Stratagems #17: Alex Set an AI Bait. The Catch Wasn't Code — It Was Someone Who Shouldn't Have Been Watching.

Continuing the 36 Stratagems series, this episode uses a honeypot scenario to explore how AI systems can be instrumented to catch unauthorized observers — not external attackers, but people inside an org who should not be watching a particular model or pipeline. The insider-threat angle is underexplored in most AI security writing, which tends to focus on prompt injection and model theft rather than access-pattern anomalies. The fictional framing makes it slow reading, but the core idea — deliberately leaving a visible but monitored hook in your AI infrastructure to surface unexpected access — is worth ten minutes if you run anything with sensitive data pipelines. The reservation is the same as episode 16: the series format and parable style slow down the signal-to-noise ratio considerably. -> Best for: technical PM or AI engineer responsible for access control in multi-user AI infrastructure