Iso 42001 Complete Guide

ISO/IEC 42001 Policies Related to AI: Guidance & Best Practices

Organizations seeking ISO/IEC 42001 certification must establish robust policies related to AI.

These policies set the tone for AI governance by providing management direction and support for all AI systems in line with business needs.

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ISO/IEC 42001

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A.2 / B.2 – Policies related to AI

In this guide, we break down the requirements of ISO 42001’s AI policy controls and offer implementation best practices. The Policies Related to AI domain in ISO 42001 comprises three key controls: developing an AI policy, aligning it with other organizational policies, and reviewing the AI policy. Adopting these controls helps IT and compliance professionals ensure AI is used responsibly, ethically, and in compliance with all obligations.

A.2.1 / B.2.1 – Objective: Management Direction for AI Systems

To provide management direction and support for AI systems according to business requirements.

In essence, top management should steer the organization’s AI efforts in line with its strategic goals, risk appetite, and values. This objective underpins all AI-related policies and ensures that AI systems are not developed or used in a vacuum. Instead, AI activities should be guided by clear principles from leadership and embedded into the organization’s overall strategy and governance.

A.2.2 / B.2.2 – Establishing an AI Policy (Control 2.2)

The organization should document an AI policy for the development or use of AI systems. This AI policy is a formal, written policy that outlines how the organization governs AI. It is the cornerstone of AI governance, setting the rules and expectations for all AI-related activities.

Implementation Guidance & Best Practices

When crafting an AI policy, consider the following key factors and include essential elements:

  • Alignment with Strategy and Values: Ensure the AI policy is informed by your business strategy, organizational values, and culture. It should explicitly state how AI initiatives support business objectives (e.g. enhancing customer service, optimizing operations) and reflect the amount of risk your organization is willing to accept with AI. The policy must align with your risk appetite – for example, some organizations may be open to innovative, experimental AI projects, while others may restrict AI use to well-understood applications. Clearly tie the policy to strategic goals and include a statement of commitment from top management.
  • Risk Assessment and Scope: Account for the level of risk posed by AI systems in your organization. AI systems vary – some may be low-risk (such as an AI sorting internal documents), while others carry high stakes (like AI in medical diagnostics or financial decisions). Identify what AI systems and uses are in scope of the policy. High-risk AI applications might warrant stricter controls or require specific approval. The policy should mandate that AI projects undergo risk assessments (e.g. an AI system impact assessment prior to deployment). 
  • Legal and Regulatory Requirements: Your AI policy must comply with applicable laws, regulations, and contractual obligations. Inventory the laws relevant to AI in your industry and regions of operation – for example, data protection laws (GDPR, CCPA), sector-specific AI guidelines, or emerging AI regulations. If you have contracts that impose requirements on AI (for instance, client agreements stipulating no AI-generated decisions without human review), incorporate those. The policy should commit to ethical AI principles and non-discrimination to meet any forthcoming AI laws. Keeping the policy updated with the legal landscape will help avoid compliance violations.
  • Impact on Stakeholders: Consider the impact to interested parties. AI can affect customers, employees, partners, and society. Involve relevant stakeholders when developing the policy – for example, your customer privacy team for AI that uses personal data, or HR if AI is used in hiring. The policy should state that AI systems will be designed and used with respect for stakeholder rights and expectations (such as privacy, transparency, and fairness). It’s wise to reference mechanisms for stakeholders to raise concerns or provide feedback on AI systems (which ties into other controls like reporting of AI concerns).
  • Policy Principles: Define core principles that guide all AI activities in your organization. Common principles include: Transparency (AI decisions should be explainable and auditable), Fairness (mitigate bias and ensure equitable outcomes), Safety & Security (AI systems must be safe and secure against threats), Accountability (assign clear responsibility for AI outcomes and compliance), and Privacy (protect personal data and use it ethically). These high-level principles set the tone for your AI governance and demonstrate commitment to responsible AI. For example, your policy might declare that the organization strives to implement AI in an ethical, transparent, and human-centric manner.
  • Roles and Responsibilities: The AI policy should assign or reference roles responsible for AI governance. Specify who approves the AI policy (e.g. the CEO or Board), who is responsible for enforcement (perhaps an AI governance committee, CISO, or compliance officer), and who must follow the policy (all staff, contractors, etc.). This clarity ensures everyone knows their accountability in managing AI.
  • Handling Deviations and Exceptions: Include a defined process for policy exceptions. Despite a comprehensive policy, there may be scenarios where an AI project needs a waiver or a deviation from a requirement. The policy should state how to request an exception, who can approve it, and how those exceptions are documented and monitored. For instance, an AI development team might request a temporary exception if a specific tool doesn’t fully meet a guideline – the policy might require them to justify the risk and obtain formal approval from the AI governance committee. Having this process ensures deviations are controlled and not done secretly.
  • Integration with Processes: Your AI policy should not sit in isolation – it should integrate with existing organizational processes for technology management. Ensure it references related procedures like project management, change management, information security, and procurement. For example, the policy might require that AI systems go through the standard IT change control process and security testing before deployment, or that procurement of third-party AI services must include a vendor risk assessment. Integration means AI governance becomes part of business-as-usual operations.

