An AI audit tests claims against evidence. Organizations say their systems are safe, fair and compliant, and an audit asks whether the records, tests and controls actually support those statements. As regulators, customers and boards ask harder questions about AI, audit capability is becoming a core part of responsible AI programs. This guide explains what an AI audit is, the different types, how to plan and run one, what to examine for safety, fairness and compliance and how to report and follow up. Professionals who want a recognized foundation can explore the certified responsible AI expert certification. This guide is general information and not legal advice, and requirements vary by region and sector.

What is an AI audit?

An AI audit is a structured, evidence-based review of an AI system and the processes around it, carried out to determine whether the system meets defined criteria. Those criteria can come from laws and regulations, industry standards, contractual commitments or the organization's own policies and principles.

An audit is different from monitoring or testing, although it uses both. Monitoring is continuous and operational, testing measures specific properties of a model, and an audit independently evaluates whether the whole control environment works and whether the evidence supports the conclusions.

Types of AI audits

Audits differ in who performs them and what they examine.

Internal and external audits

Internal audit teams or risk functions provide ongoing assurance and can examine many systems. External auditors bring independence and specialist expertise, and their reports carry weight with regulators, customers and partners.

Technical and governance audits

A technical audit looks at data, models, testing and security. A governance audit examines policies, roles, approvals and records. Strong audits combine both, because a technically sound model can still be badly governed.

Pre-deployment and post-deployment audits

Pre-deployment audits check readiness before launch, while post-deployment audits examine real-world performance, drift and incident handling. High-risk systems usually need both.

Planning the audit

Good audits begin with scope and criteria. Define which system or process is in scope, its purpose and risk level, and the standards against which it will be assessed. Agree the timing, the access auditors need and the independence requirements. Gather the documentation up front, including system registers, data descriptions, model documentation, impact assessments, test results, approvals and incident logs. Clear scope prevents the audit from drifting and makes findings defensible.

What to examine for safety

Safety audits ask whether the system performs reliably and does not cause unacceptable harm. Auditors typically review the intended use and foreseeable misuse, testing under realistic and stressful conditions, robustness against unexpected inputs and the design of human oversight and fallback procedures. They also check security controls over data, models and access, and examine incident response plans. Evidence should show that issues found in testing were fixed or consciously accepted by an authorized person.

What to examine for fairness

Fairness audits look for unjustified differences in outcomes or errors across groups and for the processes that prevent them. Auditors examine how data was collected and whether it represents the people affected, which fairness measures were chosen and why, and what testing was done across relevant groups. They check whether results were reviewed by people with the right expertise, whether findings led to changes and whether complaints and appeals are tracked. Because fairness has no single definition, documentation of the reasoning matters as much as the numbers.

What to examine for compliance

Compliance audits compare practice with legal, regulatory and contractual requirements. Typical areas include data protection and permissions, transparency notices, record keeping, human oversight, vendor management and sector-specific rules. Auditors map each requirement to controls and evidence, and they identify gaps. Where requirements are unclear or changing, they note the interpretation used and who approved it.

Running the audit: methods and evidence

Auditors use several methods to build a reliable picture. These commonly include:

Document review of policies, assessments, approvals and logs
Interviews with developers, owners, risk staff and users
Independent testing or re-performance of key tests
Sampling of decisions and cases to check how the system behaves in practice
Review of vendor documentation and contract terms

Findings should rest on evidence that is complete, relevant and traceable. Tamper-evident records strengthen the evidence base, and professionals interested in technologies for integrity and traceability can explore deep tech certifications.

Auditor competence and independence

Audit quality depends on the people performing it. Auditors need knowledge of AI, data, risk and the relevant rules, and they need independence from the team that built the system. Organizations should check the qualifications and background of internal and external auditors, and verification services such as HireValid support credential and background checks. Technical literacy helps auditors probe model behavior, and some professionals build it through tech certifications.

Reporting and follow-up

An audit report should state scope, criteria, methods, findings, ratings and recommendations in plain language. Findings should be ranked by severity, with management responses, owners and deadlines. Track actions to closure and verify that fixes work. Share summaries with the governance body and senior leaders, and use recurring findings to improve policies, templates and training.

Using audits in external communication

Audit results can support trust, but they must be described accurately. Say what was audited, by whom, against which criteria and when, and avoid implying that an audit certifies a system as safe for every use. A digital marketing course can help communication teams present assurance claims clearly and within legal and ethical limits.

Conclusion

An AI audit gives organizations and their stakeholders evidence about whether AI systems are safe, fair and compliant. Audits may be internal or external, technical or governance-focused, and carried out before or after deployment. Good audits start with clear scope and criteria, examine safety, fairness and compliance with appropriate methods, rely on traceable evidence and are carried out by competent and independent auditors. Reports must lead to tracked actions, and public claims must reflect what was actually audited. Check which standards and rules apply in your region and sector before you begin.

Frequently asked questions

These are the questions people ask most about auditing AI.

1. What is an AI audit?

A structured, evidence-based review of whether an AI system and its processes meet defined criteria.

2. How is an audit different from testing?

Testing measures specific properties, while an audit evaluates the whole control environment and the evidence behind conclusions.

3. Why do we need AI audits?

They give assurance to leaders, regulators, customers and partners that claims are supported.

4. Who performs AI audits?

Internal audit or risk teams, or independent external auditors.

5. When should we audit?

Before deployment, after deployment and on a regular schedule, with more often for high-risk systems.

6. What is a technical audit?

A review of data, models, testing and security.

7. What is a governance audit?

A review of policies, roles, approvals, records and oversight.

8. What criteria are used?

Laws, regulations, standards, contracts and internal policies.

9. What documents are needed?

System register, data and model documentation, assessments, test results, approvals and incident logs.

10. What does a safety audit check?

Reliability, robustness, security, human oversight, fallback procedures and incident response.

11. What does a fairness audit check?

Data representativeness, fairness measures, testing across groups, review of results and complaint handling.

12. What does a compliance audit check?

Whether practice meets legal, regulatory and contractual requirements, with evidence for each.

13. What methods do auditors use?

Document review, interviews, independent testing, sampling and review of vendor information.

14. Can an audit certify that AI is safe?

No. It assesses against defined criteria and scope, and does not guarantee safety for every use.

15. How independent should auditors be?

Independent from the team that built or operates the system.

16. How do we check auditor credentials?

By verifying qualifications, experience and background, often with verification services.

17. What goes in an audit report?

Scope, criteria, methods, findings, severity ratings and recommendations.

18. What happens after the audit?

Management responds, owners fix issues by deadlines and progress is tracked to closure.

19. Can we audit vendor AI?

Often through contract rights, documentation review and third-party assurance reports.

20. How should we describe audit results publicly?

Accurately, stating what was audited, by whom, against which criteria and when.