AI lifecycle evidence & model-specific artifacts
AI lifecycle evidence and model-specific artifacts in ISO 42001 are the concrete records that prove you govern each AI system from conception to retirement, not just at the policy level. Auditors expect a joined-up evidence trail that links risks, design decisions, testing, deployment, monitoring, and decommissioning for every in-scope model.
Lifecycle stages and expected evidence
For ISO 42001, evidence must cover the full AI lifecycle (inception/design, development, verification/validation, deployment, operation/monitoring, re-evaluation, retirement). Annex A control A.6 explicitly ties controls and records to these lifecycle stages.
- Inception and planning: This phase is typically evidenced by an approved business case, a clearly articulated problem statement, feasibility analyses, high-level risk and impact screening, and initial drafts of the AI impact assessment (AIIA).
- Design: Evidence commonly includes requirements specifications, system architecture documentation, data flow diagrams, threat models, detailed AI risk and impact assessments, and recorded decisions on explainability, human oversight, and guardrails.
- Development: Auditors will expect to see model training logs, data quality validation records, bias and robustness testing results, documented rationale for parameter and feature selection, and versioned experiment outputs.
- Verification and validation: This stage is supported by test plans and results, fairness and performance benchmarks assessed against defined acceptance criteria, red-team or adversarial testing reports, and formal go/no-go decisions.
- Deployment and operation: Typical evidence includes release and approval records, change management logs, monitoring dashboards and alert histories, human-in-the-loop escalation records, incident tickets, and structured user feedback.
- Re-evaluation and retirement: Evidence consists of periodic reassessments of AI risks and impacts, documented retraining or update decisions, decommissioning plans, and records of data and model disposal or archival.
- Model identity: This includes a unique model identifier, version history, assigned owner, defined purpose and criticality, and traceable links to supporting business processes and relevant stakeholders.
- Data lineage: Evidence covers data sources and collection methods, labeling and enrichment processes, data quality metrics, and provenance checks, including documented handling of synthetic and third-party data.
- Risk and impact artifacts: Auditors will look for AI-specific entries in the risk register, documented impact assessments covering individual, societal, and regulatory considerations, and recorded risk treatment decisions with associated compensating controls.
- Performance and fairness: This is demonstrated through baseline and ongoing accuracy or utility metrics, segment-level fairness evaluations, drift detection analyses, and the thresholds used to trigger retraining, rollback, or other corrective actions.
- Governance hooks: Evidence typically shows human oversight through approval gates and override logs, clear accountability via defined roles and RACI mappings, and integration with security, privacy, and incident management processes.
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