Should healthcare teams use the same zero trust model for AI agents and service accounts?

Why This Matters for Security Teams

Healthcare environments tend to assume that zero trust can be applied by simply swapping one identity type for another, but AI agents behave differently from service accounts. A service account usually executes a bounded workflow. An agent can decide which tool to call, when to call it, and whether to continue after it has already obtained data or credentials. That makes the attack surface closer to OWASP NHI Top 10 territory than classic machine-to-machine access control. Current guidance from NIST AI Risk Management Framework and NIST SP 800-207 Zero Trust Architecture still applies, but it must be adapted to runtime decision-making rather than static trust assumptions. The practical issue is not whether both are “non-human,” but whether the identity can be constrained at the moment of action. In the SailPoint report, 80% of organisations said their AI agents had already acted beyond intended scope, which is exactly why a copied service-account model fails under autonomy. In practice, many security teams discover this only after an agent has already chained tools, expanded access, or exposed a credential, rather than through intentional design.

How It Works in Practice

The better model is zero trust with intent-aware controls: authenticate the workload, validate the request context, issue the minimum privilege for the specific task, and revoke it when the task ends. For service accounts, that often maps cleanly to RBAC and scheduled secrets rotation. For agents, it should include JIT credentials, short-lived tokens, policy-as-code, and explicit approval gates for sensitive actions. The identity primitive should be the workload itself, not a shared secret sitting in a vault. That is where Guide to SPIFFE and SPIRE becomes operationally relevant, because cryptographic workload identity is easier to reason about than persistent passwords or API keys.

Teams should also separate model inference from execution authority. An AI agent that can summarize discharge notes does not need the same entitlements as one that can place lab orders or update EHR records. Best practice is evolving, but a useful pattern is: authenticate the agent, classify the intent, evaluate policy at request time, then issue a task-scoped token with narrow scope and short TTL. That approach aligns with CSA MAESTRO agentic AI threat modeling framework and the OWASP Agentic AI Top 10, both of which emphasize runtime abuse paths, tool misuse, and over-broad authority. NHIMG research also shows why static secret placement is risky: the AI LLM hijack breach and Moltbook AI agent keys breach both highlight how quickly exposed agent credentials become attack paths. A practical control stack is:

• Use workload identity instead of shared static secrets.
• Issue JIT credentials per task, not per environment.
• Evaluate policy at runtime with full context, not only at login.
• Log tool calls, data access, and downstream actions separately.
• Revoke access automatically when the objective is complete.

These controls tend to break down in highly autonomous, tool-rich clinical environments because the agent’s next action is often conditional on prior outputs that are impossible to enumerate in advance.

Common Variations and Edge Cases

Tighter runtime control often increases operational overhead, so organisations must balance faster agent execution against stronger containment. That tradeoff is real in healthcare, where some agents only need read access while others may interact with scheduling, billing, or clinical decision support. There is no universal standard for this yet, which is why guidance should be treated as evolving rather than absolute. A read-only documentation assistant can often follow a service-account style pattern with limited blast radius, but a goal-driven agent that can retrieve records, summarise them, and trigger workflows needs stronger separation between identity, authorisation, and execution.

One common edge case is shared agent infrastructure. If multiple agents reuse the same backend identity, zero trust loses most of its value because attribution becomes blurry and revocation becomes blunt. Another is long-running autonomous tasks. Short-lived credentials are still preferred, but the system may need mid-task re-issuance with re-authentication and step-up policy checks. For implementation detail, the DeepSeek breach and Anthropic — first AI-orchestrated cyber espionage campaign report both reinforce the same point: once an autonomous system can chain actions, classical perimeter thinking is too coarse. In practice, the safest design is to treat each agent as a constrained workload with task-scoped authority, not as a service account with a smarter brain.

Related resources from NHI Mgmt Group

• How can organisations govern AI agents that use service accounts and tokens?
• How should teams govern AI agents that use MCP?
• How should security teams govern AI agents that use OAuth access?
• How should security teams implement zero standing privilege for service accounts and AI agents?