Accountability should sit with the teams that own the business process and the identity controls together, not with security alone. The identity owner needs to be able to answer why the credential exists, when it must expire, and what happens if it is misused. That is the standard boards should expect for non-human access.
Why This Matters for Security Teams
Accountability for AI workload credentials cannot sit in a security-only silo because the risk follows the workload’s business purpose, not just the secret itself. If an agent can call tools, chain actions, and request new access at runtime, then the people who approve the workflow must also own the lifecycle decisions that keep that access bounded. Current guidance increasingly treats workload identity, expiry, and revocation as operational controls, not just technical cleanup.
That is why identity governance needs to connect business ownership to credential issuance, rotation, and termination. The OWASP Non-Human Identity Top 10 and NHI lifecycle practices emphasise that unclear ownership is itself a control failure, not a documentation gap. NHIMG’s NHI Lifecycle Management Guide also frames lifecycle control as a shared operational responsibility across engineering, platform, and governance teams. In practice, many security teams encounter credential misuse only after an AI workload has already used its access in an unintended way, rather than through intentional lifecycle review.
How It Works in Practice
The cleanest accountability model assigns the business process owner responsibility for why the AI workload exists and what it is allowed to do, while the identity or platform owner is responsible for how that access is issued, monitored, and revoked. Security sets policy, verifies evidence, and tests the control design, but it should not be the sole owner of day-to-day lifecycle decisions.
For AI workloads, the control boundary should follow the workload identity, not a human operator. That means using workload identity primitives such as SPIFFE/SPIRE or equivalent OIDC-based assertions to prove what the agent is, then issuing short-lived credentials only for the task at hand. The SPIFFE workload identity specification is useful here because it anchors access in cryptographic identity rather than static shared secrets. NHIMG’s Ultimate Guide to NHIs — Static vs Dynamic Secrets reinforces the operational difference between long-lived credentials and short-lived secrets that can be rotated or revoked automatically.
A practical operating model usually includes:
- Business owner approval for the workload’s purpose, data scope, and tool access.
- Identity owner approval for credential type, TTL, rotation, and revocation triggers.
- Platform owner implementation of secret storage, workload issuance, and telemetry.
- Security oversight for policy enforcement, exception review, and periodic audit.
Where available, board reporting should show ownership, last rotation, expiry date, and emergency revocation path for each AI workload credential. That creates a defensible chain of accountability instead of a shared blame model. These controls tend to break down when fast-moving AI platforms rely on manually tracked secrets, because responsibility is split while the workload changes faster than the review process.
Common Variations and Edge Cases
Tighter lifecycle control often increases operational overhead, requiring organisations to balance faster AI delivery against stronger governance. That tradeoff becomes sharper when workloads are ephemeral, multi-tenant, or assembled from third-party tools, because the ownership model can blur between application teams, platform teams, and vendor-managed services.
There is no universal standard for this yet, but current guidance suggests the accountable party should be the team best positioned to answer three questions: why the credential exists, when it expires, and what action occurs on misuse. In regulated environments, that often means the product or service owner retains accountability even if infrastructure teams run the automation. In highly autonomous agentic systems, the expectation should be even stricter because static role-based IAM cannot reliably anticipate an agent’s next action. The NIST AI Risk Management Framework is useful as a governance layer, while NHIMG’s Guide to the Secret Sprawl Challenge highlights how ownership gaps usually appear as uncontrolled credential growth, not just weak passwords.
Best practice is evolving toward joint accountability: business owners own the risk, identity owners own the lifecycle, and security owns assurance. That model matters most when a workload can generate its own requests, because the credential may become an active attack path rather than a passive access artifact.
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 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 Non-Human Identity Top 10 | NHI-03 | Lifecycle ownership and rotation are central to controlling workload credentials. |
| CSA MAESTRO | MAESTRO addresses governance for autonomous agent workflows and their access paths. | |
| NIST AI RMF | AI RMF governance requires clear accountability for AI risk decisions and controls. |
Assign a named owner to each NHI and enforce rotation, expiry, and revocation with evidence.
