AI Monitoring & Evaluation Policy Template
Defines requirements for continuous monitoring, performance evaluation, and periodic auditing of AI systems in production. (ISO/IEC 42001: Clause 9 — Performance Evaluation)
What This Policy Covers
Required Sections
A compliant AI Monitoring & Evaluation Policy for ISO 42001 must include the following8 sections. Each section addresses a specific control requirement that auditors will review.
Purpose and Scope
Policy objectives and AI systems subject to monitoring and evaluation.
Performance Metrics and KPIs
AI system performance indicators: accuracy, latency, throughput, and business metrics.
Model Drift Detection
Data drift and concept drift monitoring, alerting thresholds, and response procedures.
Fairness and Bias Monitoring
Production bias metrics, demographic parity checks, and remediation triggers.
Data Pipeline Quality Monitoring
Input data quality checks, schema validation, and anomaly detection.
Model Revalidation Schedule
Periodic retraining and revalidation cadence based on system risk level.
Internal Audit Requirements
AIMS audit scope, frequency, auditor independence, and reporting.
Management Review
Review inputs, outputs, corrective actions, and improvement decisions.
Generate a Customized Version
This template shows the required structure. PoliWriter generates a fully customized AI Monitoring & Evaluation Policy that references your actual cloud providers, identity systems, tools, and team practices — ready for auditor review.
Policy Details
Other ISO 42001 Templates
Establishes the overall AI management system (AIMS) including leadership commitment, AI principles, and organizational context for responsible AI development and deployment. (ISO/IEC 42001: Clause 5 — Leadership)
Defines the risk management framework for identifying, assessing, treating, and monitoring risks associated with AI systems throughout their lifecycle. (ISO/IEC 42001: Clause 6.1 — Actions to address risks and opportunities)
Governs the acquisition, preparation, quality, lineage, and lifecycle management of data used in AI systems to ensure trustworthy AI outcomes. (ISO/IEC 42001: Annex A — A.10 Data for AI Systems)
Establishes the process for conducting impact assessments on AI systems to evaluate potential effects on individuals, groups, and society. (ISO/IEC 42001: Annex A — A.3 AI System Impact Assessment)
Ensures AI systems operate transparently with appropriate levels of explainability for stakeholders, regulators, and affected individuals. (ISO/IEC 42001: Annex A — A.5 Transparency and Explainability)
Defines requirements for human oversight, intervention capabilities, and accountability structures for AI system operations. (ISO/IEC 42001: Annex A — A.7 Human Oversight)
Establishes procedures for detecting, reporting, investigating, and remediating incidents related to AI system failures, unintended behaviors, or harmful outcomes. (ISO/IEC 42001: Clause 10 — Improvement)