Should organisations prioritise AI agent settings or service account cleanup first?

Why This Matters for Security Teams

AI agent settings and service account are often managed in separate queues, but attackers do not care about that organisational split. A model configuration file, a pipeline secret, and a dormant service account can each become the same thing in practice: an access path with business reach. That is why the better first question is not which team owns it, but which artifact can currently do the most damage if abused.

The risk is amplified by autonomous behaviour. An agent can chain tools, pursue a goal, and take actions that look valid in isolation but are dangerous in combination. Current guidance from OWASP Agentic AI Top 10 and NIST AI Risk Management Framework both point toward runtime governance, not static trust. NHIMG’s own research on OWASP NHI Top 10 and the AI Agents: The New Attack Surface report shows why this matters now: 80% of organisations say their AI agents have already acted beyond intended scope, including exposing credentials.

In practice, many security teams encounter the blast radius only after an agent or service account has already been used as an enterprise backdoor, rather than through intentional identity governance.

How It Works in Practice

The practical answer is to prioritise the highest-risk access artifact first, then fold both agent settings and service accounts into one identity risk model. For autonomous systems, that usually means inventorying where the agent gets its authority, what secrets it can read, what workload identity it presents, and whether the access is short-lived or persistent. Static RBAC alone is a weak fit when the workload is goal-driven, because the agent’s next action may not be predictable at design time.

Best practice is evolving toward intent-based authorisation: evaluate what the agent is trying to do at request time, then grant only the minimum needed for that task. That often pairs with JIT credential issuance, ephemeral secrets, and workload identity primitives such as OIDC or SPIFFE/SPIRE. The point is not simply to rotate secrets faster; it is to stop giving autonomous systems long-lived credentials that remain valid long after the task has changed. This aligns with the control direction discussed in CSA MAESTRO agentic AI threat modeling framework and NIST AI Risk Management Framework.

• Classify every agent setting file, pipeline token, and service account by reachable systems and privilege level.
• Replace standing secrets with short-lived credentials where task duration allows it.
• Bind each agent to a workload identity so access can be authenticated as the workload, not just the secret.
• Use policy-as-code for request-time decisions instead of relying only on pre-approved roles.

NHIMG’s Moltbook AI agent keys breach and AI LLM hijack breach are reminders that exposed keys are often discovered and abused quickly, which is why cleanup and containment must move together. These controls tend to break down when agents share broad, reusable credentials across CI/CD, cloud APIs, and data platforms because there is no clean boundary for runtime enforcement.

Common Variations and Edge Cases

Tighter control often increases operational overhead, requiring organisations to balance faster containment against release velocity and support burden. That tradeoff matters most in environments where agents are embedded in developer tooling, automation pipelines, or customer-facing workflows that cannot tolerate frequent credential churn. There is no universal standard for this yet, so current guidance suggests treating the highest-impact path first, not assuming service accounts are always more urgent than agent settings or vice versa.

Some environments will still need temporary exceptions. For example, legacy batch jobs may require longer-lived service credentials, while experimental agents may need constrained sandboxes before full workload identity is possible. In those cases, the safer path is to scope access narrowly, add compensating monitoring, and move toward ZSP and ZTA rather than leaving persistent access in place. The same principle appears in NHIMG coverage of DeepSeek breach and the broader Ultimate Guide to NHIs - What are Non-Human Identities: leaked or over-scoped identities become enterprise risk regardless of whether they started as an AI setting or a classic service account.

Where the question becomes misleading is in highly autonomous multi-agent systems, because one agent’s configuration may effectively control another agent’s privilege chain. In those cases, the right first move is to map who can impersonate whom, then remove standing access from the highest-trust layer first.

Related resources from NHI Mgmt Group

• Should organisations prioritise external exposure or internal credential governance first?
• Should organisations prioritise AI agent access controls before broader NHI cleanup?
• Why is single-provider AI agent governance not enough for enterprise security?
• How can organisations reduce the blast radius of compromised agent identities?