Accountability should sit with security, identity, and platform teams together, because machine and agent access crosses traditional ownership boundaries. Security sets policy, identity defines entitlement and trust, and platform teams enforce controls in the systems where credentials are used. Clear ownership prevents gaps when credentials move from human workflows into automation.
Why This Matters for Security Teams
Accountability for machine and agent access to secrets is a governance issue, not just an implementation detail. When autonomous workloads request tokens, certificates, or API keys, ownership often spans security policy, identity lifecycle, and the platform that actually brokers access. That means gaps appear fast if one group assumes another is watching the credential path. The risk is amplified by agentic systems that can chain tools, retry actions, and request new secrets at runtime.
Current guidance from OWASP Non-Human Identity Top 10 and the NIST AI Risk Management Framework points toward shared accountability with explicit control ownership, not informal handoffs. NHIMG’s State of Secrets in AppSec found that only 44% of developers follow secrets best practices, which helps explain why secrets governance breaks down once machine-issued credentials move beyond human review. In practice, many security teams discover the ownership gap only after a leaked credential has already been reused in automation.
How It Works in Practice
The practical model is a three-part accountability chain. Security owns the policy standard for when secrets may be issued, how long they may live, and what evidence is required for approval. Identity owns the trust model, including workload identity, entitlements, and revocation. Platform teams own enforcement in CI/CD, runtime environments, secret brokers, and service meshes where credentials are actually consumed.
For machines and agents, that means the account owner should not be a person who “uses” the secret, but the system owner who can prove the workload’s identity and current purpose. Best practice is evolving toward ephemeral, task-scoped access using just-in-time provisioning, short time-to-live secrets, and real-time policy decisions rather than static RBAC alone. For agentic systems, that aligns with context-aware authorization, where the decision is made at request time based on the action, tool, environment, and risk level. The operating model should also tie into a named runbook for revocation so a secret can be withdrawn as soon as the task ends.
That approach is consistent with the direction described in OWASP Agentic AI Top 10 and CSA MAESTRO agentic AI threat modeling framework, where the emphasis is on controlling behaviour at runtime rather than assuming a fixed access pattern. NHIMG’s 52 NHI Breaches Analysis shows why this matters: once a secret is available to an automated workload, it can be replicated, forwarded, or reused outside the original owner’s control. These controls tend to break down when secrets are hardcoded into pipelines or shared across many services because revocation becomes slow and attribution becomes unclear.
Common Variations and Edge Cases
Tighter secrets governance often increases operational overhead, so organisations must balance faster delivery against stronger control of machine credentials. That tradeoff becomes most visible in CI/CD, delegated agent workflows, and multi-team platforms where no single owner has full visibility into secret issuance and use.
There is no universal standard for this yet, but the current guidance suggests a few common patterns. Central security teams usually set the policy and exception process, platform teams enforce workload-level controls, and application or product teams retain responsibility for the service that requested the secret. In regulated environments, this may extend to audit ownership and evidence retention as well.
Edge cases appear when a secret is issued by one platform, consumed by another, and rotated by a third. In those situations, accountability should follow the control boundary that can actually stop misuse, not the team that merely requested the integration. That is especially important for autonomous agents, because an agent can change its path mid-task, call a new tool, or request a different credential without a human ticket in the loop. The NIST Cybersecurity Framework 2.0 remains useful for mapping responsibilities, but it does not by itself resolve ownership for dynamic secret use. For that reason, organisations should document one accountable business owner, one policy owner, and one enforcement owner per secret class, then test whether each can act during an incident.
Standards & Framework Alignment
This section maps relevant standards and security frameworks to the operational risks and controls described in this guidance.
OWASP Non-Human Identity Top 10, OWASP Agentic AI 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 Non-Human Identity Top 10 | NHI-01 | Defines governance for non-human identities and their secret use. |
| OWASP Agentic AI Top 10 | A-03 | Agentic systems need runtime control over secret access, not static role assumptions. |
| CSA MAESTRO | GOV-2 | MAESTRO emphasizes governance for autonomous agent behaviour and access paths. |
| NIST AI RMF | AI RMF GOVERN supports accountability for AI-enabled access decisions. | |
| NIST CSF 2.0 | PR.AC-1 | Identity and access governance requires explicit accountability and least privilege. |
Create clear ownership for policy, identity, and platform enforcement across agent secret workflows.
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