Accountability should sit with the team that can change the entitlement and explain the business purpose of the access, not only with the team that operates the platform. In many organisations that means IAM, cloud security, and AI owners need a shared model for access review and risk acceptance rather than separate sign-off chains.
Why This Matters for Security Teams
When identity, cloud, and AI security overlap, the ownership problem is rarely technical. It is a governance gap: the team that runs the platform may not control the entitlement, the team that controls identity may not understand the workload, and the AI team may not know which access is acceptable for a given task. That split creates delayed approvals, duplicate reviews, and risk acceptance that no one can trace cleanly.
This matters because non-human identities already dominate many environments, and NHIMG notes that NHIs outnumber human identities by 25x to 50x in modern enterprises. That scale makes shared governance unavoidable, especially when access is dynamic and tied to cloud roles, service accounts, API keys, and agentic workflows. Current guidance from the NIST Cybersecurity Framework 2.0 and NHIMG’s Ultimate Guide to NHIs both point toward accountable ownership, continuous oversight, and lifecycle control rather than siloed sign-off.
In practice, many security teams encounter entitlement drift only after a cloud role, secret, or agent permission has already been overused in production, rather than through intentional review.
How It Works in Practice
The strongest operating model is shared accountability with one clear decision owner for the entitlement and one clear control owner for the platform. In practice, that usually means identity governance defines who can have access, cloud security defines where the access is used, and AI or application owners explain the business purpose and task scope. No single team should be able to approve access in isolation when the access can create broad execution authority.
For NHIs and AI agents, the practical control pattern is to issue just-in-time access, bind it to workload identity, and evaluate policy at request time. That shifts the question from “who has this role?” to “what is this workload trying to do right now?” Emerging guidance in the CSA MAESTRO agentic AI threat modeling framework and NHIMG’s lifecycle guidance for managing NHIs supports that model because it ties governance to runtime intent, secret rotation, and offboarding.
- Identity teams own approval criteria, expiry, and periodic review cadence.
- Cloud teams own the resource boundary, logging, and environment-specific guardrails.
- AI or app owners own the business justification and task-level context.
- Security governance owns escalation paths, exceptions, and evidence collection.
This works best when access requests include workload identity, expected actions, data sensitivity, and a defined TTL for the credential or token. It also works better when policy is codified and reviewed at runtime, not just in a quarterly spreadsheet. These controls tend to break down in fast-moving agentic environments where permissions are chained across tools and no team has a complete view of the full execution path.
Common Variations and Edge Cases
Tighter governance often increases review overhead, requiring organisations to balance speed against control. That tradeoff is especially visible in teams running cloud-native pipelines, external SaaS integrations, or autonomous agents that need short-lived access across multiple systems.
There is no universal standard for this yet, but current guidance suggests a few defensible variations. In smaller organisations, one security leader may own the policy model while cloud and AI operators execute it. In larger enterprises, a federated model often works better, with shared standards for entitlement review, exception handling, and evidence retention. The important point is that ownership should follow the ability to change access and explain why it exists, not the org chart of the system it touches.
NHIMG’s research on Top 10 NHI Issues and the regulatory and audit perspective is useful here because it shows why visibility, rotation, and offboarding cannot be owned by separate teams without creating accountability gaps. Where agents can call tools, chain actions, or request new access mid-task, the governance model needs explicit fallback rules for exceptions, emergency access, and post-incident review. Best practice is evolving, but split ownership without shared evidence usually fails audit and slows incident response.
Standards & Framework Alignment
This section maps relevant standards and security frameworks to the operational risks and controls described in this guidance.
OWASP Agentic AI Top 10 and CSA MAESTRO address the attack and risk surface, while NIST AI RMF set the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| OWASP Agentic AI Top 10 | A01 | Agentic systems need runtime control over autonomous access decisions. |
| CSA MAESTRO | MAESTRO addresses shared governance for multi-agent and tool-based workflows. | |
| NIST AI RMF | GOVERN | AI RMF governance covers accountability across overlapping security domains. |
Define who may authorize agent actions at request time and require task-scoped approval.
Related resources from NHI Mgmt Group
- Who should own cloud security findings that involve identity, workloads, and data at the same time?
- Why is single-provider AI agent governance not enough for enterprise security?
- Who should own cloud identity decisions when security architecture and IAM overlap?
- Who should own cloud security standards across multi-cloud programmes?
