AI Audit Trail

An AI audit trail is a tamper-resistant record of what an AI system actually did and why: the inputs it received, the knowledge it pulled up, the rules and tools it called on, the reasoning behind its output, and any human approval or override along the way. It exists so a compliance team, an auditor, or a regulator can go back and reconstruct a specific decision after the fact.

A log vs. a decision record

The distinction that actually matters is between a log and a decision record. A log just confirms a workflow ran at a given time. A decision record explains why the system put together the response it did: which agents or models it called, what they changed, and which alternatives it considered.

Under the EU AI Act, both providers and deployers of high-risk systems have logging obligations, so an audit trail is not just good practice, it is a legal requirement too. For predictive models, the trail also needs to cover lineage: which model version, trained on which data, actually produced the output.

Niyuk versions every model with its lineage and hyperparameters, logs each agent's reasoning steps and source inputs, and treats that record as a receipt attached to every action it takes.