Ownership should sit with the identity, security, and platform teams jointly, because the control problem spans human delegation, machine credentials, and runtime auditability. If each team manages only its own layer, no one can reconstruct the full action chain or revoke access cleanly when the workflow changes.
Why This Matters for Security Teams
When humans and AI agents share access paths, the governance problem is no longer just about who can log in. It becomes a question of who can delegate authority, who can approve runtime actions, and who can prove what happened after an incident. Static ownership models fail here because the access chain spans identity, secrets, policy, and telemetry. That is why NHI Management Group treats joint ownership as a control requirement, not an organisational preference.
The risk is amplified when agent workflows can call tools, chain prompts, or request additional resources without a human in the loop. Guidance from the OWASP Agentic AI Top 10 and the NIST AI Risk Management Framework both point toward shared accountability, but neither substitutes for clear operational ownership. In practice, many security teams encounter breakdowns only after an agent has already used delegated access in ways the original approver never intended.
How It Works in Practice
Ownership should be split by function and joined by process. Identity teams typically own authentication, federation, workload identity, and credential lifecycle. Security teams own policy, monitoring, detection, and incident response. Platform teams own the runtime where humans and agents actually execute tasks. The governance model works only when these teams share a common control plane for approvals, revocation, and audit trails.
For AI agents, the best practice is evolving toward task-scoped delegation rather than standing access. That means using just-in-time secrets, short-lived tokens, and workload identity so the agent proves what it is at runtime, rather than carrying long-lived credentials that outlive the task. Controls described in the OWASP Non-Human Identity Top 10 align well with this model, especially where secret sprawl and stale privilege create hidden risk. NHIMG’s Ultimate Guide to NHIs and Lifecycle Processes for Managing NHIs both reinforce the same operational lesson: lifecycle ownership matters as much as initial access approval.
- Use a single approval path for human-to-agent delegation, with recorded business context.
- Issue credentials per task, not per team, and revoke them automatically when the task ends.
- Require policy evaluation at request time so new context can block unsafe agent actions.
- Log both human intent and agent execution so incident responders can reconstruct the full chain.
This model works best when the platform can enforce policy centrally; it tends to break down in fragmented environments where agents inherit access from multiple SaaS tools, local scripts, and unmanaged secrets stores.
Common Variations and Edge Cases
Tighter governance often increases workflow friction, so organisations must balance control against delivery speed. That tradeoff is especially visible when teams share access paths across service accounts, internal copilots, and autonomous agents. Best practice is still emerging for some of these mixed environments, and there is no universal standard for this yet.
One common edge case is the “shared approval, separate execution” model, where a human approves a task but an agent executes it later. Another is break-glass access for incident response, where speed may justify temporary exceptions if the revocation path is still explicit. A third is multi-agent pipelines, where one agent’s output becomes another agent’s privilege trigger. The CSA MAESTRO agentic AI threat modeling framework is useful here because it encourages teams to model handoffs, not just endpoints.
NHIMG research on the Top 10 NHI Issues shows how governance gaps often begin with ownership ambiguity, then spread into credential sprawl and weak auditability. In mixed human-agent environments, the safest answer is not to assign ownership to one team alone, but to define a clear control owner for each stage of the lifecycle.
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 | A1 | Agentic systems need shared governance for delegated and runtime actions. |
| CSA MAESTRO | MAESTRO models multi-step agent workflows and shared control boundaries. | |
| NIST AI RMF | GOVERN | AI RMF GOVERN covers accountability for AI-enabled decisions and oversight. |
Define owner approval, runtime policy, and revocation for every human-to-agent access path.
