Human controls focus on authentication, session assurance, and user behaviour, while AI agent controls must also manage runtime scope, delegated actions, and system-to-system access. In healthcare, the difference matters because an agent can act at machine speed across multiple systems, so governance must cover lifecycle, authorization, monitoring, and revocation together.
Why This Matters for Security Teams
The difference is not just philosophical. Human users can be governed through login assurance, step-up authentication, and session monitoring because their actions are usually bounded by a person’s intent and attention span. AI agents, by contrast, are autonomous software entities with execution authority, tool access, and the ability to chain actions across systems. That means controls must address the agent’s runtime scope, delegated permissions, and revocation path, not just who logged in. Current guidance from OWASP Agentic AI Top 10 and NIST AI Risk Management Framework both point to the same reality: once an agent can decide, act, and reuse context, classic user-centric IAM no longer gives complete coverage.
This is where NHI governance becomes operationally important. Agents often authenticate as workloads, not people, so their identity is closer to a Non-Human Identity than a user account. If that distinction is missed, organisations over-trust RBAC and under-invest in intent-aware authorisation, ephemeral secrets, and continuous monitoring. In practice, many security teams encounter agent misuse only after data has moved or credentials have already been exposed, rather than through intentional design.
How It Works in Practice
Controlling humans and controlling agents use different primitives. Human governance starts with identity proofing, MFA, session assurance, and role assignment. Agent governance starts with workload identity, tool boundaries, and runtime policy checks. A practical model issues a short-lived credential for a specific task, evaluates the request against policy at the moment of use, and automatically revokes access when the task ends. That is the operational meaning of JIT access for agents: not a standing entitlement, but an expiring permission tied to a concrete action.
The reason static IAM fails is that autonomous behaviour is not predictable in advance. An agent can inspect context, chain tools, call APIs, and pursue a goal in ways that a predefined role never anticipated. Best practice is evolving toward intent-based authorisation, where policy decides whether the requested action matches the declared purpose, data sensitivity, and current risk. Frameworks such as CSA MAESTRO agentic AI threat modeling framework and MITRE ATLAS adversarial AI threat matrix are useful for structuring those runtime decisions.
- Use workload identity, such as SPIFFE or OIDC-backed service identity, to prove what the agent is before granting tools.
- Prefer short-lived secrets and just-in-time credentials over long-lived API keys.
- Evaluate policy at request time with context, not only at provisioning time.
- Log every tool call, data access, and revocation event for audit and rollback.
NHIMG research shows why this matters: in the AI Agents: The New Attack Surface report, 80% of organisations said their AI agents had already acted beyond intended scope. These controls tend to break down in legacy environments with broad service accounts and shared secrets because the agent cannot be safely constrained once it inherits too much standing privilege.
Common Variations and Edge Cases
Tighter agent controls often increase engineering overhead, requiring organisations to balance speed of deployment against governance depth. That tradeoff is real, especially where agents need to call many tools or operate across multiple business units. There is no universal standard for every agent pattern yet, so current guidance suggests using the minimum privilege needed for the shortest possible time, then tightening controls as the agent’s blast radius grows.
One common edge case is the hybrid workflow: a human approves the goal, but an agent executes the steps. In that model, the human should not inherit the agent’s permissions, and the agent should not inherit the human’s standing access. Another edge case is emergency operation, where break-glass access is required. Even then, the agent should receive a separate, time-boxed credential, not a reused human token. The OWASP NHI Top 10 and the NIST AI Risk Management Framework both support this separation between identity, authority, and execution.
In high-volume healthcare or customer-service environments, the hardest problem is revocation. Humans can be told to stop; agents may already have queued actions, cached tokens, or delegated workflows in flight. The practical answer is continuous monitoring plus immediate token invalidation, not just policy review after the fact.
Standards & Framework Alignment
This section maps relevant standards and security frameworks to the operational risks and controls described in this guidance.
OWASP Agentic AI Top 10 and CSA MAESTRO address the attack and risk surface, while NIST AI RMF set the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| OWASP Agentic AI Top 10 | A1 | Agentic apps need runtime controls beyond human IAM. |
| CSA MAESTRO | Maps threat modeling to autonomous agent workflows and controls. | |
| NIST AI RMF | Provides governance structure for AI risk, accountability, and monitoring. |
Assign ownership, evaluate risk continuously, and document agent decisions and exceptions.
Related resources from NHI Mgmt Group
- What is the difference between managed identities and hardcoded secrets for AI agents?
- What is the difference between human identity governance and AI agent governance?
- What is the difference between workload identity and API keys for AI agents?
- What is the difference between governing human access and governing AI agent access?
