For Heads-of · Practitioner
Audit Trail
A complete, timestamped record of the actions an AI system took and the decisions made with it, so that they can be reviewed, explained and attributed afterwards.
- audit trail
- logging
- accountability
- compliance
Contents
The input the model received, the model and prompt version used, the output generated, any confidence score, and when and by whom the decision was made or reviewed. For an agent, also which actions it took and on whose authority.
Purpose
An audit trail makes it possible to investigate a single decision, find systemic issues across many, and answer a complaint, a regulator or a court with evidence rather than reconstruction.
Governance requirement
The EU AI Act requires high-risk systems to allow automatic logging of events over their lifetime (Article 12), and the UK GDPR accountability principle expects an organisation to be able to demonstrate how personal data was used in a decision. AI system monitoring and logging is the control that produces the record.
Related Terms
Accountability
The principle that individuals and organisations can be held responsible for decisions made by the AI systems they deploy, and must be able to explain those decisions to the people affected.
Model Monitoring
Continuous observation of a deployed model's inputs, outputs, performance and behaviour, to detect degradation, drift, misuse or security anomalies.
Model Versioning
The practice of recording which model, trained how and on what, was deployed when, enabling comparison, rollback and reproducibility. For systems built on models the organisation did not train, it extends to versioning the surrounding assembly, since that is the only part it controls.
Transparency
The disclosure of information about an AI system to the people who need it: that AI is being used, what it does, what it is for and what risks it poses, in terms they can understand. For people whose personal data it uses, being informed is also a legal right.