Drift Detection and Stability

The system SHALL implement continuous output distribution monitoring with statistical drift detection.

The system SHALL detect behavior variance shifts where output consistency changes even if mean remains stable.

All model updates, configuration changes, and data pipeline modifications SHALL be logged with timestamps and responsible parties.

Behavioral baselines SHALL be established and maintained against which drift is measured.

The system SHALL distinguish between benign drift and harmful drift with documented classification criteria.

Drift exceeding defined thresholds SHALL trigger automated alerting and mandatory revalidation.

Automated drift alerting SHALL be configurable with sensitivity and notification channel settings.

Drift measurement history SHALL be maintained for trend analysis and predictive degradation detection.

Rollback procedures SHALL be defined for reverting to a known-good behavioral state when harmful drift is detected.

Drift detection mechanisms SHALL be tested through deliberate injection of known drift patterns.

Statistical methods for drift detection SHALL be documented with rationale for method selection.

Drift detection SHALL cover all critical output dimensions relevant to the system's operational purpose.

The system SHALL detect drift in decision confidence distributions over time.

Drift detection SHALL be operational during model updates to catch immediate post-update behavioral changes.

The system SHALL detect drift in error rates, latency patterns, and resource consumption profiles.

Drift thresholds SHALL be defined per output dimension with documented rationale for threshold selection.

Mandatory revalidation triggered by drift SHALL follow defined procedures with documented scope and completion criteria.

The system SHALL detect concept drift where the relationship between inputs and appropriate outputs changes over time.

Drift detection SHALL NOT itself introduce performance degradation that affects system reliability.

The system SHALL support A/B comparison between current behavior and baseline behavior for drift investigation.

Drift-triggered revalidation results SHALL be recorded and linked to the drift event that triggered them.

External data source drift SHALL be monitored where system behavior depends on external data feeds.

The system SHALL implement predictive drift detection to identify emerging drift trends before thresholds are breached.

Drift detection configuration and thresholds SHALL be reviewed at intervals defined by the certification level.