To address these challenges, standards bodies and regulators worldwide are raising the bar. The EU's proposed AI Act introduces a risk-based regime for AI systems, the U.S. is promoting the NIST AI Risk Management Framework (RMF) for trustworthy AI, and the OECD has issued AI Principles emphasising human rights, fairness, transparency, and accountability. Complementing these efforts, ISO/IEC 42001:2023 - the first international “AI Management System” standard- provides a structured, lifecycle-oriented approach to building a trustworthy AI program. Crucially, it calls for robust internal policies that turn high-level ethics into practical controls.

For CISOs, compliance officers, and business leaders, mastering ISO 42001 means more than checking a box; it means embedding AI governance into strategy, operations, and culture. This guide shows how to do that. We explain the key policy domains defined by ISO 42001, weave in complementary frameworks (NIST, OECD, EU AI Act), surface emerging tools and techniques..

The Role of Internal Policies in AI Governance

Internal policies are more than compliance documents; they are the operational backbone of an effective AIMS. They translate the principles of ISO/IEC 42001 into actionable, organisation-specific practices. Well-crafted policies help organisations:

• Ensure consistency across AI initiatives
• Assign clear roles and accountability
• Align with legal, ethical, and stakeholder expectations
• Promote continuous improvement and risk awareness

These policies guide how AI systems are developed, used, and monitored, and ensure the organisation can prove its commitment to responsible AI when audited or challenged.

Aligning Your Policies with ISO/IEC 42001 Requirements

ISO/IEC 42001 requires top management to establish an AI policy appropriate to the organisation's context. This policy must support AI objectives (Clause 6.2), ensure regulatory compliance, and enable ongoing improvement.

The standard uses a Plan-Do-Check-Act (PDCA) structure familiar from other ISO systems (e.g., ISO 9001, ISO 27001). Policies serve a vital role at each PDCA phase:

• Plan: Policies define the scope of the AI management system, identify applicable controls, and outline how risks and ethical implications will be evaluated.
• Do: Policies guide the implementation of AI governance practices, ensuring responsible AI principles like fairness, explainability, and data transparency are embedded in daily operations.
• Check: Policies establish the procedures for regularly monitoring AI performance and evaluating the AIMS itself to ensure ongoing compliance with evolving regulations.
• Act: Policies should include mechanisms for continual improvement, detailing how performance outcomes and regulatory developments will lead to refinements in AI governance strategies.

Key Policy Areas to Address in an ISO/IEC 42001-Aligned AIMS

To effectively implement ISO/IEC 42001, your organisation's internal policies should cover, but are not limited to, the following critical areas:

• AI Policy and Objectives (Clauses 5.2, 6.2, Annex A.2.2): This foundational policy should articulate your organisation's commitment to responsible AI, reflecting your business strategy, organisational values, legal requirements, and the interests of relevant parties.
• AI Risk Management (Clauses 6.1, Annex A.5, B.5): Develop policies for the systematic identification, assessment, and mitigation of AI-related risks, including concer ns like bias, data security, and accountability. This includes policies for conducting AI system impact assessments (Clause 6.1.4) to evaluate potential consequences for individuals, groups, or society.
• Data Governance & Protection (Annex A.7, B.7): Policies must ensure the quality, provenance, acquisition, and secure preparation of data used in AI systems. This is crucial for adhering to privacy laws and safeguarding against data breaches, which is a core concern for ethical AI.
• Human Oversight & Accountability (Annex A.3, B.3.2):Policies should clearly define the roles, responsibilities, and accountability for AI systems across the organisation. They should ensure appropriate human involvement in critical AI-driven decisions and establish mechanisms for reporting concerns related to AI systems.
• Transparency & Explainability (Annex A.8, B.8, B.9.3):Draft policies that promote clear communication about the capabilities, limitations, and decision-making processes of AI systems. The goal is to ensure AI systems are explainable, auditable, and free from bias, allowing justification of AI-driven decisions to regulators and stakeholders.
• AI System Life Cycle Management (Annex A.6, B.6):Policies should govern the entire AI system lifecycle, from design and development to verification, validation, deployment, operation, and monitoring. This ensures responsible practices are embedded at every stage.
• Third-Party and Supplier Management (Annex A.10, B.10):Given the increasing reliance on external AI solutions, policies must address the management of compliance risks associated with third-party AI systems. This includes vendor assessments, contractual safeguards, and independent audits to ensure adherence to ethical and operational standards
• Alignment with Other Organisational Policies (Annex A.2.3, B.2.3):Policies should ensure the AI management system integrates seamlessly with existing management systems, such as those for information security (ISO 27001) and privacy i nformation management (ISO 27701).

