Observability & Monitoring

StackSeal monitors your AI systems against their documented regulatory posture in real-time. Performance metrics, output distributions, and behavioural patterns are continuously compared against the baselines established during the TRACE Architect phase. When a system deviates from its documented behaviour, you know immediately — not at the next quarterly review.

Model performance drift, data distribution shift, and output deviation from expected ranges — detected automatically with configurable thresholds. StackSeal distinguishes between natural drift (gradual distribution changes) and anomalous drift (sudden behavioural changes that may indicate data quality issues, adversarial inputs, or model degradation).

When an AI system's operational behaviour diverges from its documented intended purpose, StackSeal raises a regulatory divergence alert. This is distinct from performance drift — divergence means the system is doing something different from what the documentation says it does. Post-quantum cryptographic verification ensures alert integrity cannot be compromised.

Every monitoring observation, alert, and response action is recorded in a cryptographically chained evidence log. Post-quantum cryptographic signatures ensure that the monitoring record cannot be tampered with retroactively — critical for regulatory investigations and audit proceedings.