What Auditors Will Look For in an ISO/IEC 42001 Audit

Feb 19

A Practical Whitepaper for Organizations Implementing AI Governance

As artificial intelligence becomes embedded across business operations, regulators, customers, and stakeholders are demanding greater transparency and accountability around how AI systems are governed. ISO/IEC 42001, the world’s first international standard for Artificial Intelligence Management Systems (AIMS), provides a structured, auditable framework to manage AI risk while enabling responsible innovation.

For many organizations, however, ISO/IEC 42001 is new territory. One of the most common questions we hear is simple but critical:

“What Will Auditors Actually Look For?”

This whitepaper demystifies the ISO/IEC 42001 audit process. Rather than focusing on theoretical requirements, it outlines the practical evidence, governance structures, and controls auditors expect to see, so organizations can prepare with confidence and avoid common pitfalls.

1. Understanding the Purpose of an ISO/IEC 42001 Audit

ISO/IEC 42001 audits are often misunderstood, particularly by organizations encountering formal AI governance requirements for the first time. Unlike technical assessments or model performance reviews, an ISO/IEC 42001 audit evaluates whether an organization has established and operates an effective Artificial Intelligence Management System (AIMS). At its core, the audit assesses governance, not algorithms.

Auditors are not judging how sophisticated, accurate, or innovative your AI systems are. Instead, they are evaluating whether your organization has:

Clearly defined how and where AI is used
Identified and assessed AI-related risks
Implemented proportionate controls to manage those risks
Established accountability, oversight, and decision-making structures
Embedded continuous monitoring and improvement into AI operations

The audit is therefore evidence-driven and risk-based. Auditors seek assurance that AI-related decisions are intentional, documented, and aligned with organizational objectives and risk appetite.

Organizations that approach ISO/IEC 42001 as a governance framework rather than a technical compliance exercise tend to achieve more consistent outcomes, smoother audits, and greater long-term value from certification.

2. Defining the Scope of the AI Management System (AIMS)

Defining a clear scope for your AI Management System is a critical first step in preparing for an ISO/IEC 42001 audit. Auditors will focus on whether your organization has identified and documented all relevant AI activities, ensuring that governance and controls are applied consistently.

Key elements auditors expect to see include:

A documented AIMS scope statement that outlines which AI systems, processes, and activities are in scope.
Identification of AI systems in development, deployment, or operation.
Inclusion of both internally developed AI and third-party AI tools or platforms.
Clearly defined boundaries of in-scope and out-of-scope AI activities.

A common gap in early implementations is underestimating AI usage. This can include informal or shadow AI tools used by employees, such as generative AI platforms, embedded AI within SaaS tools, or low-code AI applications. Auditors will look for evidence that the organization has made a reasonable effort to discover and document all AI activity.

Establishing a well-defined scope ensures that your AI governance, risk assessments, and controls are focused, relevant, and auditable, laying the foundation for a successful ISO/IEC 42001 audit.

3. Alignment With Established Management System Standards

ISO/IEC 42001 is designed to integrate with existing management system standards rather than operate in isolation. Auditors will look for evidence that AI governance is aligned with broader organizational frameworks for risk, security, and privacy.

In practice, this means organizations that already operate standards such as ISO/IEC 27001 or ISO/IEC 27701 can leverage existing governance structures, including risk management methodologies, internal audit processes, management reviews, and corrective action mechanisms, to support the Artificial Intelligence Management System (AIMS).

Organizations that align ISO/IEC 42001 with established management systems typically experience:

Reduced duplication of controls and documentation.
More consistent risk evaluation and decision-making.
Clearer accountability across security, privacy, and AI governance domains.
More efficient audits due to shared evidence and traceability.

Auditors recognize and value this integrated approach, as it demonstrates that AI governance is embedded within the organization’s overall management system rather than treated as a standalone or experimental initiative.

