Who is accountable for ISO/IEC 42001 evidence and AI access control?

Accountability belongs to the organisation that owns the AI system, not the tool itself. The programme needs named owners for data sources, access rules, monitoring, and audit evidence so responsibility does not disappear into shared workflows. In practice, this is a joint responsibility across security, data governance, and identity teams.

Why This Matters for Security Teams

ISO/IEC 42001 evidence only has value when it is tied to a clearly accountable operating model. The standard expects organisations to demonstrate control over AI system risks, but evidence, logs, approvals, and access decisions can quickly become fragmented across security, data, platform, and product teams. That makes it easy for accountability to blur unless ownership is explicit and auditable. For teams managing credentials and secrets, the same problem appears in NHI governance, where fragmented control undermines trust and response speed, as highlighted in The State of Secrets in AppSec.

Security teams often assume AI access control is “covered” once a policy exists, but evidence gathering fails when no one owns the underlying access decisions, exception handling, or proof of enforcement. The practical question is not who helped implement the control, but who can produce defensible evidence that the control operated as intended. In practice, many security teams encounter audit gaps only after an assessment request arrives, rather than through intentional evidence design.

How It Works in Practice

Accountability for ISO/IEC 42001 evidence and AI access control should be assigned to named control owners, not distributed informally across the organisation. The accountable owner is usually the organisation that operates the AI system, while execution is shared across security, data governance, identity, and platform teams. For access control, that means someone must own policy definition, approval paths, enforcement, exception review, and periodic recertification. For evidence, it means someone must own what gets captured, where it is retained, and how it can be produced quickly.

A workable model is to split responsibilities into four layers:

• Policy ownership: defines who may access models, prompts, datasets, and admin interfaces.

• Control operation: enforces RBAC, JIT access, and privileged workflow approvals.

• Evidence stewardship: preserves logs, tickets, attestations, and audit trails.

• Review accountability: validates that access remains appropriate over time.

Current guidance suggests aligning this with existing identity governance rather than creating a separate AI-only process. That is where a control framework such as the OWASP Non-Human Identity Top 10 helps, because AI systems and their supporting services often rely on machine credentials, service accounts, and API tokens that must be governed like any other privileged workload. NHIMG’s Ultimate Guide to NHIs reinforces the same operational pattern: ownership only becomes real when identity, access, and evidence are managed together.

For regulated environments, evidence should be produced from the system of record, not reconstructed from memory or email. That means access reviews, policy exceptions, and change approvals need durable retention and clear timestamps. Where payment or card data is in scope, practitioners also map this discipline to PCI DSS v4.0 style evidence expectations for access control and monitoring.

These controls tend to break down when AI access is handled through ad hoc developer tokens, shared service accounts, or unmanaged cross-functional workflows because no single owner can prove who approved access, who used it, and whether it was revoked.

Common Variations and Edge Cases

Tighter access control often increases operational overhead, requiring organisations to balance auditability against delivery speed. That tradeoff becomes visible in fast-moving AI programmes, where model owners want broad experimentation access while auditors want narrow, documented permissions. Best practice is evolving, but there is no universal standard for this yet: some organisations centralise evidence collection in GRC, while others keep it inside IAM or platform operations and expose it through reporting.

Edge cases usually involve shared responsibility boundaries. For example, if an external provider hosts the model but the organisation supplies the data and user base, the organisation still remains accountable for its own access decisions and evidence quality. If multiple teams can grant AI access, there must still be one named owner for the control outcome. Without that, audit support becomes a scavenger hunt across ticketing systems, chat threads, and console logs. The 52 NHI Breaches Analysis is a useful reminder that control failure often stems from ownership gaps, not just technical weakness.

In high-trust environments, the most effective pattern is to treat AI access like privileged workload access: short-lived, reviewable, and tied to a named approver. Where that discipline is missing, evidence quality usually degrades first, and access misuse becomes visible only after an incident or audit finding.

Related resources from NHI Mgmt Group

• Who is accountable when an autonomous worker changes access or gathers evidence incorrectly?
• Who should be accountable for AI access decisions in identity programmes?
• Who is accountable when AI-related access outpaces governance?
• How should security teams govern API keys used for generative AI access?