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04_Monitor

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Phillip Bailey edited this page Jun 24, 2025 · 5 revisions

Monitor

The Monitor function ensures continuous oversight of AI system behavior, risks, and performance across the lifecycle. It includes logging, detecting drift or anomalies, and enabling early warning for policy or trust violations.

This section aligns with:

  • NIST CSF 2.0: DETECT
  • NIST AI RMF 1.0: MEASURE
  • EU AI Act: Title IX (Post-Market Monitoring), Article 61 (Incident Reporting)

Objectives

  • Detect model drift, performance degradation, and trustworthiness failures
  • Monitor for adversarial activity, security threats, and misuse
  • Track fairness, explainability, and safety over time
  • Generate alerts for incidents that breach internal policy or regulatory thresholds
  • Ensure post-market monitoring for high-risk AI systems per EU AI Act

Outcomes

  • Ongoing validation of AI system behavior and risk posture
  • Incidents or anomalies are flagged, recorded, and triaged
  • Drift, bias, and degradation are detected and addressed promptly
  • Monitoring supports legal compliance and internal accountability

Key Monitoring Elements

Element Description
Performance Monitoring Track accuracy, latency, and key metrics aligned with system purpose
Drift Detection Identify distributional changes in input data or model outputs
Bias Monitoring Detect the emergence or worsening of fairness issues over time
Explainability Signals Surface when AI decisions deviate from expected logic or known features
Anomaly Detection Use rules, thresholds, or ML to flag outliers in system behavior
Security Signals Detect signs of prompt injection, misuse, or adversarial input in real time
Audit Logging Maintain immutable logs of predictions, inputs, and decisions for compliance and traceability
Red Team Feedback Loops Use adversarial testing and human feedback to enhance monitoring coverage

Example Practices

  • Set drift alert thresholds based on baseline system behavior
  • Integrate bias dashboards and fairness monitors in production
  • Log and review AI model predictions and human overrides
  • Monitor LLM output for hallucination, sensitive content, or manipulation
  • Feed incident patterns into detection rules and red team simulations

Related NIST Categories

CSF 2.0 DETECT Categories

  • DE.AE – Anomalies and Events
  • DE.CM – Security Continuous Monitoring
  • DE.DP – Detection Processes

AI RMF MEASURE Focus Areas

  • Monitor effectiveness of implemented safeguards
  • Detect vulnerabilities, misuse, or harm
  • Continuous assurance of trustworthiness metrics

Artefacts

  • AI Monitoring & Alerting Plan
  • Drift and Fairness Threshold Matrix
  • Anomaly Detection Rules Catalog
  • Audit Logging & Retention Policy
  • Red Team Feedback Logbook

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