Accountability sits with the organisation, not the agent or the platform alone. Security, IAM, cloud, and application teams must define ownership for secret lifecycle management, policy enforcement, audit logging, and access review. In practice, governance should ensure that both human and machine identities are covered by the same control objectives and reviewed under the same risk framework.
Why This Matters for Security Teams
Accountability for protecting access to AI agents and cloud secrets cannot sit with a single tool owner, because the risk spans identity, policy, runtime behaviour, logging, and incident response. In hybrid environments, the same secret may be used by a CI runner, an agentic workflow, and a cloud service account, which means a narrow platform view misses who can issue, reuse, rotate, or revoke access. NHI Management Group’s research on The State of Secrets in AppSec shows why this matters: leaked secrets can take an average of 27 days to remediate, even when organisations believe their controls are strong.
That delay is especially dangerous when an agent can chain tools, call APIs, and act faster than human review can intervene. Current guidance from OWASP Agentic AI Top 10 and the NIST AI Risk Management Framework points toward shared accountability, but not shared ambiguity. In practice, many security teams encounter ownership gaps only after a secret is exposed or an agent has already used overbroad access to reach systems no one intended.
How It Works in Practice
The most reliable model is to assign accountability by control domain rather than by deployment boundary. Security sets policy standards, IAM defines identity and trust conditions, cloud teams manage provider-side permissions, and application teams own how agents request and use secrets inside workflows. That split matters because AI agents are not static users. They need access that is often task-specific, time-bound, and context-aware.
For agents, best practice is evolving toward workload identity plus just-in-time secret issuance. A workload identity, such as a SPIFFE-style identity or OIDC-backed service token, proves what the agent is at runtime. Then policy evaluation decides whether the requested action is allowed in that moment, based on environment, tool, data sensitivity, and request intent. This is materially different from role-based access that assumes fixed behaviour. The supporting body of NHIMG research on OWASP NHI Top 10 and the Ultimate Guide to NHIs reinforces that long-lived static credentials are a liability when machine actors can operate continuously.
- Define a single owner for secret lifecycle management, including issuance, rotation, and revocation.
- Require runtime policy checks for each privileged action instead of pre-granted standing access.
- Separate human approval from machine execution, especially in production workflows.
- Log the identity, intent, and target resource for every agent-initiated secret use.
- Review agent permissions on the same cadence as human privileged access.
Where this guidance breaks down is in loosely governed hybrid estates with shadow SaaS, unmanaged CI runners, or embedded secrets in build pipelines, because accountability exists on paper but not at the point of use.
Common Variations and Edge Cases
Tighter secret controls often increase operational overhead, so organisations must balance speed of delivery against revocation discipline and auditability. That tradeoff becomes sharper when multiple cloud accounts, third-party agent platforms, and developer-owned automation all touch the same data.
There is no universal standard for this yet, but current guidance suggests three common patterns. First, managed agent platforms may handle the runtime, while the customer still owns secrets, policies, and approvals. Second, in federated hybrid environments, cloud teams own provider-side IAM but application teams still own embedded secrets and tool permissions. Third, for multi-agent systems, each agent may need its own workload identity and its own scoped secret path so that compromise does not cascade across the chain.
NHIMG case research such as CoPhish OAuth Token Theft via Copilot Studio and Meta AI Instagram Account Takeover shows that identity abuse often begins at the workflow boundary, not in the core cloud control plane. That is why NIST and CSA guidance is increasingly converging on shared governance, even if implementation details still vary across vendors and architectures.
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, OWASP Non-Human Identity Top 10 and CSA MAESTRO address the attack and risk surface, while NIST AI RMF and NIST CSF 2.0 set the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| OWASP Agentic AI Top 10 | A2 | Agentic systems need runtime controls for autonomous access decisions. |
| OWASP Non-Human Identity Top 10 | NHI-03 | Secrets lifecycle management is central to protecting non-human identities. |
| CSA MAESTRO | TRUST-02 | MAESTRO addresses trust boundaries and governance for agentic workloads. |
| NIST AI RMF | AI RMF supports accountable governance for autonomous AI behaviour. | |
| NIST CSF 2.0 | PR.AC-4 | Least privilege and access management apply to both human and machine identities. |
Use request-time policy checks for each agent action instead of standing permissions.
Related resources from NHI Mgmt Group
- Who is accountable when AI agents and other non-human identities make access decisions that create risk?
- Why do external vendor access workflows need stronger identity governance in hybrid cloud environments?
- How should financial services teams automate access governance across cloud and hybrid environments?
- Who is accountable for governing AI security policy across cloud and edge environments?
