Vivek Kumar, Alter Domus & Mayank Upadhyay, Snowflake | theCUBE + NYSE Wired: Cyber Security Leaders

Palo Alto Studio Connection Silicon Valley and Wall Street. I'm John B here with Dave Volante, my co-host. Welcome to the Cube studio here at the New York Stock Exchange. I'm Jim Allen with NYC Wired cyber security leaders where we talk to the people shaping and securing the future of technology, business, and markets. For decades, enterprise cyber security has been built around one basic assumption and that is that there is a human on the other end. But we know AI agents are changing that equation rapidly. They can operate autonomously, access multiple systems, call tools, and increasingly we hear call each other and they've been given permissions that historically belong to humans. With this raises a very interesting question. Could the next major enterprise breach come not from a compromised employee but an agent itself? Joining me are two security leaders looking at this from very different angles of enterprise. May OPDI snowflake's chief security and trust officer and Vivic Kumar global chief information security officer at Alraas. Welcome folks. >> Thank you. >> Thank you. Really exciting to be here. >> What a great venue. >> Two fascinating companies. I mean, we're no stranger to ultradomus here on Wall Street. And we're also no stranger to Snowflake, right? You guys have had an interesting year on the street. There's a lot of enthusiasm for Snowflake and for Altored. And the future we know is changing very, very quickly. So, let's get straight into it. We're going to talk about trust security and what's happening faster than I think many of us predicted, perhaps even yourselves. >> [snorts] >> Let's start just unpacking the world of 2026. We're halfway through and I'm going to start with you, Vic. >> Yeah. >> You have built the backbone of caption markets, right? But it's essentially about giving clients and customers access to real-time data as efficiently as possible. We know AI is changing that. Talk me through how that changes your job from the perspective of security and trust. Break it down for me. What's changed in the last two years? I have I have more job security. I'm just kidding. [laughter] Uh it is changing a lot because the landscape has for last two years 3 years since the Aentic AI adoption the whole landscape has changed. Earlier you know uh whenever the new tools and new products and new things which used to come we used to do like a yearly review or yearly new releases coming up and all those kind of thing were very delayed then it moved to you know 6 months then monthly now weekly now daily. So if you talk about anything happening in this world especially in terms of uh security in terms of technology releases are happening every single day. So so it has become very dynamic and to cope up with that dynamic environment security as a chief security officer it is it becomes very very difficult task and challenging task though I'm loving it to cope up with that. So that's yeah whole dynamics paradigm has changed now. So let's talk about Snowflake. I mean fascinating company. Everyone I think who's worth their salt in the industry knows exactly who Snowflake is and what you guys do. We know though that the world of data, the accessibility, the interoperability, who and how that data is accessed is changing at a scale that like I said no one expected. Right. This year a lot of enthusiasm around Cortex the gateway that Snowflake is introduced. Certainly did something interesting to your stock price. Talk me through it though from the perspective of the buildout like you were you one of the kind of front minds behind this >> break it down for me like give me the lowdown. Look, I think what's happened in the last couple of years, people went from simple chat bots to agents who can take actions and um there's two things which have happened really in the last six months, I'd say. One, the agents have got really good at parallelizing everything they do. And their job basically is to look under every rock, look at every nook and cranny. You give them a goal and they try to get to that goal. And this means that if they're operating as you, they're going to have all the permissions you have. if there's any security issues in your enterprise, they're going to find those. That's a huge problem. The second thing that's going on is that these models have gotten really smart. You know, these agents are different from traditional software in that they have their own brain, right? Which is these models. So with traditional software, you knew exactly it was going to call this next piece of software through an API. It was going to have very very fixed deterministic behavior. But these agents, they have a brain of their own. And these brains are getting smarter and smarter. And the models have gotten so good these days at what's called long horizon reasoning without hallucinating, right? They're able to go deep and find issues uh as a result of that. So when you put all these things together, you're in this this interesting uh scenario where on the one hand you've got this massive productivity boost, but when something goes wrong, it can go spectacularly wrong. So it's honestly a great time to be working in the security industry and trying to bring all of this chaos under control. So we started off on this journey you know we've been in the agentic journey for a year and a half now. Um earlier this year we acquired a company called Ntoma which does bring some governance to MCP. We're building on the back of that we've now got an