AI Audit

An AI audit is a structured review process that evaluates an AI system across multiple dimensions: technical performance (accuracy, reliability, robustness), fairness (bias assessment across demographic groups), safety (vulnerability testing, harm potential), compliance (regulatory adherence, policy alignment), and governance (documentation, accountability, oversight mechanisms). Audits can be internal (conducted by the organisation's own teams) or external (performed by independent third parties). External audits provide greater credibility and objectivity, which is increasingly important for regulatory compliance and stakeholder trust. The audit process typically involves reviewing documentation (model cards, data lineage, development processes), testing the system (performance evaluation, bias testing, adversarial testing), interviewing stakeholders (developers, users, affected populations), and producing a report with findings, risk assessments, and recommendations.

AI audits are becoming a regulatory requirement. The EU AI Act mandates conformity assessments for high-risk AI systems. Financial regulators require model risk management. Employment discrimination laws require fairness assessments for AI hiring tools. Proactive auditing prepares organisations for these requirements. Beyond compliance, audits identify risks and improvement opportunities. They can uncover biases that have crept into production systems, performance degradation due to data drift, security vulnerabilities, and governance gaps. Addressing these issues proactively prevents costly incidents. Regular audits also demonstrate due diligence to customers, partners, and regulators. Organisations that can show they regularly audit their AI systems for safety and fairness build stronger trust and are better positioned when incidents occur or regulations change.