In some cases, the AI policy can be high-level and refer out to more detailed policies or procedures on specific topics. ISO 42001 suggests considering topic-specific aspects where additional guidance may be needed. For example, your organization might maintain separate policies or sections for:

  • AI Resources and Assets Management – Guidelines for inventorying and managing AI assets (data sets, models, computing resources, etc.). The AI policy can require maintaining an AI asset register, but details might reside in an asset management procedure. (See ISO 42001 Control 4.2: Resource Documentation for how to document AI system components and assets in an inventory.)
  • AI System Impact Assessment – A process to evaluate potential impacts of AI on individuals and society before deployment. The AI policy might state that an impact assessment is mandatory for all new AI systems, and reference a procedure for conducting these assessments. (For guidance, see ISO 42001 Control 5.2: AI System Impact Assessment Process, which details how to systematically assess and document AI risks and effects.)
  • AI System Development Lifecycle – Standards or procedures for AI model development and testing (similar to an SDLC but for AI). This could include requirements for data quality, model validation, bias testing, and documentation at each stage. The AI policy can tie into broader AI system lifecycle controls (like those in ISO 42001 section A.6) to ensure that AI development follows responsible practices (e.g. peer review of models, adherence to coding standards, etc.). For instance, ISO 42001 Control 6.1 provides management guidance on responsible AI system development, which your policy might reference for specific development guidelines.

All relevant organizational policies (such as IT security policies, data governance policies, procurement policies) should be reviewed and updated to include AI considerations as needed. Likewise, the AI policy itself should guide the development, purchase, operation, and use of AI systems. This means whenever your teams are building a new AI tool, buying an AI product from a vendor, running an AI system in production, or using an AI service, they must consult and follow the AI policy and any related policies. Embedding these expectations in everyday processes will help make AI governance an integral part of your operations.

For a detailed breakdown of what an AI policy should contain and how to build one, see our dedicated page on the ISO 42001 AI Policy (Control 2.2). There, we offer a comprehensive list of AI policy elements and examples of policy statements that align with best practices.

A.2.3 / B.2.3 – Aligning AI Policy with Other Organizational Policies (Control 2.3)

The organization should determine where other policies (existing organizational policies) can be affected by or apply to the organization’s objectives with respect to AI systems. In simpler terms, you need to analyze and ensure that your AI objectives and policy do not conflict with, and are adequately supported by, your organization’s broader set of policies. Conversely, you should update other policies to account for AI, where appropriate, so everything remains consistent and aligned.

