Should enterprise agents be treated like service accounts or like users?

Enterprise agents need a service-principal model with user-like governance. They should have named owners, approvers, and review cycles, but their permissions should be limited and logged like any other non-human identity. That approach preserves accountability without pretending the agent is a person.

Why This Matters for Security Teams

Enterprise agents are not just another credentialed workload. They can plan, chain tools, request data, and adapt their next action based on what they observe, which makes a pure service-account model too blunt and a pure user model too permissive. Current guidance suggests treating the agent as a non-human identity with explicit ownership, while applying user-like governance for approvals, reviews, and accountability. That framing matters because agentic systems expand the attack surface in ways traditional IAM does not anticipate, as discussed in the OWASP NHI Top 10 and the NIST AI Risk Management Framework.

That is why a named owner, documented purpose, and periodic review are not optional extras. They are the governance layer that makes it possible to constrain an autonomous workload without pretending it is a human employee. In practice, many security teams encounter over-privileged agents only after tool chaining or data exfiltration has already occurred, rather than through intentional design.

How It Works in Practice

The operating model is usually: service-principal identity for the machine, plus human-style governance around its use. That means the agent should authenticate as a workload, not as a person, while its actions are approved and monitored through policies that reflect business intent. In agentic environments, static RBAC alone is usually too rigid because the workload’s next action is not fully known in advance. The better pattern is intent-based authorisation evaluated at request time, aligned with the control logic described in the OWASP Agentic AI Top 10 and the CSA MAESTRO agentic AI threat modeling framework.

Practically, that means:

• Issue JIT credentials per task, not long-lived secrets that outlast the job.
• Bind the agent to workload identity, such as SPIFFE/SPIRE or OIDC-backed proofs, so access is tied to what the agent is, not what it claims.
• Use policy-as-code for real-time decisions, with contextual checks for data sensitivity, destination, and action type.
• Log every tool call, token use, and approval path so ownership is auditable after the fact.
• Separate operator permissions from runtime permissions so the person who manages the agent does not inherit its full reach.

This is consistent with the broader NHI evidence base from NHI Mgmt Group, including the observation that only 5.7% of organisations have full visibility into their service accounts, which is exactly the kind of blind spot autonomous systems exploit. These controls tend to break down when agents are allowed to self-orchestrate across multiple SaaS tools with no central policy point because privilege chains become difficult to predict and contain.

Common Variations and Edge Cases

Tighter control often increases operational overhead, requiring organisations to balance faster agent execution against stronger change management. That tradeoff is real, especially where teams want agents to act continuously or across many business domains. There is no universal standard for this yet, but current guidance favours a hybrid model: treat the agent like a service principal for authentication and token handling, and like a user for approval workflows, review cycles, and accountability.

Some edge cases need special handling. A background automation that only reads telemetry can often stay closer to a classic service-account pattern. A customer-facing agent that can send email, move funds, or edit records should be governed much more strictly, with JIT privilege, tighter TTLs, and human approval for sensitive actions. Multi-agent systems raise the stakes further because one agent may become a dependency for another, which is why the AI LLM hijack breach and the 52 NHI Breaches Analysis both reinforce the same lesson: autonomy without scoped identity turns a single compromise into a platform-wide issue. The practical rule is simple, even if best practice is still evolving: the more autonomous the agent, the less it should resemble a standing, human-like account in privilege duration or scope.

Related resources from NHI Mgmt Group

• What problem does ownership attribution solve for service accounts and API keys?
• When do service accounts become a higher risk than ordinary user accounts?
• How should security teams govern Active Directory service accounts?
• Why do service accounts and AI agents need different controls from human users?