AI gateway that is coming out in the market this month. And this AI gateway gives you everything from protecting the model protecting the agents and integrating down to your data stack. So it's the full shebang. We've got our own experience for the last year and a half how we've modernized our further agentified our enterprise and so we want to take that even uh further offer to our customers and we're super excited about that. >> We hear a lot about MCP. It's become a real buzz word. You know we talk about it every day here on the show. >> It seems as though again every company is building towards this if not already trying to execute on this strategy. We also know though it's a different kind of vector from the perspective of security. How do you think about it like from the perspective of you know your footprint your ecosystem and the interoperability elements like what does it mean from a governance perspective? >> Um first of all you know we all are fighting the same enemy. So so the over here the partnership is a big key thing >> you know the MCP adoption is happening so fast and so rapidly than your security controls. So problem what is happening is that there are bunch of MCP uh gateways people are using which you are not even aware of and and the pro and and you it's very hard to keep track of it and govern it and find it where it is and it is creating a big issue in any industry from the security perspective they should be going in to a one governed MCP gateway so that security can watch it and do things so as you said earlier that if an enterprise level attack coming in it looks like a legitimate thing >> but although it's on track it's no longer a traditional thing what my was talking about earlier because now the MCP gateway you know all the things are going out looks like agent uh good thing legit thing one agent is talking to other agent delegating it to third agent delegating it to fourth agent and that delegation is working in such a manner that it's very difficult to govern and audit that piece so MCP gateways and all this although It's the best thing which has happened but if the governness is not in place then you are going to lose uh you know visibility and get into some kind of a big problem and there were a lot of things happened with the GitHub and other things which you saw >> and they were all because of these breaches happened because of these things of delegations and multi multi- aent uh functionalities and MCB gateways >> I mean let's stay on NC MCB for a second but let's talk a little bit about models because it's a very interesting evolution, right? We know that in some respects MCP, it opens the floodgate to all sorts of different accessibility vectors, right? Models, you know, we hear a lot about anthropic. We hear a lot about open AI. We hear a lot about the security level of open weight models versus, you know, the models we're more familiar with here in the US. What are your thoughts though from the perspective of the kind of non-negotiables from a risk perspective, especially at a company like Snowflake where there has to be some clear parameters around what can and can't happen, what can and can't engage. >> Yeah. Look, first and foremost, we um like both proprietary models as well as open weight models, right? So we can get that out of the way. We we we like to use them both. Um you get a lot of benefits from the openweight models. uh as a security uh team, you don't have constraints when you're doing an investigation uh when you're doing threat modeling. Also, from a cost governance perspective, they're so much better. The other thing which is a non-negotiable for us and for many of our customers is that um they don't want they want their data to stay within their parimeter. So, as you work with these models, you kind of have to make sure the data doesn't accidentally end up with the model provider in a way that that might get used up for training. And I know you know everybody has lots of different architectures for this but it is the crown jewels that our customers have and so that is a non-negotiable for us. So uh a lot of the ideas behind what we're rolling out right now with the AI gateway are meant to give you that data separation uh the visibility. You you me use the word visibility use the word trust. Trust comes from transparency and and predictability, right? So with our AI gateway, we want to give you that observability so you know exactly what's going on in the agent and the model and we're watching it to make sure that nothing bad is happening for you >> and you have a complex network from some perspective, right? Like there is different folks who use many different technologies, many different APIs, many different tools and that's probably only going to get even more diluted as this world of AI and inference expands. How do you think about it from the perspective of accessing the data and then securing the data once it's you know in the hands or in the presence of whatever actor you hope it hope redeem it to be. >> Yeah. So you this is very critical because data is all very important. You can't build an AI layer without having a data security layer in place. So because the AI sits on top of data. The problem happening is this MCP gateways and the models and all it's a new new thing which is now it's turning into a shadow AI thing. >> Even the MCP gateways people are using models people are using MCP gateways they are downloading things and everything and they are creating agents. Suddenly this is becoming a shadow AI kind of thing for us. It's a big problem. Agents when they talk