What it means
An AI change evidence package assembles the artifacts needed to understand, approve, reproduce, and later audit a model, data, prompt, configuration, or integration change.
Why it matters
AI behavior may shift because of several linked changes, so a ticket title and code diff rarely provide enough context for risk decisions.
Practical focus
- Define the system boundary, accountable owner, approved purpose, material risks, and decision authority for AI Change Evidence Package
- Require change purpose, affected components, risk assessment, lineage, test results, comparison to baseline, approvals, deployment plan, rollback plan, and monitoring updates.
- Collect and protect this evidence: diffs, hashes, data references, evaluation outputs, approvals, release identifiers, deployment logs, and post-change validation.
- Exercise this failure scenario as a planned test: a model update is approved without documenting a simultaneous data-filter change that drove the observed improvement.
Common mistakes
- Using the label AI Change Evidence Package without defining the exact systems, data, people, decisions, and time period in scope
- Treating a policy statement or tool demonstration as proof that AI Change Evidence Package operates consistently in production
- Failing to retain diffs, hashes, data references, evaluation outputs, approvals, release identifiers, deployment logs, and post-change validation in a form that lets an independent reviewer reconstruct decisions and changes
- Testing only the normal path and never rehearsing what happens when a model update is approved without documenting a simultaneous data-filter change that drove the observed improvement.
Certification relevance
This subject appears in or supports the following certification bodies of knowledge: