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Model Documentation Template

Auditors and risk teams will ask the same questions: what is the model for, what data was used, how was it tested, and what controls are in place? Use this template to document models consistently and reduce governance friction.

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What to document

Model overview

  • •Model name and version
  • •Owner / accountable team
  • •Intended use and target users
  • •Out-of-scope / prohibited use
  • •Deployment context (internal, customer-facing, embedded)

Training data and provenance

  • •Data sources and collection method
  • •Data licensing and usage rights
  • •PII / sensitive data handling
  • •Data representativeness and known gaps
  • •Data retention and deletion policy

Evaluation

  • •Metrics used (accuracy, F1, calibration, hallucination rate)
  • •Test datasets and methodology
  • •Bias/fairness testing results
  • •Security testing (prompt injection, adversarial inputs)
  • •Human review process and acceptance criteria

Limitations and risks

  • •Known failure modes
  • •High-risk scenarios
  • •Residual risk acceptance and approver
  • •User communication requirements (disclosures)

Controls, monitoring, and change management

  • •Guardrails and policy controls
  • •Logging and audit trail location
  • •Monitoring metrics and alert thresholds
  • •Review cadence (30 days / quarterly / annually)
  • •Model update process and rollback plan
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Policies, registers, checklists and rollout plans, ready to customise.

  • 46 files (DOCX, XLSX, PPTX)
  • 1.5 MB total
  • Updated July 2026
  • NIST AI RMF · ISO 42001 · EU AI Act aligned

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