to a different agent or other agent they have a capability of escalating their privileges. So all of these agents are doing a lot of different things. So you are like really scared to see that your data, your critical data, your PI information, your financial information doesn't get pasted and it goes out of your boundaries. So the cross broader and cross organizational uh boundaries should not be overlooked and that's a main concern when this is happening. And it is very very important that for that not to happen you should know that what kind of gateways you are using what kind of models you are using and all the agents what actions they are doing there should be a least privilege actions what what you mean is what I mean is that if a agent is supposed to do a task he should only get a permission and escalation to only do that and then it dies down. You cannot have a longl lasting controls given to them to do the perform the work. So definitely you have to build the governance as snowflake was talking about it. >> But how do you truly do that? Right? Like there's obviously governance, there's compliance, is it sandboxing first? Like what are you truly doing to ensure you know that whoever it is or whatever actor it is is trying to access data. It's not just about verification. It's about everything that comes after that. Also, >> I'd probably mention four different things to you. On the one hand, you have sandboxing, which makes sure you can do stuff, you know, on your disk, steal secrets. If you're going out to the network, we know exactly where you're going. I would throw MCP governance as as a parimeter control because you can see who's talking to what MCP tool. Are they allowed to talk to that MCP tool? The next layer I talk about is the identity layer. When you have an agent acting on your behalf, you don't want it to act with all your permissions. If you've been at a job for 5 years and changed different roles, you probably picked up a whole bunch of permissions along the way, right? You don't want to give all of them to your agent because an agent is going to try each and everything it can. Even look at paths you didn't think it might, right? So the first and foremost thing you need to do with identity and where we're headed now is we're coming up with new standards. So when you kick off an agent, you can say, "Hey, here's a scope set of things you can do on my behalf." It's a little bit like say, you know, I like to think of agents as interns. They're untrained employees, right? Now, let's say you were a company, you know, you had an office manager intern who was supposed to go buy you a new printer and you send them off to get a printer and they came back with this Wi-Fi control refrigerator, right? So, what do you do instead? You give them a a gift card for Best Buy which is set to like, you know, $200. So, you put those constraints in place and say, "Here's what you're allowed to do." So, even if you sort of, you know, wear off where you're supposed to be, you're not it's not going to be catastrophic. It's going to be more or less within those guardrails I set for you. [snorts] So those are the kinds of primitives we're coming up with in the industry and we have to make them easy for people to use. So again you can use AI to say hey you're asking your agent to go research this topic for you. I think it's going to go need to look at all these tables. So let me scope permissions to read only permissions to just those tables. So so that's you know a way of sort of making sure it doesn't go off and do something on its own. So that's the second big thing I think that's coming. And I just want to quickly mention I think we also have to come and take care of more of the observability detection techniques and we can talk about that if >> that that's what I was talking about the agent have least privilege because the agent the number of non-human identities which is the AI agents is growing faster and rapidly than the human identities. >> You can control human identities based on their birth rights and whatever permissions and access privilege access they have. But in an agentic space for the AI agent it's very difficult to do that. So with somebody coming up or snowflake coming up with this kind of a um you know security control identity becomes very key key factor for us >> and that non-human identity where there's multiple versions of a human based on a workflow or a task right that that's a complex security system and ecosystem to to oversee and you mentioned observability I mean that also changes the game fundamentally from managing someone at a domain level to managing somebody or a actor I guess you can call it and multiple proliferations. >> Yeah. H >> how do you think about that? Is that a you know when we really boil that down does that come down to like governance and compliance and very clear standards of execution or is it something more complex than that? >> No it is complex because as I said it's changing very rapidly very dynamically. The entire access management system was based on human identities. >> It has a paradigm shift now and also it's growing. Plus you don't even know what they are doing. So people are concentrating about the action what the agent did but you also need to see that who authorized it who gave them that permission to do it. So there are a lot of other complex piece which was missing uh you know which has to be incorporated in terms of this AI. Another thing you talked about the the sandboxing just want to touch upon it. What I see the problem is with the agent agentic AI and the whole AI