What is an AI audit?

An AI audit is a structured, evidence-based examination of how artificial intelligence (AI) systems are designed, trained and deployed.

AI audits can be considered a comprehensive “health check” for AI systems. While a traditional audit focuses on financial statements or IT controls, an AI audit digs into the fundamentals of the entire AI lifecycle. This includes data collection and quality, model architecture and explainability, and the decision-making process once the system goes live.

This audit process evaluates whether an organization’s use of AI aligns with its governance framework, risk management framework and ethical standards. It asks questions such as:

• Are personal data and training data collected lawfully and stored securely?
• Do the algorithms introduce bias or create new vulnerabilities?
• Are access controls and metrics in place to mitigate risks over time?

An AI audit paints a clear picture of an AI system’s inner workings, establishing trustworthiness. For stakeholders and regulators, it provides tangible proof that this emerging technology is being used responsibly.

AI systems influence high-stakes decisions every day, such as approving mortgages, triaging patients and moderating social media content. While these systems deliver speed and automation, they can also introduce potential harms. A flawed model can reinforce bias; a weak security posture can trigger a data breach; misinformed decision-making can erode stakeholder confidence.

Regulators are responding. The Artificial Intelligence Act of the European Union (EU AI Act) defines several levels of risk—limited and minimal risk, high risk and unacceptable risk—and requires stringent auditing standards.

Similarly, the National Institute for Standards and Technology (NIST) AI Risk Management Framework sets expectations for AI transparency and accountability. Internal auditors and information security teams are expected to demonstrate compliance and show that their AI systems are trustworthy from design to deployment.

An AI audit, therefore, is both a defensive and a strategic tool. It can help organizations identify vulnerabilities before they escalate, strengthening data governance practices and initiatives. And by promoting clear documentation and repeatable workflows, AI audits help organizations meet regulatory compliance requirements and build trust among customers and investors.

A comprehensive AI audit examines three interdependent areas, each with its own set of tests and metrics:

• Data
• Model
• Deployment

Auditors assess how data is collected, labeled and managed. They review dataset accuracy and quality and test for hidden bias or “stale” data that could skew outputs. Privacy and data-protection controls are critical: personal data must meet General Data Protection Regulation (GDPR) or comparable standards, while metadata should be traceable across the entire AI lifecycle.

Typical checks include data-quality scoring and fairness assessment reviews that ensure retention periods, access controls and permissions are documented and enforced.

Auditors then probe the algorithms themselves. Key questions include: What machine-learning techniques are used? How explainable is the decision logic? Are metrics and thresholds defined to monitor drift or unexpected behavior?

They often run error-rate analyses across demographic groups, apply stress tests or conduct red teaming exercises to expose vulnerabilities. Reliability tests can also help identify potential harms before deployment.

Finally, auditors examine the operational environment. They verify that governance structures and monitoring workflows remain in place after launch.

Common evaluations include conformity assessments for regulatory compliance (for example, the EU AI Act), continuous real-time performance monitoring and incident response simulations to help ensure the organization can detect and contain emerging threats. Auditors may also confirm that outputs undergo human-in-the-loop review when necessary.

A growing number of laws and standards around the world are shaping AI auditing. Several are already in force, while many others are moving quickly toward implementation. Notable regulations include:

• The EU Artificial Intelligence Act: In the European Union, the EU AI Act has been in force since August 2024 and classifies AI systems by risk level, requiring extensive documentation, testing and human oversight for high-risk applications.

• AI Risk Management Framework: Developed by NIST in 2023, this voluntary framework guides organizations in identifying, assessing and managing AI risks across the entire lifecycle. While the executive order that directed federal agencies to institutionalize the framework has been rescinded, the standard itself continues to serve as guidance.

• Directive on Automated Decision-Making: Canada’s federal directive, effective since 2020, compels departments to complete an Algorithmic Impact Assessment and publicly report results for high-impact AI systems.

• Model AI Governance Framework: First released in 2019 and updated in 2024, Singapore’s framework provides detailed guidance on transparency, stakeholder engagement and human oversight. It’s being used by many private-sector organizations.

