Search every question across sub-FAQs

Start with the operational decision: define what AI Policy means in your ISO/IEC 42001 scope, who owns it, and what record proves the decision is current.

For AI governance work, start from the AI system inventory: purpose, role, provider or deployer status, data inputs, impact assessment, control owner, monitoring signal, human oversight, and change trigger. This keeps the answer useful in audits, customer reviews, incidents, supplier reviews, and management review.

The evidence should show the process operating. For this artifact, the strongest record usually includes AIMS scope, AI inventory, AI Policy, role map, risk and impact assessments, control evidence, monitoring records, human oversight, and management review outputs.

Avoid evidence that only repeats a requirement. A reviewer should be able to see the actual owner, date, system, supplier, AI system, service, incident, risk, or control sample behind the answer.

The person who can fund, operate, and correct the process should own the decision; governance should review consistency and exceptions.

For high-impact changes, approval should include the teams affected by the evidence: security, privacy, resilience, supplier management, AI governance, legal, risk, or business service owners as relevant.

Review it at planned intervals and whenever the underlying scope, service, supplier, control, risk, AI system, personal data flow, incident process, or customer commitment changes.

A stale record is worse than a short record. If the facts change, update the evidence and mark what changed so the next reviewer can trust the page.

Start with the operational decision: define whether you are pursuing external certification, another third-party conformity assessment outcome, or an internal conformance record for your ISO/IEC 42001 scope, and record who owns that decision.

For AIMS certification work, keep the traceability chain visible: management-system scope, AI system inventory, AI policy, risk and impact assessment records, control evidence, monitoring outputs, corrective actions, and management review decisions. This keeps the answer useful in certification audits, customer reviews, supplier reviews, incidents, and management review.

The evidence should show the process operating. For this artifact, the strongest record usually includes AIMS scope, AI inventory, AI policy, role map, risk and impact assessments, control evidence, monitoring records, human oversight, and management review outputs.

Avoid evidence that only repeats a requirement. A reviewer should be able to see the actual owner, date, system, supplier, AI system, service, incident, risk, or control sample behind the answer.

The person who can fund, operate, and correct the process should own the decision; governance should review consistency and exceptions.

For high-impact changes, approval should include the teams affected by the evidence: security, privacy, resilience, supplier management, AI governance, legal, risk, or business service owners as relevant.

Review it at planned intervals and whenever the underlying scope, service, supplier, control, risk, AI system, personal data flow, incident process, or customer commitment changes.

A stale record is worse than a short record. If the facts change, update the evidence and mark what changed so the next reviewer can trust the page.

Start with the operational decision: define what Generative AI means in your ISO/IEC 42001 scope, who owns it, and what record proves the decision is current.

For AI governance work, start from the AI system inventory: purpose, role, provider or deployer status, data inputs, impact assessment, control owner, monitoring signal, human oversight, and change trigger. This keeps the answer useful in audits, customer reviews, incidents, supplier reviews, and management review.

The evidence should show the process operating. For this artifact, the strongest record usually includes AIMS scope, AI inventory, AI policy, role map, risk and impact assessments, control evidence, monitoring records, human oversight, and management review outputs.

Avoid evidence that only repeats a requirement. A reviewer should be able to see the actual owner, date, system, supplier, AI system, service, incident, risk, or control sample behind the answer.

The person who can fund, operate, and correct the process should own the decision; governance should review consistency and exceptions.

For high-impact changes, approval should include the teams affected by the evidence: security, privacy, resilience, supplier management, AI governance, legal, risk, or business service owners as relevant.

Review it at planned intervals and whenever the underlying scope, service, supplier, control, risk, AI system, personal data flow, incident process, or customer commitment changes.

A stale record is worse than a short record. If the facts change, update the evidence and mark what changed so the next reviewer can trust the page.

Start with the operational decision: define what High Risk AI means in your ISO/IEC 42001 scope, who owns it, and what record proves the decision is current.

Treat the label as context-specific. NIST guidance says organizations establish the purpose, scope, assumptions, constraints, information sources, and risk model before conducting a risk assessment, and the CSF says organizations use their mission, stakeholder expectations, threat landscape, and requirements to understand and prioritize cybersecurity risks.

The evidence should show the process operating. For this artifact, the strongest record usually includes AIMS scope, AI inventory, AI policy, role map, risk and impact assessments, control evidence, monitoring records, human oversight, and management review outputs.

Avoid evidence that only repeats a requirement. A reviewer should be able to see the actual owner, date, system, supplier, AI system, service, incident, risk, or control sample behind the answer.

The person who can fund, operate, and correct the process should own the decision; governance should review consistency and exceptions.

For high-impact changes, approval should include the teams affected by the evidence: security, privacy, resilience, supplier management, AI governance, legal, risk, or business service owners as relevant.

Review it at planned intervals and whenever the underlying scope, service, supplier, control, risk, AI system, personal data flow, incident process, or customer commitment changes.

A stale record is worse than a short record. If the facts change, update the evidence and mark what changed so the next reviewer can trust the page.

Start with a plain rule: Human Oversight is the human control point for an AI decision or process. In practice, that means a named person reviews the scope, assumptions, and risk, can challenge or stop the decision, and keeps the record current.

For AI governance work, start from the AI system inventory: purpose, role, provider or deployer status, data inputs, impact assessment, control owner, monitoring signal, Human Oversight, and change trigger. This keeps the answer useful in audits, customer reviews, incidents, supplier reviews, and management review.

Good oversight is not just an approval stamp. It should make sure the decision is understandable, owned, reviewed at the right time, and escalated when it affects risk acceptance, service commitments, customer promises, regulatory duties, or certification evidence.

The evidence should show the process operating. For this artifact, the strongest record usually includes AIMS scope, AI inventory, AI policy, role map, risk and impact assessments, control evidence, monitoring records, Human Oversight, and management review outputs.

Avoid evidence that only repeats a requirement. A reviewer should be able to see the actual owner, date, system, supplier, AI system, service, incident, risk, or control sample behind the answer.

The person who can fund, operate, and correct the process should own the decision; governance should review consistency and exceptions.

For high-impact changes, approval should include the teams affected by the evidence: security, privacy, resilience, supplier management, AI governance, legal, risk, or business service owners as relevant.

Review it at planned intervals and whenever the underlying scope, service, supplier, control, risk, AI system, personal data flow, incident process, or customer commitment changes.

A stale record is worse than a short record. If the facts change, update the evidence and mark what changed so the next reviewer can trust the page.