thing is >> you are sending a kindergartner to college directly [laughter] right there. Yeah. You have to go through elementary school, middle school, high school and then to college. So if you take anything which has just came out of the market and not sandboxing and not testing in your lab suddenly implementing them in your in your production environment, you don't know what is going to happen, right? And it creates one creates other issue, the other issue and all that the scale goes so big that it will be difficult to control. You mentioned something very interesting which is observability right and the road map of a company like Snowflake because again the expectation is also shifting right there was a time when we were so excited just to be able to access data to be able to see the data in some sort of concrete and succinct way but now the role 10 years from now is going to about observing the data who's what who also is watching that data >> how do you think about that from a company perspective like you know what what does that road map look like who owns that problem. >> Yeah. So, um, look, I think a lot of traditional security vendors are all moving very fast to try and fill this gap. Uh, at Snowflake, we just announced our Cortex AI gateway, which is going to sit right between any agent from any company and the models you're using. So, it just slides right into your architecture and it gives you that observability where it can see what these agents are doing. Um, and it's there's actually two sides to to the observability problem. Obviously, you want to make sure your agents are not misbehaving, right? So, if they're if it's a well behaved agent that's going through this, you can you can look at its what's called its traces, its trajectories, and you can see is it is it doing something it shouldn't be. But there's a different side of observability, but what if there's an agent running in somebody else's environment that's now breaking through your parimeter and entering your enterprise, right? You have to catch those two. So that goes down to so so while there's this whole new category of agent observability to make sure your agents are well behaved, there's also the traditional security world of just building detections into your parimeter, uh fixing your security vulnerabilities so agents outside don't find ways to get inside your parimeter, right? And those have to be taken super seriously and there's probably a limited amount of time for for enterprises right now to go and patch these. So using uh AI to find these vulnerabilities, to patch these vulnerabilities, right? This is the time to be doing all of that. >> I want to go back to something you said at the beginning which is very relevant to your industry too, right? Because financial services is very secure and we're hearing a lot about companies going back on prem sovereign AI bringing actually AI to your data as opposed to bring your data to AI from the perspective of Snowflake and and I'm going to put this to both of you. What does that mean in terms of like what you're building towards like how nuanced and different is that from a security perspective or is there kind of a unilateral security guideline around what it means to have data on prem versus in the cloud and how folks are going to go about again managing monitoring and continually observing what's happening with it. >> So uh it's it's a great question and every day we are exploring new things. First of all, you need to know where your data is to secure it to manage it, right? So, as you said, bringing data to AI or bringing AI to your data. So, that has become a big challenge. First is we need to know where your data is lying, where which data link is sitting. It's always scattered all over the things. Agent as you know, they have a very privilege access to go and talk to other agents and try to dig in and go everywhere. And the problem is that if there is a agent sitting outside who can come into your environment and can have access to the data through the agent which is inside which is a legitimate agent is a problem. So we have to secure our parameters. We have to make sure where our data is and make sure that that is secure. The governance and the guardrails is the biggest things which we have to work on this thing that how data needs to be treated and who can treat it and what kind of permission that person or agent has for that data and it is it has become very important. The Sims of the worlds where they used to do the detection like discovery and detection and all all was based on very traditional environment. It was not based on the AI agentric environment. So so you have to go and change. So you have to change from identity perspective, data security perspective, SIM source perspective, governance perspective, policies perspective, everywhere there is a change to see that how data can be accessed, who can access this, who authorized it, what kind of identity and governance you have for them. >> So if I could add to that Gemma, my take is that going back to onrem is not the answer. The way the world is headed, everything is interconnected, right? Unless you're a three-letter government agency that is operating, you know, in complete secret, I think in general, you would expect people and and companies want to have more trade, more uh more commerce between them, right? And people are talking about agents going and doing shopping for you. So, uh I think these are bringing more efficiencies into our life on a daily basis. So I think they and even if look the moment you expose yourself to working