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

**Maxim Bar Kogan** (0:00)
As you're exponentially doing more things with the eyes, you're going to start having really bad actions 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, enterprises are starting to realize that that risk is growing 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 enterprises that are not willing to have Anthropic or OpenAI keep that historical data because they know these are very data-hungry companies that will want to train on that data.

**Sarah Guo** (0:45)
Hey, hi, listeners. Welcome back to No Priors. Today, I'm here with Maxim Bar Kogan, the co-founder 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.

**Maxim Bar Kogan** (1:08)
Thank you. Pleasure to be here.

**Sarah Guo** (1:10)
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 years ago when you started the company was basically like DLP for chatbots, like 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?

**Maxim Bar Kogan** (1:40)
Look, I think for us, the pivotal point was auto GPT. I think auto GPT kind of let everyone's imagination, including ours, run wild because it was a...

**Sarah Guo** (1:50)
Can you remind listeners what that was?

**Maxim Bar Kogan** (1:52)
Sure. So auto GPT, and I'm sorry if I don't know the guy behind it, but a huge fan. They created the first, as far as I know, first really autonomous agent running on LLMs, right? So an agent that 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 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-4 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 could have very capable agents doing stuff for us. I think that was, in many ways, cloud code today is not this similar to other GPT back then. I think they were a bit early on, again, before the models were ready, but the concept was right. And the thought that sticked with me was, I was very eye-pilled even back then, so I was thinking, oh my god, models are going to be way smarter than us. When that happens, how do we oversee these very smart agents that are, you know, they're smarter than us, they're very capable, how we're going to feel easy about them doing stuff for us, especially when they start managing really important stuff, you know, then one day they're managing your water supply and your electricity or power grid, right? How do you control them? And that was like the thing I was kind of obsessed about that thought.
I was also too early. So I think at the time, enterprises were not using any agents. And there were hardly any agents out there. And, and talking with a lot of security bodies at the time, they were like, oh, dude, you're way too early. Like, this is not something that's going to happen.

**Sarah Guo** (3:52)
I asked you the same question. I said, is anyone going to do this before you run out of money?

**Maxim Bar Kogan** (3:57)
And I think there was a good chance that I would have run out of money before, because I think you were right. Like, I think there was an element of chance here.
But then I think the market did happen. So we had suddenly reasoning models that could do long horizon tasks. We had a cloud code, which became like the really first widely used autonomous agent. And then we had co-work and open claw.
And I think we're starting to see now that these types of agents that are very autonomous, even though they're like, everyone was afraid to build them. So everyone started building these low code platforms that were much more limited, much more based on connectors. And those platforms ended up being quite limited. So we didn't get the productivity gains from those limited platforms. But we started getting the crazy benefits from these very unleashed agents that could do everything, that had much less controls baked into them. And even very large enterprises decided they're going to adopt it. Like, Anthropix Revenue is coming from enterprises that are paying for cloud code to do a lot of the work that developers used to do. That was a bit of how we started. And we definitely were in luck that very autonomous agents appeared before it was too late.