ISO/IEC 42005 is a standard for assessing the impact of artificial intelligence systems on people, organizations, and society. It provides a structured method to identify, evaluate, and document AI-related effects across the system lifecycle, helping governance teams manage risk, accountability, and decision making in AI deployments.
What ISO/IEC 42005 Covers
ISO/IEC 42005 is about evaluating how an AI system affects people, organisations, and society across its lifecycle. It turns that broad question into a structured assessment so teams can identify consequences, compare options, and document decisions consistently.
The standard is most useful when AI use has more than a technical outcome to consider. It helps teams look beyond model performance and ask what the system changes in practice, including who is affected, what risks emerge, and what evidence should support a governance decision.
Why AI Impact Assessment Matters
An impact assessment standard exists because AI can create effects that are easy to miss if review focuses only on accuracy or uptime. Decisions about deployment, data use, automation, and human oversight can all create downstream consequences that need to be assessed before and after release.
ISO/IEC 42005 also fits naturally with broader AI governance work, where impact review is part of making AI decisions explainable, repeatable, and defensible. For teams already building risk registers or assurance workflows, a standardised impact method reduces ad hoc review and makes it easier to compare systems consistently. See AI Risk Assessment Guide for a broader view of AI risk identification and treatment.
What A Good Assessment Looks At
A useful assessment asks what the system does, who or what it may affect, and which effects are material enough to require mitigation or sign-off. That usually includes potential bias, privacy consequences, operational disruption, safety concerns, user trust, and broader organisational impact.
The key point is not to treat impact assessment as a one-time checkbox. AI systems change over time as data, prompts, integrations, users, and use cases change, so the assessment should stay connected to the system lifecycle rather than being frozen at launch.
How ISO/IEC 42005 Fits Into Governance
In practice, ISO/IEC 42005 helps convert AI review from informal judgment into a documented governance process. It gives decision makers a common way to record assumptions, evidence, residual risk, and the rationale for approval, rejection, or redesign.
That makes the standard especially relevant where multiple teams contribute to one AI deployment, because the assessment becomes a shared reference point for product owners, risk teams, legal, privacy, security, and leadership. The companion AI Impact Assessment Template shows how those governance inputs can be captured in a practical format.
For organisations building broader AI management practices, ISO/IEC 42005 is closely aligned with programme-level controls and lifecycle oversight. The ISO/IEC 42001 Guide is useful when you need the management-system view around policy, accountability, and auditability.
Risk and Threat Considerations
AI impact reviews fail when organisations treat them as paperwork instead of a control. The main risk is that harmful effects, such as unfair outcomes, privacy exposure, unsafe automation, or weak human oversight, are discovered only after deployment.
Failure mechanism: Teams approve AI based on technical performance alone, then miss contextual effects that appear in real use, especially when the system is repurposed, integrated into decisions, or exposed to new populations.
Impact: This can lead to avoidable harm, governance failure, poor accountability, and a weaker ability to justify why the system was deployed in the first place.
Standards & Framework Alignment
This section maps relevant standards and security frameworks to the operational risks and controls described in this guidance.
NIST AI RMF and NIST SP 800-53 Rev 5 set the technical controls, while ISO/IEC 42001:2023, GDPR and ISO/IEC 27001:2022 define the regulatory obligations.
| Framework | Control / Reference | Relevance |
|---|---|---|
| ISO/IEC 42001:2023 | 4.1 — Understanding the organization and its context | ISO 42001 governs AI system context and governance, which ISO/IEC 42005 supports through impact review. |
| Recommendation — Link AI impact assessment outcomes to your AI management system context and governance decisions. | ||
| NIST AI RMF | GOVERN — Govern | NIST AI RMF governs AI risk oversight and accountability, matching lifecycle impact assessment needs. |
| Recommendation — Use AI impact findings to inform governance decisions, accountability, and risk treatment. | ||
| NIST SP 800-53 Rev 5 | RA-3 — Risk Assessment | AI impact assessment is a structured risk assessment activity for system effects and consequences. |
| Recommendation — Perform and document risk assessments that capture AI system impacts and residual risk. | ||
| GDPR | Article 35 — Data Protection Impact Assessment | When EU personal data is involved, AI impact review aligns with DPIA-style assessment of high-risk processing. |
| Recommendation — Run a DPIA where AI processing creates high-risk effects on personal data or individuals. | ||
| ISO/IEC 27001:2022 | A.5.31 — Legal, statutory, regulatory and contractual requirements | AI impact assessment often documents obligations and approval evidence tied to governance requirements. |
| Recommendation — Record legal and regulatory obligations that affect AI deployment approval and oversight. | ||
Practitioner Guidance
Governance implication: Treat ISO/IEC 42005 as part of AI decision governance, not as a standalone document exercise. The assessment should be owned by the team accountable for the deployment, with evidence that the review covered the intended use, affected stakeholders, and the residual risk decision.
What to watch for: If the assessment cannot be kept current as the system, data, or use case changes, the process is too static to be reliable. In that case, the standard should drive periodic reassessment and sign-off triggers, not one-off approval.
Related resources from NHI Mgmt Group
- Who is accountable for ISO/IEC 42001 evidence and AI access control?
- Why does ISO/IEC 27001:2022 matter for IAM and NHI programmes?
- Why do organisations choose ISO/IEC 27001 when they already have other security frameworks?
- What is the difference between ETSI TS 119 461 and ISO/IEC 30107 in identity proofing?
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Reviewed and updated by the NHIMG editorial team on October 23, 2026.
NHI Mgmt Group — the #1 independent authority on Non-Human Identity, IAM, and Agentic AI security. nhimg.org