Context
AI governance is becoming a key pillar in the development and deployment of AI systems. It helps organizations mitigate a wide range of risks, including legal, operational, and ethical ones, and avoid related harms, while maintaining alignment with business goals.
To implement AI governance effectively, organizations can adopt internationally recognized standards that provide structured, auditable approaches to managing AI-related risks and ensuring compliance.
Published in late 2023, ISO 42001 [1] has emerged as a comprehensive framework for establishing an AI Management System (AIMS). It follows the ISO-defined blueprint for Management Systems, which is consistently applied across standards such as ISO 9001 (quality), ISO 14001 (environment), ISO 20000 (IT service), ISO 27001 (information security), ISO 31000 (risk), and ISO 50001 (energy).
Among these, ISO 27001 [2], originally developed in 2005, is particularly relevant for AI, as it defines an Information Security Management System (ISMS) focused on preserving the confidentiality, integrity, and availability (CIA) of information.
While ISO 42001 and ISO 27001 have different aims, areas of focus, and stakeholder groups, they share a common foundation through ISO’s High-Level Structure (HLS) and often apply to overlapping domains (see Table 1). AI systems, for example, rely on data processing during both training and inference, requiring secure storage, transfer, and access, which are areas where both standards converge.
| Dimension | ISO 27001 – Information Security Management System | ISO 42001 – AI Management System |
| Aim | Protect the confidentiality, integrity, and availability of information assets through a structured Information Security Management System (ISMS). | Ensure the trustworthy, ethical, and compliant development, deployment, and use of AI systems through an AI Management System (AIMS). |
| Risk Focus | Cyber threats, data breaches, unauthorized access, insider threats, operational disruptions. | AI-specific risks such as algorithmic bias, lack of transparency, model drift, over-reliance on automation, and unintended societal impacts. |
| Stakeholders | IT teams, security officers, compliance managers, data owners, system administrators. | AI developers, product managers, governance teams, ethics boards, end users, impacted individuals (e.g. consumers, citizens), regulators. |
| Regulatory | Addresses existing privacy and security laws (like GDPR), | Aligns with emerging AI-specific regulations (e.g. EU AI Act). |
| Commonalities | Shared ISO structure, risk-based approach, management involvement, document and evidence, audits, and support for integration. | |
| Joint use cases | AI systems in healthcare, finance, and public sectors require both secure data handling (ISO 27001) and ethical, transparent AI use (ISO 42001), especially for sensitive data and data governance. | |
In summary, ISO 27001 & ISO 42001 are structurally aligned, conceptually complementary, and often implemented together in real-world AI systems.
The following article outlines how both standards are implemented within the Modulos AI Governance Platform, encouraging organizations to establish an Integrated Management System (IMS) and enabling organizations to manage multiple standards in a unified, scalable, and traceable way.
Implementation
Modulos’ AI Governance Taxonomy
In a previous article, we introduced the Modulos AI Governance Taxonomy [3], a comprehensive ontology that translates regulatory frameworks into actionable components. This taxonomy forms the content backbone of the Modulos AI Governance Platform.
At its core, the taxonomy (see Figure 1) identifies atomic, actionable units of work called Controls, each representing a specific implementation task aimed at addressing AI risks and fulfilling legal or regulatory Requirements. These Controls are not tied to a single regulatory framework. This independence allows them to be reused across multiple standards while maintaining traceability to the original source Requirements.
In addition, the Risks defined in the Platform’s Risk Module are directly linked to the assets at risk. Users can perform risk management activities while capturing information from associated Controls that support risk identification, assessment, mitigation, and monitoring. This linkage ensures continuous information sharing and clear distribution of responsibilities between technical teams and risk management, while enabling organizations to integrate risk management consistently across frameworks.
In the following sections, we explain how this structure aligns with both ISO 27001 and ISO 42001, and why it provides a strong foundation for efficient and scalable implementation.
ISO-Centric Content and Structure
The Modulos AI Governance Platform mirrors the content and structure of ISO 27001 and ISO 42001 to support implementation, review, and audit readiness.
Content Fidelity – Practitioners receive clear implementation guidance, supported by AI agents. Evidence can be uploaded directly against each requirement. Governance experts can generate the Statement of Applicability (SOA), and auditors can assess compliance using the full ISO requirement set.
