Zscaler, Inc. Common Stock Goldman Sachs Communacopia + Technology Conference 2026

I wasn't there during the IPO, but I was at companies that were competing with Zscaler without proxy architectures. I certainly know the space well. I think that the argument on all of it when we think from a product perspective is what's right fit for purpose of what problem you're trying to solve. You have to come back to some sort of first principle there. The reason why a proxy architecture is powerful in cybersecurity specifically is because when you get in the path of something, you have the chance to actually see and inspect. The whole point of doing cybersecurity is you're looking for either threats coming in or you're trying to understand where data is moving and should it be moving or not.

Just connecting things serves a purpose that allows you to get from point A to point B, but the whole reason why you have that concept of a proxy is it's an inspection point, and it's a chance to at least see and observe. You can observe, take notes, and take no action, or you have the chance to enforce policy. That architecture, time and time again, comes back up as a staple of cybersecurity products and technology because it works and it's extremely powerful. Now, it's like any security piece, it's friction in a process, so you have to be careful how much friction you introduce into a process. With agents are not operating in silos. They're operating by connecting to other data sources or connecting by working with other agents.

You have a whole interconnection to consider, and that is ripe for where you apply security into that. What do you do at the edge? What do you do in that connectivity path? That's why we think that proxy architecture is very powerful. The challenge will be, and the key is in how do you apply it and what's the same, and then what's different when that traffic is agent traffic versus any other kind of traffic.

Sure. I'll give you one very vivid example that'll try to drive that point home. In most communications or most sessions, if a human's driving it, like I was describing before, you want to understand, should I allow something or not? You basically have milliseconds to do that, because anything longer than that, then the user thinks something's wrong and they go away, and they file that IT ticket, or they just find a path around it. You don't get a long time to decide is this a good website or is this a bad website, or should I allow this transaction or not. A lot of, as an example, in inline DLP, you look for a very quick pattern match. Yep, that's sensitive content. My policy says sensitive content isn't allowed out, so it's not allowed. Right? You make that real decision.

In AI, and this can be both human working with, let's say, a generative AI chat interface or an agent, the iteration, the back-and-forth conversation is actually how that works. The intent of a one-line request that mentions cookie, you could say that's a chocolate chip cookie recipe, right? If it's a back and forth over six different turns, it might be about a session cookie that's holding an authentication token for a particular transaction, and that might be much more sensitive information that's being sent out. So you actually have to look at, in using AI terms, a context window, right? Or understand multi-turns of a conversation. So the way you inspect is different, and what you're looking for also starts to change depending on what you're trying to protect.

In more classic world, it's looking for vulnerabilities or is this malware or is this an exploit coming down or is my data leaking? In the AI world, you might be looking at is someone trying to poison my model? Is someone trying to get it to do something it wasn't supposed to do? There's no vulnerability there. There's no signature for I've exploited a vulnerability. That's the way how a lot of cyber works. When you're proxying AI communications, you need different kinds of inspection engines for that traffic.

Sorry, she said Kevin three times.