Evidence Room export sample
A sanitized PDF that mirrors the evidence pack auditors expect to receive from an AI governance system.
Last updated: Dec 16, 2025 · Version v1.0 · Fictional sample. Not legal advice.
What this sample is for
Use it to align compliance, legal, and engineering on what evidence looks like.
Auditors and procurement teams do not evaluate AI systems by screenshots. They ask for evidence: a verifiable export that shows what ran, who approved it, and how data moved through the workflow. The Evidence Room sample is a sanitized PDF that mirrors the structure of a real export from KLA Digital. It includes a system summary, scope boundaries, key controls, and a manifest so reviewers can trace each claim back to logged events.
Use the sample to sanity-check your own documentation. Look for provenance details like policy versions, prompt versions, and model IDs, plus human oversight records (who approved, what context, timestamps) and integrity signals like hashes and checksums. A defensible export also documents data sources, retention policies, and redaction rules so auditors know what was excluded and why. These elements are what move a packet from marketing to evidence.
This sample is designed for regulated workflows such as credit, claims, KYC/AML, and HR. It highlights the minimum information reviewers expect: decision boundaries, risk tier, monitoring and sampling policy, escalation paths, and incident handling notes. If any of those are missing, regulators will push back or require follow-up. Treat the sample as a checklist to align internal teams on what must be captured at runtime.
Download the PDF to review the layout and the level of detail. If you need a full export that maps to your exact workflow, KLA can generate one from live telemetry, policies, and review trails. The sample is fictional and for orientation only. It is not legal advice and should be adapted with counsel for your jurisdiction.
What to look for
- Evidence manifest with checksums and export timestamps.
- Policy, model, and prompt version references.
- Human oversight records with rationale and context.
- Monitoring signals, thresholds, and sampling rules.
- Data lineage and retention guidance.
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