Testing supports governance only when later reviewers can understand what was actually tested, under which conditions, and what remained uncertain. A short evidence record can prevent a passing label from becoming more authoritative than the underlying evaluation.
Define the purpose of the test
State which behavior, failure mode, control, or requirement the evaluation is intended to examine. Connect the scope to the approved use case rather than testing the model in isolation.
Record representative scenarios
Document the inputs or scenario categories, environment, model or system version, relevant configuration, expected outcome, and method used to judge the result. Include difficult or adverse cases where they are relevant to the risk.
Keep failures and limitations visible
Record failed cases, uncertain results, missing data, known test limitations, and any conditions under which the evidence should not be generalized. Link unresolved findings to owners and follow-up work.
Identify who reviewed the evidence
Separate test execution from approval when independent review is needed. Record the reviewer, decision, date, and any deployment conditions or monitoring requirements.
Do not turn technical testing into professional certification
An internal test result does not establish legal compliance, clinical safety, financial suitability, employment fairness, accessibility compliance, or another regulated conclusion unless the appropriate qualified process supports that determination.
Related knowledge
Use this with AI Monitoring Thresholds, AI Model Change Reviews, AI Use-Case Inventories, and AI Governance Evidence Packs.
References
This resource supports operational governance. It does not establish legal compliance or replace qualified legal, regulatory, clinical, financial, employment, safety, privacy, accessibility, or other professional review.