Treat the AI workflow as a non-human identity with delegated access. Define which service accounts, tokens, and API keys it may use, limit that access to the smallest possible scope, and review the runtime paths that consume those credentials. If the workflow can call tools, it needs identity governance as much as any other privileged system.
Why This Matters for Security Teams
AI systems that can call tools, query data, or trigger workflows are no longer passive outputs. They operate with delegated authority, which means their secrets, tokens, and service accounts become part of the trust boundary. Governance failures here are rarely about the model alone. They usually involve weak credential scope, unclear ownership, poor auditability, and runtime behaviour that is not reviewed with the same discipline applied to human privileged access.
The practical risk is that an agentic workflow may be designed to help one team but end up holding credentials that can touch multiple systems, especially when integration shortcuts are taken during delivery. Security teams should treat this as an identity problem first and an AI problem second. The control question is not whether the model is clever, but whether the workflow is authorised to use each secret, for each action, under each condition. The NIST Cybersecurity Framework 2.0 is useful here because it anchors governance, protection, and monitoring around business-critical systems rather than around isolated technology stacks. In practice, many security teams encounter AI credential misuse only after a workflow has already been allowed to overreach under normal operations, rather than through intentional design review.
How It Works in Practice
Operational governance starts by inventorying every AI workflow that can invoke a tool, API, database, or automation path. Each workflow should have a named owner, a defined business purpose, and a documented set of allowed actions. If a workflow needs secrets, those secrets should be issued to the workflow’s non-human identity or service account, not hard-coded into prompts, notebooks, or application config files. Current guidance suggests binding credentials to the smallest feasible scope, with short lifetimes, environment-specific separation, and explicit approval for elevation.
Security teams should also review the full execution path, because the model is only one component. A safe design usually includes:
- Dedicated service identities for each AI workflow or environment
- Secret storage in a managed vault, not in code or conversation context
- Tool allowlists that limit which endpoints, commands, or actions can be called
- Strong logging for prompts, tool calls, secret access, and downstream effects
- Periodic access review that tests whether the workflow still needs each credential
Governance should also address prompt injection and tool abuse. The OWASP Non-Human Identity Top 10 is especially relevant because it frames credential exposure, weak lifecycle management, and over-privileged machine identities as first-class risks. For AI systems that use retrieval, plugins, or orchestration layers, the control objective is to prevent untrusted content from steering a privileged action. That means separating decision-making from execution, requiring policy checks before tool invocation, and validating outputs before they are written, sent, or used to trigger another step. These controls tend to break down in fast-moving environments where engineering teams share generic service accounts across multiple agents because attribution and revocation become too weak to support meaningful governance.
Common Variations and Edge Cases
Tighter credential governance often increases delivery overhead, requiring organisations to balance speed against traceability and blast-radius reduction. That tradeoff becomes more visible when agentic systems span multiple teams, cloud accounts, or data domains. In those environments, best practice is evolving rather than settled, especially for how much autonomy an AI workflow should have before a human approval step is required.
There is also a difference between a bounded assistant and a semi-autonomous workflow. A chat interface that drafts text may need no persistent secrets, while an agent that books tickets, changes records, or provisions resources may require tightly controlled delegated access. The latter should be governed like any privileged system, with JIT access where feasible, explicit scoping, and rollback plans for unsafe actions. Edge cases arise when the same AI workflow is used for testing and production, when secrets are inherited from a parent pipeline, or when third-party tools return data that is later re-used as an instruction. In those cases, identity boundaries blur, and the security design should assume that any untrusted input can attempt to influence a privileged path.
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 MITRE ATLAS address the attack and risk surface, while NIST CSF 2.0 and NIST AI RMF set the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| NIST CSF 2.0 | ID.AM | AI workflows with tools and secrets need clear asset and identity inventory. |
| OWASP Non-Human Identity Top 10 | Machine identities and secret lifecycle are central risks in this scenario. | |
| OWASP Agentic AI Top 10 | Tool use, prompt injection, and action abuse are core agentic AI threats. | |
| NIST AI RMF | GOVERN | Governance is needed to assign accountability for autonomous AI behaviour. |
| MITRE ATLAS | AML.TA0001 | Adversarial input can steer an agent toward unsafe tool use or secret exposure. |
Model threat paths for prompt injection and tool manipulation, then test detections and guardrails.