• AI Governance Guidelines: Updated in 2023, Japan’s guidelines call for risk assessments, explainability and independent third-party audits, emphasizing accountability across the AI lifecycle.

• AI Principles: Adopted by more than 40 members nations of the Organisation for Economic Co-operation and Development (OECD) since 2019, these non-binding principles promote fairness, transparency and robust security. They serve as a global reference point for emerging national regulations.

Several national frameworks are also emerging from countries including Brazil, the United Arab Emirates and South Korea. These initiatives advance AI governance bills and publish detailed guidelines that emphasize ethical use, privacy protections and stakeholder accountability.

Regulations at the industry level add another layer of accountability. In the US, healthcare organizations must comply with the Health Insurance Portability and Accountability Act (HIPAA), including its Privacy and Security Rules, which require strict safeguards for any patient data used in AI systems.

Financial institutions follow model-risk management standards such as the Federal Reserve’s SR 11-7 guidance and the Office of the Comptroller of the Currency’s model-validation requirements, both of which mandate independent testing and the ongoing monitoring of AI models.

Frameworks translate regulatory requirements into practical auditing standards. Organizations often blend elements from several frameworks to create an auditing approach tailored to their industry and regulatory environment.

Common frameworks include:

• COBIT: The Control Objectives for Information and Related Technologies (COBIT) framework provides a control-oriented approach that integrates AI auditing into enterprise IT governance and information security programs. This helps ensure consistent oversight across departments.

• COSO ERM: The Committee of Sponsoring Organizations of the Treadway Commission Enterprise Risk Management Framework (COSO ERM) helps organizations embed AI risk within broader risk-management strategies and board-level oversight—connecting emerging technology to corporate objectives.

• GAO AI Accountability Framework: The US Government Accountability Office (GAO) Artificial Intelligence Accountability Framework offers guidance for evaluating governance, data quality and performance monitoring, making it useful for both public and private sector audits.

• IIA AI Auditing Framework: The Institute of Internal Auditors (IIA) Artificial Intelligence Auditing Framework adapts traditional audit standards to the unique characteristics of AI systems, emphasizing ethical standards and transparency in decision-making.

• PDPC Model AI Governance Framework: The Singapore Personal Data Protection Commission (PDPC) Model Artificial Intelligence Governance Framework provides detailed guidance on stakeholder engagement and responsible data handling for organizations operating across multiple jurisdictions.

Effective AI auditing combines best practices with a disciplined, step-by-step roadmap. While there are myriad strategies for auditing AI, one approach could follow these steps: 

Create a governance structure that defines roles for audit teams, internal auditors, data scientists and business stakeholders. Engage the internal audit function as soon as AI projects are proposed, so that risk assessment and data governance measures are built in from the start.

Catalog every AI model in use or in development, including generative AI models, chatbots and automation tools. Document training data sources and data quality metrics to create the foundation for risk assessment and future monitoring.

Conduct a formal risk assessment that considers potential harms such as bias, data breaches or misuse of personal data. Validate data collection methods, verify privacy protections and ensure that datasets meet regulatory standards for metrics like retention and accuracy.

Choose the auditing frameworks that align with your industry and regulatory environment. For instance, some organizations blend COBIT’s control focus with COSO’s enterprise-risk view, then add GAO or IIA elements for accountability and PDPC for stakeholder transparency. Use auditing tools that support explainability testing, algorithmic auditing and real-time metrics.

Develop templates for evidence collection, access controls and reporting. Define metrics for model performance and mitigation strategies. Ensure that audit workflows include checkpoints for internal audit sign-off and stakeholder review.

Perform the audit, document findings and share results with executives and regulators as required. Establish continuous monitoring to track both data quality and operational risk. Metrics and dashboards can give audit teams real-time visibility into vulnerabilities and allow for quick mitigation.

Auditing disciplines are evolving in step with AI technologies. Looking ahead, organizations can expect to see:

Regardless of how the future unfolds, auditing is no longer a one-time compliance exercise. It is becoming a continuous, intelligent process that can help enterprises deploy AI technologies safely and with confidence.