Tier 2: Evidence-grade controls

AI model validation and independent review

Independent confirmation that an AI model is fit for its intended purpose, performs as claimed, and meets control expectations before and after deployment.

Board-defensible evidence

• Validation plan defining scope, test methodology, acceptance criteria, and who performs validation versus who built the model, including the independence standard applied.
• Validation report documenting performance results, limitations, sensitivity analysis, stress testing, and known failure modes, with explicit sign-off by the independent reviewer and the accountable owner.
• Data and feature review evidence showing training and testing data provenance, representativeness concerns, leakage checks, and documented decisions about inclusion or exclusion of sensitive variables.
• Issue log and remediation tracker showing validation findings, severity ratings, corrective actions, retesting results, and the date the model was cleared for production use.
• Periodic re-validation triggers and records showing when validation must be repeated (drift, data changes, code changes, new populations, new geography), and who approved continuing operation.

Why this matters

When outcomes are challenged, validation records are how you prove the model was reviewed independently and approved based on evidence, not optimism.