Checklist

AI Governance for Operations: Validation & Safety Checklist (Interactive)

Interactive, operational checklist to validate AI/ML systems before deployment and during operation. Covers objectives, boundaries, validation on production-like data, monitoring and drift detection, human-in-the-loop design, access controls, incident response, versioning, compliance, evidence capture, and formal sign-off by operations, safety, and IT/data owners.

Interactive Tool

AI Governance Validation & Safety Checklist

Use this checklist to validate AI/ML components used in operations so they deliver expected value while preserving safety, auditability, and trust. Fill in factual answers, link or paste evidence, and collect required sign-offs. This form is intended to be completed by the model owner with input from operations, safety, and IT/data teams. If any required item is unanswered or marked as 'Do Not Deploy' at the end, pause deployment and follow the incident/mitigation workflow.

Describe the business decision the model supports and the precise decision boundary (what the model can and cannot decide). Include where human judgment is required.
Provide 2–4 brief examples that illustrate correct usage and clear out-of-scope examples.
Estimate the operational risk if the model behaves incorrectly.
List the metrics used (accuracy, precision, recall, RMSE, false positive/negative rates, business KPIs) and baseline numbers from validation datasets.
Confirm validation used data representative of production inputs, including edge cases, and not just train/test splits.
Summarize validation approach (holdout, cross-validation, A/B), key results, known limitations, and paste links to reports, dashboards, or datasets.
Have quantitative thresholds been defined for safe operation?
List thresholds, monitoring triggers, and acceptable degradation ranges (e.g., precision > 0.85, false negative rate < 2%).
Confirm there is a plan to detect concept, data, or label drift as inputs or environment change.
Which metrics will be monitored (data distribution shifts, feature importance changes, performance decay) and how often will they be checked?
List runtime metrics to monitor (latency, throughput, error rates, model output distributions, key business KPIs). Include alert thresholds where appropriate.
Does the system log inputs, outputs, decisions, and contextual metadata for audit and incident investigation?
Provide a link or pointer to where logs are stored or how to access them.
Is a human reviewer included where required (e.g., for high-risk decisions)?
Describe quantitative or qualitative triggers that cause human review and the steps for escalation (who is notified, SLA for response).
Are model controls restricted to authorized roles? Is production behavior change protected by change control?
List roles with access, how new access is granted, and where the policy is documented.
Is there a documented plan to respond to incidents, revert model behavior, or disable automatic actions?
Link to the incident playbook or brief description of steps to contain and remediate issues.
Has privacy, data protection, and regulatory compliance been reviewed and approved?
Document key compliance constraints, data residency, consent, PII handling, or regulatory approvals.
Is each model version tracked and are changes subject to formal change control?
Provide link to model registry entry, CI/CD pipeline, or change ticket.
Select robustness and resilience tests performed.
Are there clear triggers (data drift, performance drop, periodic cadence) for retraining or redeploying the model?
Describe triggers and the responsible team for model updates.
Paste links to validation reports, dashboards, datasets, code repositories, or relevant artifacts.
Primary business owner responsible for operational behavior.
Operations owner confirms checklist is complete and acceptable.
Person responsible for safety or risk oversight.
Safety owner approval if applicable.
Owner responsible for infrastructure, logging, access control, and model lifecycle.
IT/Data owner confirms controls and monitoring are in place.
Any outstanding issues, planned mitigations, or follow-up items.
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