Structural Alignment – The platform reflects the ISO top-down structure, from organizational context to evaluation, ensuring full traceability and alignment.
The table below summarizes the mapping between ISO 27001 and ISO 42001 concepts and their implementation within the Modulos AI Governance Platform.
| ISO Layer | Purpose | Description | Modulos Platform Equivalent |
| Requirements | High-level “what” | Define general expectations and obligations, typically found in the main chapters of the standards. | Requirements |
| Controls | Low-level “what” | Represent specific, actionable measures to meet the Requirements and manage associated risks. Controls in Annex A are mandatory and must be covered in the SOA. Organizations may also define additional custom Controls. | Controls |
| Guidance | Low-level “how” | Offer recommended approaches to implement the Controls, such as those in Annex B of ISO 42001. Organizations may use alternatives, as guidance is optional and not covered in the SOA. | Guidance |
Together, this content and structural alignment ensures that users can implement both standards efficiently, with full traceability and confidence in audit readiness.
Modular Controls
The Modulos Platform is designed to simplify compliance with multiple standards, such as ISO 27001 and ISO 42001, by enabling the reuse and extension of Controls.
✅ Reusable – Given the significant overlap between these frameworks, many Controls can serve dual purposes. For instance, a single “Context Assessment” Control can contribute to both standards, even if their scopes differ. Shared evidence like organizational charts or policy templates often satisfies multiple Requirements, and the platform helps maintain clarity by keeping each Requirement distinct while guiding users on completeness.
✅ Extensible – All Annex Controls are mapped to their corresponding Requirements, enabling users to generate a Statement of Applicability (SOA) aligned with each standard. Where standards include complex or heterogeneous Requirements, the Platform proposes additional reusable Controls to ensure full coverage, minimize interpretation effort, and strengthen compliance assurance.
✅ Granular – Crucial to this process is the granularity of each Control. To be actionable, a Control must have a clear purpose, assigned accountability, and a well-defined implementation scope. Each Control should represent a concrete action that contributes to fulfilling a specific Requirement. While the specificity of the Requirement drives the level of detail, practical judgment ensures the right balance: too broad, and Controls lose precision; too detailed, and they become overwhelming or disconnected from context.
The Modulos Platform streamlines compliance by providing reusable, extensible, and well-balanced granular Controls that align with multiple standards, ensuring efficient, traceable, and precise governance across frameworks.
Harmonized Risks
The Requirements and Controls defined earlier are not only meant to fulfill compliance obligations, they also play a key role in mitigating specific Risks.
Take, for example, a data protection Control designed to reduce the risk of unauthorized access. This type of risk is ideally assessed in terms of both frequency and potential impact. Its lifecycle, from identification to mitigation and monitoring, along with the assigned accountability, can be nearly identical whether it falls under the scope of ISO 27001 or ISO 42001.
This alignment further supports the case for harmonizing Controls across standards and managing Risks in a unified, consistent manner.
Organization-level vs Application-level Execution
ISO 27001 and ISO 42001 define how to design and implement Information Security and AI Management Systems. Both include organization-level Requirements, such as setting policies, defining context, and managing risk, which are flagged as such in the Modulos Platform.
Application-level frameworks put these principles into practice. They include technical measures like data bias mitigation or access controls and are also clearly labeled in the platform.
This structured distinction helps translate governance into concrete actions, aligning responsibilities across strategic and technical teams.
Conclusion
The alignment between ISO 27001 and ISO 42001 represents a unique opportunity for organizations to manage AI governance and information security in a coherent and efficient way. While each standard addresses its own domain, their structural similarity and overlapping areas make a unified approach both practical and beneficial.
The Modulos AI Governance Platform is designed with this integration in mind. By structuring Requirements, Controls, and Guidance in a reusable, granular, and standards-aligned format, it enables organizations to implement both frameworks without duplication, and with full traceability. From high-level policies to technical safeguards, from risk management to audit readiness, the platform supports the full lifecycle of an Integrated Management System.
As AI adoption accelerates and regulatory expectations evolve, having a solid, adaptable foundation becomes critical. Modulos equips organizations with the tools to navigate this complexity, reduce compliance overhead, and build trustworthy AI systems at scale.
Discover how the Modulos AI Governance Platform can help you implement ISO 27001 and ISO 42001 in an integrated, scalable way. Contact us for a demo and see how our platform can simplify your compliance journey.