An artificial intelligence audit gathers evidence about how a system produced a result. Reviewers may inspect data preparation, model versions, access controls, decision records, infrastructure and the reproducibility of earlier outputs.
High-stakes audits need durable evidence because a decision may be challenged years later. The process should preserve provenance, state and control history while protecting sensitive information and stating the limits of what the audit can establish.