4. AI Risk Management: The Core of the Audit

AI risk management is the centerpiece of ISO/IEC 42001 and a primary focus for auditors. It goes beyond general IT or operational risk management by addressing the unique risks associated with AI systems.

Auditors evaluate whether your organization has established a systematic, evidence-based approach to identify, assess, and manage AI-specific risks. They will look for:

Identification of AI-related risks across all systems in scope.
Assessment of likelihood and impact for each risk.
Documented decisions on risk treatment, including mitigation, acceptance, or transfer.
Ongoing monitoring and review of risk status and effectiveness of controls.

Common types of AI risks that auditors examine include:

Bias and fairness: Are outputs equitable and free from unintended discrimination?
Data quality and integrity: Are datasets accurate, complete, and representative?
Security and privacy: Are systems protected from unauthorized access or data leaks?
Regulatory and legal exposure: Are applicable laws, standards, and contractual obligations considered?
Ethical and reputational: Could the AI system cause harm or negatively impact stakeholders?

It is important to note that auditors do not expect risk elimination. The goal is to demonstrate a structured, proportionate, and defensible approach to managing AI risks.

Organizations that effectively integrate risk management into the AIMS, document their methodology, and demonstrate continuous monitoring are better positioned for a smooth audit and can derive greater operational and strategic value from ISO/IEC 42001.

5. Risk Proportionality and Context-Aware Governance

A core principle of ISO/IEC 42001 is that governance controls must be proportionate to AI risk. Auditors do not expect all AI systems to be governed uniformly. Instead, they assess whether the level of oversight reflects the potential impact, complexity, and context of each AI use case.

Auditors will expect enhanced governance for AI systems that:

Influence or automate decisions affecting individuals, customers, or employees.
Process sensitive, personal, or regulated data.
Rely on complex, opaque, or rapidly evolving models.
Carry legal, regulatory, ethical, or reputational implications.

Lower-risk AI systems, such as narrowly scoped internal tools or limited automation, may be governed through streamlined controls, provided this approach is supported by documented risk assessments and management decisions.

Demonstrating risk-proportionate governance signals that the organization understands its AI landscape, applies oversight intentionally, and can clearly justify why different AI systems are governed differently. This approach aligns with evolving regulatory expectations and provides auditors with a defensible, evidence-based rationale for governance decisions.

6. Governance Across the AI Lifecycle

ISO/IEC 42001 requires organizations to implement effective governance throughout the entire AI lifecycle. Auditors will assess whether controls and oversight are applied consistently from conception to retirement of AI systems.

Key areas auditors examine include:

Design and development: Governance structures that ensure AI models and systems are built with risk, compliance, and ethical considerations embedded from the outset.
Testing and validation: Verification processes for accuracy, fairness, reliability, and alignment with intended use cases.
Deployment and release: Controls to ensure AI systems operate as intended and are appropriately monitored during rollout.
Monitoring and performance evaluation: Ongoing oversight to track performance, detect anomalies, and measure outcomes against objectives.
Change management and retirement: Structured processes for updating, decommissioning, or replacing AI systems responsibly.

Auditors expect these controls to be proportionate to the associated risk. Low-risk AI systems should have streamlined governance, while high-risk systems require more comprehensive oversight. Demonstrating lifecycle governance helps ensure the organization can respond to incidents, adapt to changes, and continuously improve its AI management practices. These lifecycle controls operate in support of the organization’s AI risk management framework described in Section 3.

7. Policies, Procedures, Documentation, and Records Auditors Commonly Request

An ISO/IEC 42001 audit is evidence-driven. Auditors will assess not only whether controls exist, but whether they are documented, implemented, and operating effectively. Organizations should be prepared to provide a range of documented information that demonstrates the design and ongoing operation of the Artificial Intelligence Management System (AIMS). While the exact documentation requested will vary based on scope and risk profile, auditors commonly review the following:

Core Governance and Organizational Documentation

A Statement of Applicability (SoA) identifying applicable controls and justifications for inclusion or exclusion.
An organization chart and job descriptions for relevant roles, clearly defining responsibilities related to AI governance, risk management, and oversight.
A high-level description of the organization, including key business processes and how AI supports or influences them.
High-level details of IT infrastructure, including platforms, environments, and dependencies supporting AI systems.