Why this is needed

Your company likely already has policies for information security, data privacy, corporate ethics, product safety, IT operations, human resources, etc. AI systems can introduce new considerations or stress points in all these areas. Control 2.3 is about harmonizing AI governance with existing governance. This avoids situations where, for example, your AI policy says one thing but an IT security policy says another, or your AI team operates without considering the privacy policy. Alignment ensures no policy gaps or contradictions in how AI is handled across the organization.

Implementation Guidance & Best Practices

 Achieving policy alignment involves a thorough review and coordination effort:

  • Identify Intersecting Domains: Start by listing out all relevant organizational policies and domains that AI might impact. Common areas include:
    • Quality Management: If you have a quality policy or ISO 9001 procedures, consider how AI outputs are evaluated for quality. AI models affecting product/service quality must meet the same standards.
    • Information Security: Your security policies (like those following ISO 27001) should incorporate AI-specific threats. For instance, add provisions for AI cybersecurity (protecting AI models and data from attacks such as data poisoning or model theft) to your security policies. Ensure AI systems adhere to access control, incident response, and encryption policies just like other IT systems.
    • Privacy and Data Protection: Align with privacy policies (GDPR, etc.) by addressing how AI systems use personal data. Your AI policy might require privacy impact assessments for AI, or enforce data minimization and anonymization in AI training data. Make sure your data protection policy explicitly covers AI use of personal data (e.g. no AI model is trained on personal data without legal basis and consent).
    • Safety and Ethics: If you operate in sectors like healthcare, automotive, or any safety-critical field, your safety policies must include AI considerations. For example, if an AI system could affect patient safety or physical safety, integrate those requirements (testing, fail-safes, human override mechanisms) into both the AI policy and existing safety protocols. If you have an ethical conduct policy or corporate social responsibility guidelines, ensure AI ethics (fairness, avoiding bias, human oversight) are consistent across both.
    • IT Governance and Change Management: Incorporate AI into IT change management, project management, and procurement policies. For instance, update your procurement policy to evaluate AI suppliers for compliance and ethics (linking with third-party risk management controls), or update SDLC documentation standards to handle AI model documentation.
  • Policy Mapping and Gap Analysis: Perform a policy mapping exercise to see exactly where AI topics intersect with existing policies. For each existing policy, ask: “Does this policy mention AI or is it affected by AI use?” If an intersection exists, determine if the current policy is sufficient or needs an update. For example, your Incident Response Policy should cover incidents involving AI systems (like an AI malfunction or breach affecting AI training data). If it doesn’t, that’s a gap to fill. The outcome of this analysis might be a set of updates to various policies and procedures, or creation of new ones, to fully cover AI-related scenarios.
  • Update and Integrate: Based on the analysis, update other policies to reflect AI considerations. In some cases it’s a small addition (e.g. adding “including AI systems” to scope statements, or adding specific AI risk examples in a risk management policy). In other cases, more substantial changes might be needed (e.g. expanding a data governance policy to include managing datasets used for machine learning, with rules on data bias and quality). Where policies are heavily impacted by AI, coordinate with the policy owners and subject matter experts to rewrite sections as needed. Ensure these changes are approved through the usual governance channels so they carry authority.
  • AI Policy Cross-References: Conversely, your AI policy can reference existing policies rather than duplicating rules. For instance, instead of writing a full data security section in the AI policy, it can say “All AI systems must comply with the company’s Information Security Policy and Data Protection Policy.” This makes it clear that general policies apply to AI systems too. Cross-referencing ensures consistency – readers of the AI policy know to consult other policies for detailed controls (like how to handle data or access). It also reinforces that AI isn’t separate; it’s part of the enterprise processes.
  • Holistic Governance and Oversight: Establish a governance mechanism to maintain alignment. Many organizations form an AI governance committee or assign the task to an existing risk or compliance committee. This group should include stakeholders from various domains (IT security, legal/privacy, risk management, operations, etc.) to regularly review how AI activities fit into the big picture. They can oversee updates to policies across departments. Additionally, the board of directors or top governing body should be aware of how AI governance is integrated. Notably, ISO/IEC 38507:2022 provides guidance for boards on the governance implications of AI use. It helps leadership ensure that organizational policies (like corporate governance charters or enterprise risk management frameworks) incorporate AI considerations and that the governing body establishes appropriate policies for transparent, responsible AI. Following such guidance, your board can set a tone that all policies – from top-level governance down to technical procedures – consistently address AI opportunities and risks.

