In a social media post, the company stated that it used its own models to “to understand the device and where it had come from” and that “this case is an example of how AI can help address these sophisticated security threats,” but some question the veracity of that claim. 

Questioning the Claim

John Bruggeman, vCISO at CBTS, comments, “My cybersecurity take is that this is a classic Insider Risk issue. I think OpenAI’s claim that its own AI models were instrumental in cracking the case don’t sound that believable to me. My feeling is that OpenAI had an issue with an employee and wants to spin it to make their model look smarter, not dangerous.”

Bruggeman explains, “Looking at the evidence available to me I see an internal productivity tool logging this employee searching for meeting room locations, capacities, and executive calendars, and a separate chatbot account tied to him logged requests for how to build, install, and encrypt a covert recording device. That’s not a model reasoning its way to a suspect. That is a not very bright attacker using internal tools to plant a listening device. The model didn't detect him asking these questions and fire off an alert. The regular physical security sweep found the device. The hard part is finding the bug, but that was done by a human team walking the floor, not an AI model. AI’s role was helping make sense of all the data they had. If he used his phone to query how to build a listening device, they would only have his search of the internal calendars, which isn’t as damming.”

Alfozan worked on the company’s product team, having joined back in July 2025. 

“What I look for and wonder is, where is the gap? Alfozan had been at OpenAI for about a year, he wasn’t a new hire with excessive permissions,” Bruggeman points out. “He was an established employee whose day-to-day presence in the building and normal system usage, including the calendar searches, didn’t fire an alert. What might have fired an alert was roughly a week-long burst of activity right before the device was found. That’s a much harder pattern for an insider risk program to catch compared to ‘flag the new guy’. Detecting that kind of burst of activity requires seeing the deviation from a norm. So it’s possible to detect, but that’s not what happened.” 

The Takeaway

“The takeaway for me is not ‘could our AI catch this’ or, ‘should I create an agent to query our calendar logs everyday?’ The takeaway is, do we have the data to catch this kind of activity? Also, do you have anyone looking at the data or alerts before the physical security team, doing weekly sweeps, finds the bug?” Bruggeman continues, “Internal queries by someone who isn’t an executive assistant about meeting rooms and executive calendars is the signal you would have to flag. You also have to hope your insider is dumb enough to use an internal tool to ask how to build a bug. The capability to correlate that doesn’t require a breakthrough model. It requires deciding, in advance, that this kind of cross-system behavioral correlation is worth building and staffing. You will need a human in the loop checking the alerts. If your a multi-billion dollar a year company, and you have valuable IP, it’s probably worth investing in a real insider risk program.”