ISO 42001 AI Governance & Certification Readiness

What we assess

We define AI management-system boundaries, roles, objectives, risk criteria, and impact-assessment practices. The assessment connects policy commitments with system development, deployment, monitoring, and change decisions. AI controls need evidence from product, data, engineering, security, and governance teams rather than a policy owned by one department.

Evidence to prepare

• AI system and use-case inventory, role assignments, and governance policies.
• AI impact and risk assessments, treatment decisions, and lifecycle approvals.
• Evaluation results, monitoring records, supplier reviews, internal audits, and corrective actions.

Keep design evidence separate from operating evidence. An approved procedure shows how a control is designed; dated records show whether it ran consistently over the review period. Evidence should identify its source, owner, scope, and collection date.

What you receive

• AI management-system gap assessment and implementation roadmap.
• Evidence responsibilities and governance checkpoints across the AI lifecycle.
• Internal review and independent certification preparation under the agreed engagement.

Common questions

How does ISO 42001 relate to the NIST AI RMF?

ISO 42001 sets management-system requirements; the NIST AI RMF provides a voluntary risk-management approach. They can share inventories, risk decisions, evaluations, and monitoring evidence when mapped carefully.

What determines the engagement timeline and cost?

The system boundary, number of entities and locations, existing control maturity, evidence availability, and assessment pathway determine the effort. Share your customer requirements and target milestone so we can define a realistic scope before proposing a schedule.