Defining an AI Management System
An AI Management System is not a single tool or technical platform. It is the set of organizational processes, defined roles, and documented controls that govern how an enterprise identifies, assesses, and manages risk across the AI lifecycle, from initial development through deployment and ongoing operation.
ISO/IEC 42001:2023, published by ISO/IEC JTC 1/SC 42, is the first international standard to specify requirements for establishing this kind of system. Like other ISO management system standards, it does not prescribe specific technologies. Instead, it defines what an organization must have in place, at a governance and process level, to manage AI risk in a structured and auditable way.
How ISO 42001 Structures AIMS Requirements
ISO/IEC 42001 follows the Annex SL high-level structure shared with ISO 27001 and ISO 9001, organizing requirements across clauses 4 through 10: context of the organization, leadership, planning, support, operation, performance evaluation, and improvement. This shared structure means organizations with an existing ISO 27001 or ISO 9001 management system can extend that foundation to cover AIMS-specific requirements rather than building a parallel system from scratch.
Within this structure, the standard requires top management to approve an AI policy and to define roles, responsibilities, and authorities relevant to the AIMS. Accountability cannot be delegated solely to technical teams. It also requires organizations to conduct AI risk assessments and AI impact assessments as part of planning and operational controls, and to maintain documented information demonstrating that these processes are actually operating, not just defined on paper.
Annex A Controls and Documented Evidence
ISO/IEC 42001 includes Annex A, a list of AI-specific controls, and Annex B, implementation guidance for applying those controls. Annex A areas generally reference AI system impact assessment, data quality, third-party and supplier management, and AI system lifecycle documentation.
The standard requires continual improvement of the AIMS through internal audit, management review, and corrective action, mirroring the evidence expectations found in ISO 27001. In practice, conformance evidence tends to consist of documented risk assessments, impact assessments, monitoring records, and internal audit reports, rather than continuous technical telemetry.
Organizations preparing for certification should verify exact Annex A control wording against the official ISO/IEC 42001:2023 text, since summaries and secondary sources vary in how they describe control scope.
AI Agents, Autonomous Systems, and the Interpretation Gap
ISO/IEC 42001 does not define the term AI agent or address agentic AI as a distinct category. Autonomous or multi-step AI systems fall under the standard's general AI risk, monitoring, and impact-assessment clauses rather than dedicated provisions. This creates an interpretation gap that enterprises deploying AI agents must address operationally.
In practice, this means mapping an agent's decision-making, tool use, and autonomy boundaries into the existing AIMS risk assessment and impact assessment processes, since the standard provides no purpose-built framework for evaluating multi-step or tool-invoking AI behavior. Organizations should treat this as an area requiring internal interpretation and documentation, not as a gap the standard itself resolves.
Evaluation Criteria Before Establishing or Assessing an AIMS
- Confirm whether the AIMS scope explicitly includes AI agents and autonomous decision-making systems, or only traditional AI and ML models.
- Identify what documented evidence, including risk assessments, impact assessments, and audit logs, will be required for agentic AI use cases.
- Determine how an existing ISO 27001 or ISO 9001 management system needs to be extended to satisfy AIMS-specific Annex A controls.
- Assign and document accountability for AI risk decisions under the AIMS roles and responsibilities requirements.
- Identify what monitoring or operational evidence internal audits and management reviews will expect on a recurring basis, not as a one-time project.
Where Runtime Governance Intersects with ISO 42001
ISO/IEC 42001 specifies management system requirements, not runtime enforcement mechanisms. It does not mandate specific technologies for monitoring or controlling AI behavior at execution time. That said, the standard's clauses on monitoring, measurement, and internal audit require documented evidence that AI risk is being actively managed, and logging or audit trails from AI agent runtime environments can support the documented information requirements found in the standard's operational clauses.
Runtime governance and policy enforcement, capabilities such as agent identity, permissions, tool approval workflows, and audit logging, operate as the technical control layer that can generate this kind of evidence. Trussed AI provides runtime governance and security for enterprise AI agents, including runtime policy enforcement, monitoring, and audit logging, which organizations can use to produce operational records relevant to AIMS monitoring and audit clauses. This is a complementary role: the standard defines what evidence is required, while runtime tooling is one way to generate it consistently.
AIMS at a Glance
| Aspect | Description |
|---|---|
| First AI-Specific ISO Standard | ISO/IEC 42001:2023 is the first international management system standard written specifically for artificial intelligence. |
| Annex SL Structure | Uses the same clause structure (4 through 10) as ISO 27001 and ISO 9001, easing integration with existing management systems. |
| Risk and Impact Assessments | Requires documented processes to identify AI-specific risks and assess system impact before and during deployment. |
| Certifiable Standard | Organizations may pursue third-party accredited certification, distinct from self-assessment alone. |