Building an AI Guardian for Enterprise with Onyx Security CEO Maxim Bar Kogan
In a Nutshell
Onyx Security builds specialized oversight agents that monitor and control autonomous AI agents in enterprises, using lightweight trained models as efficient gatekeepers that escalate only high-risk actions to more capable systems. Traditional security tools fail because they lack context into agent reasoning and cannot restrict permissions without destroying agent utility, while AI vendors cannot access enterprise historical data needed for anomaly detection. The company positions itself as foundational infrastructure for managing superintelligent agents controlling critical systems, with Israeli cybersecurity expertise enabling products that understand how security teams actually operate.
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As enterprises exponentially increase their use of AI agents, the risk of illegitimate or incorrect agent actions grows dramatically. Enterprises have observed agents accidentally publishing code and tokens they weren't supposed to expose. Companies recognize this risk has grown exponentially but cannot stop adoption, forcing them to implement measures to reduce the chance of harmful agent actions. Enterprises have access to substantial historical data on agent behavior but are unwilling to allow companies like Anthropic or OpenAI to retain this data due to concerns these AI companies will train on enterprise data.
Maxim Bar Kogan is the co-founder and CEO of Onyx Security, an Israel-based startup composed of researchers, mathematicians, and engineers building agents to oversee AI agents. The company focuses on specialized model training, alignment research, and operates within the Israeli security and AI ecosystem.
Two years prior to the conversation, the primary security concern was DLP for chatbots—what employees were inputting into ChatGPT. The current environment reflects near market-wide panic around AI security, particularly regarding autonomous agent actions.
The pivotal moment for founding Onyx was AutoGPT. AutoGPT created the first autonomous agent running on LLMs—an agent that allowed the LLM not merely to generate text but to decide actions, receive API access to execute those actions via tools, and operate in a loop. This architecture theoretically enabled agents to perform complex tasks equivalent to human computer operations. Though AutoGPT didn't function optimally due to insufficient model capabilities at the time (even GPT-4 was inadequate), it provided a vision of future autonomous agents. Cloud Code today follows a similar structure to AutoGPT, validating the original concept.
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