How should security teams govern agent identities differently from service accounts?

Why This Matters for Security Teams

Agent identities should not inherit the same governance model as service accounts because their behaviour is not fixed by a single workflow. A service account typically performs narrow, repeatable machine-to-machine tasks. An agent can choose tools, chain actions, and continue operating without a human at each step, which makes standing permissions and broad tokens much riskier. That shift is exactly why current guidance in OWASP Agentic AI Top 10 and the NIST AI Risk Management Framework emphasizes runtime control, accountability, and context-aware limits rather than static entitlement design.

The practical risk is not just credential theft. Agentic systems can initiate follow-on actions that were never explicitly planned, especially when tool access spans code, data, tickets, and external APIs. NHIMG research on the Ultimate Guide to NHIs shows how often long-lived secrets and excessive privileges persist in enterprise environments, which becomes more dangerous when the identity can act autonomously. In practice, many security teams encounter agent abuse only after a tool chain has already been traversed, rather than through intentional design.

How It Works in Practice

Governance should start by classifying the identity as an autonomous workload, not as a conventional service account. That means the control objective changes from “who can use this account?” to “what can this agent do, under what conditions, and for how long?” Best practice is evolving toward intent-based authorisation, where policy is evaluated at request time with the full context of the task, data sensitivity, tool scope, and environment state. Static RBAC alone is too blunt when the agent’s action path is dynamic.

For that reason, security teams should pair workload identity with just-in-time delegation. A well-governed agent receives short-lived credentials per task, not a durable token that can be reused later. Workload identity mechanisms such as OIDC-backed tokens or SPIFFE-style proof of workload identity help establish what the agent is, while policy-as-code engines decide what the agent may do right now. That model aligns with the direction of the CSA MAESTRO agentic AI threat modeling framework and the OWASP Top 10 for Agentic Applications 2026, both of which stress tool-use control and containment.

• Issue credentials per task and revoke them automatically when the task completes.
• Scope tool access to the minimum action set, not the minimum role label.
• Log each agent action with the triggering intent, policy decision, and downstream tool call.
• Review delegation boundaries whenever a new tool, connector, or prompt path is introduced.

NHIMG’s Lifecycle Processes for Managing NHIs is a useful reference for turning that model into operational controls, especially where offboarding, rotation, and visibility are weak. These controls tend to break down when agent tooling is shared across teams because delegated authority and revocation ownership become ambiguous.

Common Variations and Edge Cases

Tighter agent controls often increase operational overhead, requiring organisations to balance reduced blast radius against slower execution and more policy maintenance. That tradeoff is real, especially in environments where agents are expected to work across many systems or recover from partial failures. Current guidance suggests that there is no universal standard for this yet, so teams should define control tiers based on task criticality rather than force every agent into the same model.

Hybrid cases create the most confusion. A script that only refreshes a dashboard may still be a service account, but an agent that decides whether to fetch data, trigger a workflow, or open a ticket should be governed as an autonomous identity. The difference is whether the identity merely executes a known sequence or selects the sequence at runtime. NHIMG’s reporting on the AI LLM hijack breach and the OWASP NHI Top 10 underscores why prompt injection, tool chaining, and privilege escalation must be treated as governance issues, not just application bugs.

In practice, the safest pattern is to assign separate governance, separate secrets, and separate audit expectations to agents that can make choices. If the environment cannot support short-lived credentials, runtime policy checks, and strong action logging, the design is still too close to a traditional service account model.

Related resources from NHI Mgmt Group

• How should security teams govern service accounts, machine identities and workload access differently?
• How should security teams govern non-human identities alongside human accounts?
• How should security teams govern machine identity credentials in agentic AI environments?
• How should security teams govern AI agents that use OAuth access?