Ensuring alignment might reveal that some new policies are needed. For example, if you start using AI extensively, you might create a dedicated “AI Ethics Policy” or an AI data management policy, rather than trying to pack everything into one AI policy. That’s acceptable, as long as these policies link together cohesively. The main point of Control 2.3 is that there should be no contradictions or blind spots – all organizational policies collectively support a responsible AI program. When done well, AI policy alignment means any employee or auditor can look across your documentation (security policies, HR policies, etc.) and see a unified approach to managing AI that complements overall business governance.

For more on ensuring AI governance fits into your existing compliance framework, see our guide on Aligning AI Policy with Organizational Policies (Control 2.3). We discuss common domains like security, privacy, and quality management, and how to integrate AI considerations into each.

A.2.4 / B.2.4 – Reviewing and Updating the AI Policy (Control 2.4)

The AI policy should be reviewed at planned intervals, or sooner if needed, to ensure it remains suitable, adequate, and effective. In other words, you must periodically evaluate and update your AI policy to keep it up-to-date with the organization’s needs and the evolving AI environment.

Purpose

This control ensures that your AI policy is not a static document. Given how quickly AI technology, regulations, and business priorities change, a policy could become outdated or insufficient if not revisited. Regular reviews allow you to adapt the policy continuously so it stays relevant, comprehensive in covering risks, and effective in practice. The goal is to make your AI policy a “living document” that grows with your organization and the external landscape.

Implementation Guidance & Best Practices

To implement Control 2.4, establish a clear policy review process with defined frequency, responsibilities, and criteria:

