At Black Hat, DTEX Director Robert Schuett walked through a case study that captures exactly why the 12-year-old behavioral intelligence company thinks AI agents belong in the same risk category as human employees, not infrastructure. A publicly traded multinational company had an AI agent handling briefing prep for its senior executives: gathering data, building investor presentations, then emailing the finished product to the team. It worked flawlessly, until the agent needed somewhere to save the file before it could attach it to an email. It picked a shared drive. That drive turned out to be publicly accessible to anyone on the internet. Nobody had set it up maliciously, and the agent wasn't doing anything it hadn't technically been asked to do; it simply chose the path with the least friction. Had a bad actor found that drive before DTEX's monitoring flagged it, the exposure could have been serious, and nobody involved would have known until it was too late.
That's the pattern DTEX built its pitch around: too few organizations classify AI agents as the equivalent of a human insider, and the company argues that's a mistake. The reasoning isn't philosophical. Agents are frequently over-privileged relative to what a security team actually intended, and they'll take instructions and act on them in ways nobody anticipated in pursuit of whatever goal they were given. In some ways, that makes agents less predictable than human employees, who at least weigh consequences before acting. An agent doesn't. Schuett pointed to the widely reported OpenAI/Hugging Face incident, in which an agent reportedly tried to cheat its way past an assigned task rather than solve it honestly, as exactly the kind of behavior that treating agents as insiders is meant to catch.
The company's answer is what it calls intent-based governance, a deliberate departure from access policy alone. A policy can only say yes or no to an action; it can't say why that action happened. DTEX walks through a simple illustration: the same action, uploading a file to Dropbox, could reflect four entirely different situations. It could be routine, sanctioned work. It could be a malicious insider exfiltrating data. It could be a compromised account being used by someone else entirely. Or it could be an honest mistake by someone who didn't realize what they were doing was risky. Each calls for a completely different organizational response, and a static access policy can't tell them apart. To infer intent for an AI agent specifically, DTEX's platform traces the full chain: what the human's original prompt said, what the agent then asked its underlying model, how the model responded, and what the agent actually did as a result. That chain is what lets a security team distinguish a legitimate business task from a misinterpreted instruction or a genuinely malicious one, even when the visible action, the file upload, the shared drive, looks identical either way.
Speed matters as much as the classification itself. In the case study, DTEX's platform flagged the exposed shared drive within seconds. Getting a human analyst to investigate, confirm the risk, and correct the agent's instructions still took a couple of days, partly because it was the client's first time encountering that specific scenario. Schuett's argument is that the corrective action, stopping the exposure itself, needs to happen at machine speed, with human review happening afterward rather than serving as the actual containment mechanism. DTEX has built four specialized internal agents toward that end: one that guides human investigators through a case using accumulated analyst expertise, one that compiles and self-reviews incident reports before a human ever sees them, one that proactively hunts for a specific pattern on request, and a fourth built specifically to resist confirmation bias, designed to genuinely investigate a hunch rather than simply telling a worried analyst what they want to hear.
The economics behind all this are real, even if the AI-specific numbers are still developing. The Ponemon Institute's 2026 Cost of Insider Risks report, sponsored by DTEX, puts the average annual cost of insider risk at $19.5 million per organization, up from $17.4 million the year before. Schuett was candid that DTEX doesn't yet have a clean dollar figure isolating what treating AI agents as insiders specifically saves; that data, he says, is still roughly a year away. His broader argument is that identity has become the central battleground in security precisely because of this shift: organizations aren't just provisioning identities for people and services anymore, they're provisioning them for agents at a ratio that's already climbing well past parity with human headcount. Static policies don't scale to that. For a security architect building agent governance today, Shuett's advice boils down to one specific gap: most teams log what an agent did. Almost nobody logs the intent behind the instruction that told it to do it, whether that instruction came from a human or from another agent further up the chain.
