Maintaining an ISO 42001-compliant AIMS

Maintaining an AIMS is about ensuring that ISO/IEC 42001 becomes a living governance program, not a one-time compliance project. Because AI systems, data, and regulations evolve rapidly, ongoing maintenance is essential to keep AI governance effective and credible. A well-maintained AIMS continuously adapts to technical changes, emerging risks, and regulatory expectations while providing clear evidence of control and oversight. Here’s how you can do it: 1. Continuous risk and impact assessment AI risks must be reassessed periodically and whenever significant changes occur. This includes changes to models, training data, use cases, deployment environments, or applicable laws. Risk registers and treatment plans should be updated to reflect new or evolving risks. 2. Operational monitoring and regular reviews Organizations must continuously monitor AI systems in operation. This includes tracking technical metrics such as accuracy, robustness, and model drift, as well as ethical and operational indicators such as bias, user complaints, and unintended outcomes. Monitoring results should be reviewed regularly and fed into governance and management review processes. 3. Internal audits and corrective actions Planned internal audits are required to assess compliance with ISO 42001 Clauses 4 to 10 and relevant Annex A controls. Audit findings must be documented, corrective actions implemented, and effectiveness verified. This ensures governance gaps are identified and addressed early. 4. Incident and change management All AI incidents, near misses, significant model changes, and new deployments must be logged and managed through formal processes. This includes impact assessment, approvals, root cause analysis, and lessons learned. Effective incident and change management prevents repeat failures and strengthens governance maturity. 5. Training and awareness AI governance depends on people as much as technology. Organizations must regularly refresh training for engineers, product teams, and governance stakeholders. Training should cover AI risks, organizational policies, incident handling, and updates to regulations or standards.