with partners and interacting with systems outside there's a a threat surface there that you have to worry about that can be attacked. So I think you have to go back to the basics and the basics don't mean going onrem it means fixing finding and fixing your vulnerabilities putting detections in place so that if you're compromised you can detect that at machine speed and you can remediate at machine speed. There's also a super interesting and simple technology called deception. It's kind of like, okay, let's say you have a giant house and you've got lots of doors and windows and you can't put a burglar alarm everywhere. Well, guess what? You can you can get a big safe in the middle of your house. So, if a burglar gets in, they get they zoom in on that safe and the moment they open that safe, the alarm goes off. So, there's lots of interesting tricks to deal with this. And I think the the it's we're headed to a world where the the basic security practices about cleanup and vulnerability patching and management uh as well as detections and remediations are have to be carried out at AI speed. Now, >> so last question and I love when somebody takes a position on something. You know, we hear a lot about the move back on prem though, right? And maybe that's built on false assumption or maybe there's a whole administrative layer that was never fully fixed in the era of cloud that is now suddenly coming home to roost somewhat from the perspective of security and it feels like it's an easier solution when in fact maybe it's not the long-term solution companies need. Right. >> That's right. >> But h how do you and I'm going to put this to both of you as as you both move forward. Right. These are both fascinating companies, industry leaders. How do you think about the messaging around what's happening in this industry, conversations like that one, and the role of of of companies like yours to kind of in some way lead folks forward at a time where there is so much conflicting information and so much worry? >> U so I I would agree with Mang on is not a solution. You can't move two step backwards and one step forward right because of the all the SAS platforms and everything coming up you know you can't go back on prim and lot of things yes there are certain things you can still go back but again are you going to increase your footprint which which which again involves a lot of cost right so you may be focusing on the wrong thing if you want to go back to on-prim it's some people say that it could be safer than sorry but that's not it is because the whole world is open to the SAS platform we are using a lot of these kind of tools and techniques and softwares. Now bringing everybody thing back to on-pre will not be a solution. It will be very very very expensive solution though. So what we need to look at it that whatever things we have like blank was talking about vulnerabilities management and all that kind of thing. I was talking to one of the CEO was saying it's no longer at zero day it's all minus one day minus 2 day right. So fixing the vulnerability comes next but patching process has to be very proactive. So in this environment you have to be very proactive. You should know where your thing is. If you have that obsibility, you have that visibility then proactively go and uh face it and attack it and solve it rather than thinking back what's going to take the world to onrem solution. >> Let's say I'm patching and I want you to finish with this. If we just take meters as an example, right? We know that there is huge vulnerabilities detected across operating systems at large banks, large enterprises. We also heard that the fix from a patching perspective could take anywhere from 6 to 9 months in a you know minus 2 minus one day scenario that's an alarming reality. How do you think about that? Like what are your thoughts from the perspective of where we're at right now? the pace of where we're headed to head on the gap >> I think the way u software patching has operated in the past is changing now um right now not only can AI find vulnerabilities it can also suggest fixes and some of the cutting edge models have a very high acceptance rate when they suggest a fix the developers like it like you know people are talking about upwards of 85% acceptance rate right so we're getting to a world where you know traditionally when you found a vulnerability you would have a long argument about is it actually reachable? Does it matter? Do you have other mitigating controls in place? Right? Um or or if you had open source packages, you would think about, hey, should you how do you get this package to be upgraded? Is somebody even maintaining it? But in the new world, you can you can have software generated fixes. It's like, you know, three lines of fix. Just roll it out. Don't even bother debating it, right? Or if it's a small open source package of 300 lines or less, which is the bulk of them, and nobody's maintaining it, can you just get AI to rewrite it for you? So there's all these options which have opened up and I think security teams have to think differently in this new world. >> Wow. Well, we certainly hope that they will and that the world remains protected. So folks, fascinating companies, fascinating time, great conversation. Thanks for joining us on the cube and NYC Wired. >> Thank you very much. Thank you for having us. It was great. Thank you. >> I'm Gemma Allen here at the Cube Studio at the New York Stock Exchange. This is Cyber Security Leaders, one of our programs with NYC Wired. We connect Silicon Valley to Wall Street. Thanks for watching.