Building an AI Guardian for Enterprise with Onyx Security CEO Maxim Bar Kogan

As you're exponentially doing more things with the eyes, you're gonna start having really bad action happen. And we've seen some of that happen lately with agents accidentally publishing code and tokens that they weren't supposed to. Like, definitely enterprise are starting to realize that that risk has grown exponentially and that they don't have any way to stop the adoption. They just now have to do something to reduce the chance of these agent actions being, illegitimate or incorrect. But we're allowed to look at a lot of historical data of how these agents have behaved, but any person that are not willing to have Anthropic or OpenAI give that historical data because they know these are very data hungry companies that will want to train on that data. Hi, listeners. Welcome back to Know Prior. Today, I'm here with Maxim Bar Kokan, the cofounder and CEO of Onyx Security, an Israel based startup of researchers, mathematicians, and engineers building agents to watch the AI agents. We talk about specialized model training, Mythos, alignment research, and the Israeli ecosystem in security and now AI. Welcome. Maxim, thanks so much for doing this. Thank you. Pleasure to be here. Everyone is much more concerned about security and the impact of AI on security than they were, certainly a few months ago. The consensus risk story, two two years ago when you started the company was basically, like, DLP for chatbots. Like, what are what are employees putting into ChatGPT? Now we have clearly something that is not quite panic, but close to market wide panic. How did you decide to bet on agent actions, when you started? Look, I think for us, the pivotal point was, AutoGPT. I think AutoGPT kind of let everyone's imagination, including ours, run wild because it was a Can you remind listeners what that was? Sure. So AutoGPT, and I'm sorry if I don't know the guy behind it, but a huge, huge fan. They created the first, as far as I know, first really autonomous agent running on LLMs. Right? So agent that, you know, would let LLM not generate text, but decide what to do and then give that agent an API access to do that thing, a tool to do it and then we'll do that in a loop. So it basically in theory could let agents do very complicated things, anything a person could do on the computer. Now, granted it didn't work that well, it was too early. The models were not good enough. GPT four was not good enough, but I think it did give everyone a glimpse into the future of, you know, what if the models were good enough and then basically using that same structure, we would have very capable agents doing stuff for us. I think that was in many ways, cloud code today is not dissimilar to other GPD back then. I think they were a bit early on on again before the models …