  • Planned Review Cycle: Decide on a formal review interval for the AI policy (for example, annually as a baseline). Many organizations choose an annual review, but if your AI activities are rapidly expanding or you operate in a highly regulated sector, you might opt for quarterly or bi-annual reviews. Document this interval in the policy itself (e.g. “This policy will be reviewed at least annually and as needed”). Ensure the review is scheduled like any other governance activity (you can align it with your management review or internal audit cycle for efficiency).
  • Event-Driven Reviews: Be prepared to trigger additional reviews when certain events occur, as waiting for the annual cycle might be too late in some cases. Events that should prompt an out-of-cycle review include: major changes in relevant laws or regulations (e.g. a new AI Act is passed), significant changes in business strategy or objectives (e.g. launching a new AI-driven product line), emerging new risks or incidents (e.g. a serious AI system failure or ethical issue), or significant advancements in AI technology that introduce new considerations. By immediately reviewing the policy after such events, you ensure it remains suitable under new conditions.
  • Assigned Responsibility: Management should assign a specific role or team responsible for the AI policy’s upkeep. This could be an AI Governance Officer, a compliance manager, or a committee (such as an AI Steering Committee or Risk Management Committee). The responsible party’s duties include scheduling reviews, gathering input, carrying out the analysis, and recommending updates. Having a named owner (“policy champion”) prevents the review from falling through the cracks. Also ensure top management (CISO, CIO, or even the Board) formally approves any substantive policy changes – this maintains authority and oversight.
  • Review Criteria: During each review, evaluate the AI policy for suitability, adequacy, and effectiveness:
    • Suitability: Is the policy still aligned with the organization’s current purpose, strategy, and values? For example, if your company has entered a new market or changed its risk appetite regarding AI, does the policy reflect that? Ensure the policy’s objectives and scope are still appropriate.
    • Adequacy: Are all significant AI-related risks and regulatory requirements addressed by the policy? Over time, new risks (like novel AI security threats) or new compliance obligations (like updated laws) may emerge – the policy should be updated to cover these. Check if any important topic is missing or if any section is now obsolete.
    • Effectiveness: Is the policy effectively guiding behavior and controls in practice? Look at whether the policy has been followed and whether following it has prevented problems. If there have been AI incidents or compliance issues, did the policy help prevent or detect them? If not, it might need strengthening. Also gather feedback: do employees understand the policy? Are there parts frequently misunderstood or ignored? Effectiveness is about the real-world impact of the policy.
  • Gather Inputs: A thorough review will use multiple information sources:
    • Results of Management Reviews: If your organization conducts periodic management reviews of the AI management system (as ISO 42001 requires in Clause 9.3), leverage those findings. Management review discussions on AI performance, objectives progress, nonconformities, etc., can highlight where the policy might need changes.
    • Incident and Compliance Reports: Examine any AI-related incidents, near-misses, or audit findings since the last review. For example, if there was a breach involving an AI system or a case of AI model bias that caused harm, analyze whether the current policy had gaps that allowed it. Incidents often provide concrete lessons to improve policy controls.
    • Stakeholder Feedback: Solicit feedback from various stakeholders – those who use or manage AI systems (developers, project managers), those who oversee compliance (risk officers, internal auditors), and even end-users or clients if feasible. They can provide insight on challenges faced in following the policy or suggest clarifications.
    • External Changes: Stay informed on external developments: new laws or regulations (e.g. government AI guidelines), new industry standards or best practices for AI, and technological trends. For instance, if a new regulation mandates transparency for AI algorithms, your policy should be updated to require explainability measures. Similarly, if a best practice framework (like the NIST AI Risk Management Framework) introduces useful concepts, you might integrate them into your policy approach.
  • Implementing Improvements: After the review, document any recommended changes to the policy. Improvements could include adding new sections (for example, if “Generative AI use” wasn’t covered before and now you are using it, you may add that), tightening requirements (if you found the policy too permissive in some area), or providing more clarity (rewriting vague language). Also consider enhancements like incorporating the latest AI governance trends (e.g. including a commitment to human-in-the-loop oversight, or references to ethical AI guidelines). Once approved, update the policy document version, communicate the changes to all relevant personnel, and provide training if necessary to ensure the updates are understood. Effective communication is key – everyone affected by the policy should know when it changes and what it means for them.
  • Continuous Improvement: Treat each review cycle as an opportunity not just to fix issues but to mature your AI governance. Over time, your policy process itself can improve. For example, you might develop a checklist or toolkit for reviewing the AI policy (covering all the criteria and inputs above). Leveraging software tools or compliance management platforms can help track policy revisions, gather inputs (through surveys or workflow), and maintain an audit trail of changes. The fact that ISO 42001 includes this control underscores that AI management is an ongoing journey – regularly updating your policies keeps you proactive and resilient as AI technology and risks evolve.

A well-maintained AI policy will help your organization stay ahead of regulatory changes, address stakeholder concerns promptly, and incorporate emerging best practices in AI ethics and risk management.

Read more about conducting effective policy reviews in our article on Reviewing the AI Policy (Control 2.4). It provides tips on setting up review workflows, involving the right people, and adjusting the policy in response to a changing AI landscape.

Conclusion

The Policies Related to AI controls in ISO/IEC 42001 lay the groundwork for effective AI governance. With establishing a comprehensive AI policy, aligning it with all other organizational policies, and keeping it up-to-date through regular reviews, organizations can ensure that their use of AI remains responsible, compliant, and aligned with business goals. These steps embrace trust in AI systems among stakeholders and create a governance structure that can adapt as technology and regulations change. With top management’s support and a commitment to continuous improvement, your AI policy will serve as a strong foundation for ethical and effective AI deployment across the organization.

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