Ownership should sit with the identity and security functions that already govern access policy, logging, and lifecycle controls. The key is to maintain one control plane for identity decisions, even if multiple actor types use it. That avoids duplicated rules, inconsistent audit trails, and gaps between AI operations and existing IAM programmes.
Why This Matters for Security Teams
When humans, services, and AI agents touch the same systems, the real governance problem is not just who can log in. It is who can act, under what context, and with what evidence trail. If ownership is split across IAM, platform engineering, app teams, and AI product owners, access policy quickly becomes inconsistent. That is how standing privileges persist, audit logs diverge, and incident response loses a single source of truth.
For agentic workloads, the risk is sharper because behaviour is dynamic. Current guidance from OWASP Agentic AI Top 10 and NIST AI Risk Management Framework both point toward runtime control, not static entitlement sprawl. NHIMG’s AI Agents: The New Attack Surface report found that 80% of organisations say their AI agents have already acted beyond intended scope, which shows why ownership cannot be left to a fragmented committee model.
In practice, many security teams encounter this only after an agent has already accessed something sensitive or chained actions across systems that no one assigned to a single owner.
How It Works in Practice
The most workable model is one control plane for identity decisions, with clear stewardship split by function. Identity and security should own the policy engine, assurance requirements, logging standards, and lifecycle controls. Application and platform teams should own integration details, while business or product owners define what the agent is allowed to do and in what context. That arrangement avoids multiple versions of “approved access” being enforced in different places.
For shared resources, governance should distinguish the actor type at runtime. Human users still map well to role-based access, but services and AI agents need workload identity and context-aware authorisation. For agents, current practice is moving toward ephemeral credentials, per-task tokens, and request-time policy evaluation. Standards and implementation patterns such as NIST Cybersecurity Framework 2.0 and the CSA MAESTRO agentic AI threat modeling framework support this split by emphasizing governance, assurance, and continuous monitoring.
- Use one policy owner for identity, logging, and revocation.
- Issue short-lived credentials for agents and services rather than static secrets.
- Evaluate access at request time using context, not just role.
- Record actor type, task intent, and downstream tool use in audit logs.
- Revoke access automatically when the task ends or the trust signal changes.
NHIMG’s Ultimate Guide to NHIs — Lifecycle Processes for Managing NHIs reinforces that lifecycle ownership is where most control failures begin, especially when credentials outlive the workload they were meant to serve. These controls tend to break down when agents are allowed to call tools across multiple domains without a central revocation path, because no single team can reliably see the full chain of action.
Common Variations and Edge Cases
Tighter central governance often increases operational overhead, so organisations have to balance control against delivery speed. That tradeoff is real, especially when different teams own different parts of the stack and no universal standard exists yet for agent governance. Best practice is evolving, but the direction is clear: security ownership should be central, while execution responsibility can remain distributed.
In some environments, a shared resource may be accessed by a human in one workflow, a service in another, and an AI agent in a third. In those cases, the policy model should not merge all actors into one coarse RBAC role. Instead, it should preserve actor-specific claims and authorise based on identity type, task purpose, and risk posture. This is especially important where agents can browse, retrieve, transform, and exfiltrate data in a single session.
NHIMG’s AI Agents: The New Attack Surface report shows how quickly scope creep becomes a governance gap, while the OWASP Non-Human Identity Top 10 helps frame the lifecycle and secrets-management side of the problem. Where agents are heavily autonomous, long-lived credentials and manual approvals become unreliable because the system changes faster than humans can review it.
That model is strongest for mature programmes with central IAM, policy-as-code, and strong audit operations, but it becomes brittle when teams still manage credentials manually or cannot correlate agent actions back to a single workload identity.
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 | A-03 | Agentic systems need runtime authorisation, not static roles, which fits shared-resource governance. |
| CSA MAESTRO | GOV-1 | MAESTRO emphasizes governance ownership and lifecycle control for agentic workloads. |
| NIST AI RMF | AI RMF governs accountability and monitoring for autonomous AI behaviour across shared resources. |
Use request-time policy checks and short-lived agent credentials instead of broad standing access.
