When AI helps make a decision that affects someone, “the model said so” is not an acceptable record. This tool generates a structured audit-trail template that captures exactly what to log — model version, inputs, output, confidence, the human reviewer, and any override — so AI-assisted decisions can be reconstructed and defended.
How it works
You pick the decision type, the AI system, and the regulatory framework you operate under. The tool builds an audit record with the appropriate fields, including framework-specific additions (for example human-oversight and explainability fields for the EU AI Act, or automated-decision fields for GDPR). You fill in the specifics for a real decision and export the record as text or JSON for your audit store.
Why each field matters
The model version lets you tie a decision to a specific behavior if a model is later found faulty. The input summary and output allow reconstruction. The confidence indicator flags decisions that warranted closer review. The human reviewer and override rationale demonstrate meaningful human oversight — the difference between an accountable process and a black box.
Tips and notes
- Log enough to reconstruct the decision, but minimize duplicating sensitive data — a summary plus a reference to where the full input lives is often the right balance.
- Standardize one record shape across your organization so logs are queryable and comparable.
- Pair this with the Zero-Trust AI Access Policy so the systems producing these decisions are themselves governed.