Observability & Evaluation · Software component

Alignment Drift Monitor

Software componentObservability & EvaluationObservability & Evaluationarc:AlignmentDriftMonitor

A monitoring component that periodically re-measures principle adherence of deployed or updated models against baseline measurements to detect value drift toward easier-to-optimize proxies.

Responsibility. Detects degradation of alignment over time.

Also known as: Value drift monitoring, Value drift detector, Behavioral alignment monitor

Direct neighbourhood (hover for relationship types)

Relationships

invokes dependency

Principle Adherence Evaluator Ch9.5

reads dependency

emits telemetry to dynamic

Metrics Dashboard abstract Ch9.6

escalates to dynamic

Oversight Governance Committee Ch9.6

triggers dynamic

monitors assurance

Design guidance

MUST treat alignment as a continuous process: budget for monitoring, adversarial evaluation, periodic retraining and principle updates.
MUST run continuously post-deployment; alignment verified once does not remain stable.
SHOULD detect both gradual degradation and sudden shifts, feeding real-time alignment dashboards.
SHOULD trigger escalation procedures and iterative retraining when misalignment is detected.

Classification

Patterns: Baseline comparisonPeriodic re-evaluationContinuous alignment monitoringPeriodic re-alignment
Quality attributes: Safety (ISO/IEC 25010 | NIST AI RMF: safe)Maintainability (ISO/IEC 25010)Reliability (ISO/IEC 25010 | NIST AI RMF: valid and reliable)
Risks mitigated: Value driftFine-tuning driftDistribution shiftCatastrophic forgetting of principlesCumulative interpretation changesValue drift from distribution shiftAccumulating misalignment from incremental updatesAlignment circumvention via jailbreaksGoal generalization failure
Frameworks & regulations: EU AI Act (high-risk requirements)

Sources

Ch9.5: T. Nguyen, "Constitutional AI," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 9.5. ISBN: 9798244538229.
Ch9.6: T. Nguyen, "Value Alignment Frameworks," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 9.6. ISBN: 9798244538229.
Ch10.3: T. Nguyen, "RLHF Methodology," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 10.3. ISBN: 9798244538229.