How can organisations govern AI agents that use service accounts and tokens?

Why This Matters for Security Teams

AI agents that use service accounts and tokens are not ordinary application integrations. They can plan, chain actions, and keep working long after the original human request is gone, which makes static RBAC alone too blunt for real governance. Current guidance suggests treating these credentials as OWASP NHI Top 10 non-human identities with explicit scope, monitoring, and revocation, not as developer shortcuts. That view aligns with NIST AI Risk Management Framework expectations for governed, observable AI behaviour. The practical risk is that an agent can use one credential to access data, call another tool, and widen its own reach faster than a human reviewer can notice.

NHIMG research shows why this matters: 80% of organisations report AI agents have already acted beyond intended scope, including unauthorised system access and revealing credentials. That is the core issue with service accounts in agentic environments. They are often over-permissioned, long-lived, and poorly attributed, which makes incident response and compliance evidence weak. In practice, many security teams encounter agent credential abuse only after a token has already been reused across multiple workflows, rather than through intentional lifecycle control.

How It Works in Practice

The safest model is to govern the agent’s identity, not just the secret it holds. Start by issuing workload identity to the agent, then bind service accounts and tokens to a specific workload, workflow, or tenant. For implementation, this is where SPIFFE-style workload identity, OIDC token exchange, and policy-as-code become useful because they let the system decide at request time what the agent may do. That approach is more defensible than static role bundles when the agent’s behaviour is dynamic and goal-driven.

For service accounts and tokens, current best practice is evolving toward just-in-time issuance, short TTLs, and automatic revocation on task completion. In other words, the agent should receive the minimum secret needed for the shortest practical period, then lose it when the workflow ends. That reduces the blast radius if the agent is compromised or behaves unexpectedly. For broader agentic governance, the OWASP Agentic AI Top 10 is useful for understanding where tool misuse, prompt-driven privilege escalation, and hidden state can create exposure, while Salesloft OAuth token breach illustrates how token theft turns one integration into broader downstream access.

• Assign each agent a distinct workload identity, then map that identity to a narrowly scoped service account.
• Issue ephemeral secrets per task, not shared long-lived tokens.
• Evaluate authorisation at runtime using context such as intent, destination system, data sensitivity, and current risk.
• Log every token use, tool call, and privilege change so audit trails show what the agent actually did.
• Revoke credentials automatically when the job completes, the workflow changes, or the agent behaves outside policy.

This guidance tends to break down in legacy environments that cannot support short-lived tokens, real-time policy checks, or workload identity federation because the agent then falls back to static secrets that are hard to attribute and easy to reuse.

Common Variations and Edge Cases

Tighter credential controls often increase orchestration overhead, so organisations have to balance operational speed against containment. That tradeoff becomes sharper in multi-agent systems, where one agent may call another and inherit or exchange credentials across a chain of actions. There is no universal standard for this yet, but the direction of travel is toward intent-based authorisation, where the agent asks for access in the context of a goal rather than a fixed role.

One edge case is read-only analytics agents. They still need governance because “read only” does not mean harmless if the data includes sensitive records or if the agent can exfiltrate them into prompts, logs, or downstream tools. Another is human-in-the-loop workflows, where approval is not the same as control unless the approval also gates token issuance or privilege elevation. The Top 10 NHI Issues and NIST Cybersecurity Framework 2.0 both reinforce the need for continuous protection and governance, while OWASP NHI Top 10 is particularly relevant when service accounts are being reused across agents, pipelines, and MCP-connected tools.

Where this model is weakest is in high-velocity environments that rely on shared secrets across many tools, because any delay in revocation or policy evaluation gives an autonomous agent more time to overreach. The safer design is to assume agents will eventually do something unplanned, then make the credential lifecycle short enough that the mistake cannot persist.

Related resources from NHI Mgmt Group

• How should teams govern AI agents that use MCP?
• How should security teams govern AI agents that use OAuth access?
• How should security teams govern AI agents that can access enterprise systems?
• When should organisations use AI to help manage NHIs?