00:00:00 · Sebastian Johnson, Yinon Costica
[00:00:00] S0: Hello, and welcome back to Scaling Europe Show. Today on the show, we've got an amazing guest in on co founder, VP of Product at Wiz. Phenomenal company, one of the most incredible stories of the last five or six years. Thank you for joining me. How are doing today? [00:00:11] S1: Hi, Seb. Thank you for having me. I'm doing well. [00:00:13] S0: It is my absolute pleasure. Want I to get into it. The first question I want to ask is about the similarities of the time that we're living in right now with AI to sort of the moment that Wiz was founded in with this with this, like, move to cloud. You know, five or six years ago, Wiz saw this explosive growth as companies were rushing to sort of get onto the cloud, and it presented an amazing opportunity for you and the team to be the the cloud security platform for those companies. Are we living in a... Are you seeing parallels with that movement in that moment to cloud today with AI?
00:00:45 · Yinon Costica
[00:00:45] S1: Absolutely. Actually, super happy you started with this question because this is really how I feel in many cases where I'm looking at, you know, questions on how would the AI market evolve, what will happen. So I actually take a step back. I try to remember what were the dynamic back in the cloud days, and that's how I try to really project a bit the trends. And I think that there are many similarities in a way of how do we adopt a new technology? How do we actually think about transformation? There were recently... Also, I was thinking about the similarities when cloud started. Everybody were on this journey of selecting the one cloud they will. Today, nobody, like, lives in a single cloud. Everybody's multi cloud. And I I see very... Like, it's striking similarity to how AI is being consumed. It's not gonna be about a single model or a single AI provider. We're gonna need to have a variety for diversity of thought, diversity of intelligence applied in our organization. So a lot of similarities also in terms of the economics. How do we consume the need to drive not only premium products, but also accessible, affordable products at scale? So a lot of similarities indeed. One, maybe two differences that are key to understand. One is the speed of transformation. Nothing to compare. In cloud, had maybe a decade to ingest, think, decide how to do it, how to do it right. We had a lot of time. In AI, it seems like we're really compressing a decade into less than a year in in, like, the transformation and the acceleration of the development of the technology. And the second thing relates to cybersecurity. I think that cloud, you know, wasn't a key keep... Like a key accelerator for the threat actors. It was yet another attack surface. I think that AI, when we look at it from a cybersecurity perspective, it is an accelerator for the AI threat landscape. And a lot of what we see today in cyber is driven by the application of AI, both on the offensive side and defensive side. So these are two big big differences.
00:03:08 · Sebastian Johnson
[00:03:08] S0: Those big differences are pretty stark, right, in the sense that you've got compressed timelines that people are sort of like panicking to get... To adopt this technology, and you've got the fact that there's an increased risk factor. What does that mean for your stakeholders? Like, are you seeing, like, a lot of anxiety and urgency among customers? And then I also wanna ask, like, how does that impact the way that you're building? So can we... Let's start with the stakeholders and the customers. What's going through their minds? How are they reacting, behaving?
00:03:33 · Yinon Costica
[00:03:33] S1: Absolutely. I think it changes everything. It changes... First, it's really important to understand that it's... It doesn't matter whether an organization chooses to use AI or not. The threat landscape has changed. Attackers will use AI. So by definition, we have to think about securing against an AI powered attacker regardless of whether we're using AI or not in the org. So it's not our decision. And I think that this is a... This is an understanding that is driving a lot of the adoption of AI technologies for security because we understand that if we now have to face an AI powered attacker, we have to set up AI powered defenses. And I think this is maybe every stakeholder really understands it. And I think that more than this, we have a lot of examples in... Out... Like, out in the news, in the market that are very concrete examples that help us to really structure and put a framework on how we think about the new landscape and how... What we should prepare for. So I think that's definitely on the stakeholder side. I will say that cyber today is in a state of, I would say, very high maturity when it comes to acknowledging the risks and the need to invest in cybersecurity. This has not been the case a decade ago or even five years ago. And I think that this maturity in the cyber industry really helps to drive the right discussions that are discussions of how do we enable AI, but at the same time, how do we secure ourselves and how do we do effectively secure ourselves. And these are the right discussions to have. So that's on the stakeholder side. On the... How we develop, it changed completely how we operate. It changed completely how we think about the products that we need to build. Right? First, internally, are leveraging AI across every single team. It's like we have to transform whether it's a development team, the product team, the research teams, everything is now driven by AI because it's an amazing, amazing unlock of productivity and intelligence that we can harness. So so that's for sure. On top of this, we need to rethink the capabilities with AI when we think about scanners, when we think about analyzing code, when we think about analyzing workloads, applications. AI can help us tremendously do that. And I'm thinking about the... Let's say, a pen tester in the past.
00:06:17 · Yinon Costica
A pen test red teaming was a very, very exclusive expertise that we had to apply in very certain scopes of our environment only once a year and with a very limited, let's say, capacity of a human pen test. And now we can apply through what Wiz launched as a red agent. We now can apply continuous infinite capacity. So I think that this represents our ability to automate and bring capabilities. Wiz introduced a new layer of agents. We actually named them after the security teams, red, blue, and green agents. The red agent does pen testing like an offensive security team. The blue agent does defensive investigation response. And the green is actually fixing code cloud in order to bring back to to green state. And I think that this agentic layer represents a new way to extend security products and think about how security products should behave against an AI threat landscape.
00:07:20 · Sebastian Johnson
[00:07:20] S0: And and the the... There's the early stat or the early story of you having hundreds of discovery calls with CSOs in the early days of ways to try and find, you know, the issues that you want and settling from network security back to cloud security in this kind of, like, very transformational age of AI. And you seem to be talking about your agents, but there's a lot of things you're releasing quite quickly. Yeah. What is the process by which you are deciding what to build? Are you still relying on talking to stakeholders a lot? Because I feel like there could be almost some sort of gap in the in the sense that you probably have a better understanding a lot of the AI threats and risks than some of the stakeholders do because things are moving so quickly. Are you still able to rely on the feedback and input from CSOs? Or is there a bit more education this time around because, yeah, things are changing so quickly?