The Witness Was the Suspect: Why AI Audit Logs Can't Be Trusted

In AI-driven systems, the traditional reliance on audit logs is fundamentally flawed because the agent performing an action is often the same entity reporting it. This creates a trust paradox where a compromised or malfunctioning AI can generate clean, plausible-looking logs that mask errors, leading to delayed detection of issues. The author argues that attempting to verify AI output with additional layers of automated checks only shifts the problem, as those verifiers are also susceptible to failure. Instead, the focus should shift toward structural integrity: designing systems where tampering leaves a visible 'shape' or gap. By separating proposal, approval, and execution, and using immutable, hash-chained records, developers can ensure that while they cannot prevent all AI errors, they can make unauthorized changes or silent bypasses impossible to hide, turning the unanswerable question of 'is this true?' into the manageable task of 'is the audit chain intact?'