Definition · Plain-language
AI audit
An AI audit is an independent assessment of an AI system against defined criteria such as bias, performance, governance and regulatory alignment.
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What an AI audit examines
An AI audit assesses a system against criteria agreed in advance. Common dimensions include fairness (whether outcomes are systematically skewed across groups), performance and robustness (accuracy and behaviour under edge cases or drift), transparency and documentation (model cards, data provenance, decision records), security, and alignment with relevant regulation or standards. The scope is defined at the outset: an audit may focus narrowly on a model’s bias, or broadly on the governance and management system surrounding it. Clear, measurable criteria are essential — without them an assessment cannot produce defensible conclusions.
Types and timing
Audits vary by independence and timing. An internal audit is carried out by a function separate from the development team; a third-party audit is performed by an external organisation for greater objectivity and external credibility. Timing also varies: a pre-deployment audit checks a system before it goes live, while periodic or continuous auditing monitors a live system for drift, degradation or emerging bias. The right combination depends on the system’s risk: high-impact systems typically warrant independent, recurring audits, whereas lower-risk tools may need only lighter internal review.
How audits fit governance
AI audit is one of the assurance mechanisms within AI governance. Whereas a framework defines the controls and a risk-management process identifies what could go wrong, an audit independently tests whether those controls actually work and whether the system behaves as claimed. Findings feed back into the manage and govern activities — closing gaps, updating documentation and informing leadership decisions. Management-system standards such as ISO/IEC 42001 expect internal audit as a built-in requirement, making auditing a recurring part of the improvement cycle rather than a one-off event.
Key facts
At a glance
- Definition: independent assessment of an AI system against defined criteria
- Common criteria: bias, performance, governance, regulatory alignment
- Independence: internal (separate function) or third-party (external)
- Timing: pre-deployment, periodic or continuous
- Output: evidence and findings supporting accountability
- Standards link: internal audit is a requirement of ISO/IEC 42001
Common misconceptions
What people often get wrong
Often heard: An AI audit is a single pass-or-fail test.
Actually: An audit assesses a system against multiple defined criteria and usually produces graded findings and recommendations, not a binary result. Its value is the evidence and gaps it surfaces, which feed back into governance.
Often heard: AI audits only check technical accuracy.
Actually: Audits commonly cover fairness, transparency, documentation, security and governance alongside performance. Many target the management system around a model, not just the model’s metrics.
Often heard: Once a system passes an audit, no further auditing is needed.
Actually: AI systems drift as data and context change, so audits are often periodic or continuous. A single point-in-time audit does not guarantee ongoing behaviour, especially for high-impact systems.
Going deeper
