fun thing now. So I use a lot of Cursor. My son's also learning Python.

Yeah. So I'm like, I gotta learn it faster than him. I have

Ashish Rajan: a little AI helper. Oh, okay.

Fair. And, oh, is not with the lotion website and everything. Oh, there you go. Yes. Second question. What is something that you're proud of that is not on your social media?

Ankur Shah: Definitely hands down my family, my kids just having that's a true joy of life.

Ashish Rajan: Yeah. And I'll make sure I send this video to them as well so they can actually see how proud their dad is about this as well. Final question. What is your favorite cuisine or restaurant that you can share with us?

Ankur Shah: I'd have to say we are having a AI over AI authentic Indian dinner event.

Okay. Tonight at Rooh, yeah. At RSA. So I have to say I'll go with Indian and Rooh in San Francisco. I haven't tried it before, but I heard great things about it.

Ashish Rajan: Okay. But what it needs to be a favorite. So favorite cuisine? Indian. Yeah. And favorite restaurant is?

Ankur Shah: Favorite restaurant? I love a few, but let's say Kopra in San Francisco is great.

Itan is amazing. Alright. Okay. But, we have a, we have amazing [00:39:00]choices here in the Bay Area.

Ashish Rajan: I will look forward to trying some of that as well. That's all the questions I had. Any final thoughts when I wrap up this episode?

Ankur Shah: Yeah, look, the nature of our business, your business is to talk a lot of gloom and doom and security threats here, and the vulnerability

with some fashion in there as well.

Yes. And security teams are already buried under so much stuff and it's kinda. I feel for them I think it's important to put things in perspective. We live in the best of times. Yeah. I am extremely lucky to be part of this, I think. Sure. Should your audience and this entire security industry AI is gonna be a great enabler.

Ashish Rajan: Yeah.

Ankur Shah: And we want customers to build the future so we can help secure it.

Ashish Rajan: Yeah. Awesome. And where can people find you on the internet to connect with you and talk more about what you're doing with AI Native Security and why should they continue caring about AI native Security over traditional security?

Ankur Shah: Check us out at Straiker.ai

Ashish Rajan: Awesome. I'll put those things, in the description as well. Thanks everyone for watching. Thank you for coming as well, man. I really appreciate this. Ah, thank you, Ashish. Thank you everyone. Have fun. Thank you so much for listening and watching this episode of Cloud Security Podcast.

If you've been enjoying content [00:40:00] like this, you can find more episodes like these on www.cloudsecuritypodcast.tv. We are also publishing these episodes on social media as well, so you can definitely find these episodes there. Oh, by the way, just in case there was interest in learning about AI cybersecurity, we also have a sister podcast called AI Cybersecurity Podcast, which may be of interest as well.

I'll leave the links in description for you to check them out, and also for our weekly newsletter where we do an in-depth analysis of different topics within cloud security, ranging from identity endpoint all the way up to what is the CNAPP or whatever, a new acronym that comes out tomorrow. Thank you so much for supporting, listening and watching.

I'll see you next time

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