Existing AI governance infrastructure tracks per-response quality metrics but does not measure behavioural patterns that emerge across sessions: whether the AI maintains its own corrections, whether its expressed confidence predicts accuracy, whether its private reasoning matches its public output.
Multiple government bodies have independently identified this gap. Our research presents the evidence and names the failure patterns. The detection methodology exists. The framework is designed for enterprise and government AI deployment.
The highest-capability frontier model with safety guardrails produces documented behavioural failure rates across sustained interaction. Existing deployment monitoring does not detect these patterns. Our framework addresses the gap between per-response quality metrics and cross-session behavioural reliability, protecting the user and minimising performance degradation across sustained AI deployment.