Practical Steps: Drafting and Operationalising AI Policies

Supporting Technologies and Frameworks

While policies define what to do, technology solutions are critical to help execute AI governance. A growing market of AI governance platforms and tools can automate many tasks. For example:

• AI Governance Platforms: Solutions like Holistic AI, ModelOp, or Monitaur centralize AI asset inventory, risk scoring, and controls management. They often have built-in mappings to frameworks like ISO 42001, NIST RMF, and the EU AI Act, so you can see “one pane of glass” for compliance status. Many platforms also include collaborative workflows for risk approvals and audit trails.
• Policy Automation Tools: Open Policy Agent (OPA) and similar “policy as code” tools let you enforce rules automatically. For instance, you can embed an OPA check in your CI/CD pipeline that blocks any model push if it hasn' passed a required bias test. This turns written policies into machine-enforced gates.
• Model Risk Management (MRM) Solutions: Traditional financial MRM tools (e.g. SAS Model Manager, IBM Model Risk) are adapting to AI/ML. They track model lifecycles, versioning, testing results, and maintain audit logs. If your organization already uses an MRM system for quantitative models, ensure it extends to ML models under the AI policy.
• Data Catalogs and Lineage Tools: As mentioned, tools like Collibra, Informatica Axon, or Alation help enforce data policies by automating lineage discovery and data quality rules. They can generate alerts when data used by an AI system is non-compliant (e.g. missing consent tags).

Common Framework Alignment

While this guide focuses on ISO/IEC 42001, it's wise to cross-pollinate with other standards and laws:

• NIST AI RMF: Complements ISO by providing detailed guidance on implementation. NIST emphasizes continuous monitoring and improvement of AI risk (the Manage function). It also outlines “Core” risk management categories (e.g. data quality, cybersecurity, governance) that you can map into your ISO-aligned policies.
• OECD AI Principles: These non-binding principles promote trustworthy AI globally. They stress elements like “transparency and explainability” and “capacity for human agency and oversight”. Referencing them in your policies underscores alignment with international norms. For example, mirror the OECD language by requiring “meaningful information appropriate to context” about AI decisions.
• EU AI Act: The Act (once in force) will require documentation and risk management for high-risk AI. If you operate in or with the EU, build compliance into your ISO-driven framework now. That means, for instance, ensuring policies mandate the very records (e.g. technical documentation, incident logs, CE marking) that the EU rules demand. A forward-thinking organization uses ISO 42001's clauses as a foundation but layers in EU Act obligations where applicable.
• Other Standards: Don't forget related areas: ISO 27001 for cybersecurity of AI infrastructure, ISO 27701 for data privacy, IEC TR 24028 for AI system overview, etc. Your AI policies should reference these where relevant. For instance, your AI data policy might explicitly say, “comply with ISO 27701 for data privacy and ISO 27001 for security controls.”

ISO/IEC 42001 offers a vital framework for managing AI ethically, securely, and transparently, but it's effective internal policies that turn that framework into real-world action. These policies bridge the gap between intention and implementation, ensuring AI systems align with legal requirements, ethical principles, and organizational goals. By building clear, adaptable policies, organizations can go beyond compliance and foster a culture of responsible, trustworthy AI.

At Reinvent, we help organizations align AI practices with global standards, whether you are launching your first AI project or expanding governance across your enterprise. Let's make AI transparent, ethical, and resilient.

References

https://www.a-lign.com/articles/understanding-iso-42001

https://www.modulos.ai/guide-to-ai-governance/

https://www.devoteam.com/expert-view/iso-iec-42001-building-trustworthy-ai-for-sustainable-growth/