Honeypots for insider threats from AI agents

IAPS released a report evaluating the value of honeypots as a defense against insider threats from AI agents, categorizing them into three strategies: detection, forensics, and deterrence. The report finds the evidence base for detection is strongest, providing low-cost, high-confidence alerts for unauthorized operations, but coverage is limited to paths where decoys have been deployed; forensics lacks public evidence from production environments; deterrence has the weakest evidence and should be treated as a research hypothesis, with its main basis being an industry study where, after early warning that frontier models might engage in deception, the attack success rate in a simulated enterprise cloud environment dropped from 20% to 3%. The report also lists limitations: honeypots struggle with diffuse threats where harm accumulates across multiple small actions, and can only cover paths the defender can anticipate. It proposes five priority research directions.