Third-Party and Outsourced Services

A list of approved providers of outsourced services, including subcontractors that support or deliver AI-related capabilities.
Evidence of third-party risk considerations where AI services, models, or data are sourced externally.

Risk Management and Control Evidence

The AI risk assessment methodology, including criteria for likelihood, impact, and risk evaluation.
Completed AI risk assessments covering all in-scope systems.
A documented Risk Treatment Plan, outlining mitigation actions, ownership, and timelines.

Monitoring, Review, and Assurance

Internal audit programs and reports covering the AIMS.
Management review records, demonstrating oversight, performance evaluation, and decision-making.
Reports from independent reviews relevant to information security, privacy, or AI governance, where applicable.

Compliance, Incidents, and Corrective Actions

Records supporting the organization’s internal assessment of compliance with regulatory and legal requirements, including: Incident records, Breaches of regulation or legislation, Relevant correspondence with regulators or authorities.
Documentation of internally identified nonconformities, along with evidence of corrective and preventive actions taken within the previous 12 months (or since system implementation, if shorter).

Auditors do not expect documentation to be excessive or overly complex. However, they do expect it to be complete, consistent, and aligned with the organization’s defined scope and risk profile. Well-structured documentation enables auditors to trace decisions, verify controls, and gain confidence that AI risks are being managed intentionally and responsibly.

8. Monitoring, Metrics, and Incident Management

Effective AI governance requires more than policies on paper - it demands continuous oversight, measurement, and responsiveness. Auditors look for evidence that organizations are actively tracking AI system performance, identifying issues early, and embedding lessons learned into governance practices.

Key Elements Auditors Expect to See

Proactive Monitoring and Metrics: Establish measurable indicators for AI system performance, risk exposure, fairness, and compliance. Regularly review results to detect anomalies and emerging risks.
Management Oversight and Review: Document periodic reviews by leadership or governance committees. Use these sessions to assess the effectiveness of controls, approve improvements, and ensure alignment with organizational objectives.
Incident Detection and Response: Implement structured processes to identify, report, and address AI-related incidents. Ensure clear escalation pathways and responsibilities.
Organizational Learning: Capture insights from incidents, near-misses, and monitoring findings. Adjust policies, controls, and risk assessments to prevent recurrence and enhance system reliability.

By integrating monitoring, metrics, and incident management into a continuous improvement loop, organizations not only meet ISO/IEC 42001 audit expectations but also demonstrate a mature, risk-aware AI governance culture. This approach provides tangible evidence that governance is operational, proactive, and evolving with organizational needs.

9. Conclusion: Preparing with Confidence

Successfully preparing for an ISO/IEC 42001 audit requires more than producing policies or assembling documentation. It requires demonstrating that AI governance is intentional, operational, and embedded within the organization’s existing management systems.

Organizations that take a structured approach to defining scope, managing AI risk, governing the AI lifecycle, maintaining evidence, and monitoring performance are better positioned to meet audit expectations and respond effectively to scrutiny. When governance mechanisms are aligned with established standards and applied proportionately to risk, audits become more efficient, findings are reduced, and certification outcomes are more consistent.

More importantly, a well-implemented Artificial Intelligence Management System enables organizations to make informed, defensible decisions about AI use. Rather than slowing innovation, effective AI governance provides the clarity, accountability, and confidence needed to deploy AI responsibly in an increasingly complex regulatory and risk environment.

By approaching ISO/IEC 42001 as a management system, not a documentation exercise, organizations can demonstrate trustworthiness to regulators, customers, and stakeholders while building a foundation for sustainable, responsible AI adoption.

If you’re exploring ISO/IEC 42001, we invite you to learn more about MHM’s ISO 42001 certification services and how we support organizations through the process.