An auditable AI decision is one whose full record, its inputs, reviewers, and rationale, can be produced and independently verified on demand.
Auditability is defensibility made routine. A decision is auditable when its trail is not assembled in a panic after a challenge, but captured automatically as the decision is made.
The AI audit trail
An AI audit trail records, for each AI-influenced decision: the information that fed it (provenance), what the model recommended, who reviewed and approved it, the authority under which it was acted on, and which controls applied. This is the operational core of audit readiness, the fourth pillar of DIG.
The EU AI Act requires logging and record-keeping for high-risk systems; ISO/IEC 42001 requires audit; TRAIGA requires documentation. An organization with a standing AI audit trail satisfies all three from one discipline.[3]
Frequently asked questions
What is an AI audit trail?
A record, captured per decision, of the information used, the recommendation made, the human review, the authority to act, and the controls applied, sufficient to reconstruct and verify the decision later.
What regulations require auditable AI?
The EU AI Act (logging/record-keeping for high-risk systems), ISO/IEC 42001 (audit requirements), and Texas's TRAIGA (documentation) all point toward auditable AI decisions.
References
- NIST AI Risk Management Framework (AI RMF 1.0): Govern, Map, Measure, Manage. National Institute of Standards and Technology, 2023. View source ↗
- Information governance: the records and data lifecycle discipline (storage, retention, disposition), distinct from AI decision governance. ARMA International, Generally Accepted Recordkeeping Principles; AIIM. View source ↗
- EU AI Act, Regulation (EU) 2024/1689 (Official Journal of the European Union); ISO/IEC 42001:2023; Texas Responsible AI Governance Act (TRAIGA). View source ↗
- USPTO Trademark Reg. No. 99559923, Digital Information Governance / DIG, owner Matthew Bertram. View source ↗