Purpose
Provide an independent, documented assessment of every model’s reliability, fairness, security, and compliance posture before it is submitted for release approval.
Scope
Covers all models classified as moderate or high risk, including vendor models embedded into critical processes. Low-risk models must still meet minimum validation requirements defined in this policy.
- Credit, fraud, healthcare, HR, safety, and other regulated use cases
- Generative systems influencing customer messaging or legal disclosures
- Prompt libraries driving customer-facing conversational AI
- Third-party models where we are responsible for model governance
Definitions
- Independent Validator: Qualified reviewer not involved in model development, appointed by the Validation Lead.
- Validation Package: Set of artifacts (test results, bias audits, threat models) required for release approval.
- Risk Tiering: Categorization of models (low, moderate, high) based on impact, data sensitivity, and regulatory exposure.
Policy
Every model must pass the validation gates defined for its risk tier. Validation evidence must demonstrate that key requirements (accuracy, robustness, fairness, explainability, security) achieve target thresholds. The Validation Lead issues a signed validation report which becomes part of the release request.
Roles and Responsibilities
Independent Validation Lead owns the methodology, assigns validators, and approves final reports. Responsible AI team defines fairness/bias thresholds. Security team contributes adversarial testing expertise. Model Owners remediate findings before requesting approval.
Procedures
Validation follows the steps below:
- 1. Intake: Gather model documentation (architecture, training data summary, intended use, safeguards).
- 2. Risk confirmation: Validate risk tier and ensure scope matches regulatory mappings.
- 3. Functional testing: Reproduce developer-reported metrics and run challenger tests to detect regression.
- 4. Fairness and ethics testing: Execute bias audits, disparate impact analysis, and red teaming for safety harms.
- 5. Stress and robustness testing: Perform data perturbation, adversarial prompts, and infrastructure failure simulations.
- 6. Security review: Validate secret management, dependency hygiene, and prompt injection defenses.
- 7. Report & remediation: Document findings, risk ratings, required fixes, and final pass/fail attestation.
Exceptions
Validation scope reductions must be approved by the Validation Lead plus the Responsible AI representative. An exception plan must describe which tests were deferred, compensating monitoring, and target completion dates.
Review Cadence
Validation methodologies and risk tier thresholds are reviewed twice per year, or sooner if audit/regulator feedback warrants updates. Validation effectiveness metrics (number of findings by severity, time to remediation) are presented to the governance council.
References
- EU AI Act Annex IV (Technical documentation and testing)
- NIST AI RMF Measure function
- Internal documents: Validation Playbook, Responsible AI Evaluation Checklist, Security Red Team SOP