Because the system decides what to retrieve at runtime, not before execution. The declared scope becomes only an upper bound, while the real access pattern can shift with prompts, retrieval results, and tool selection. That is why behavioural attestation matters more than static policy in healthcare AI.
Why runtime choice makes minimum-necessary scope hard to prove
With a non-deterministic agent, the apparent permission set is only the starting envelope. The actual requests can vary across runs because retrieval, prompt interpretation, tool selection, and chained actions are resolved at execution time. That means “minimum necessary” cannot be judged from a static policy alone, because the system may exercise different privileges for different inputs.
This is the core enforcement problem: you are not validating a fixed workflow, you are validating a moving decision process. In practice, the same agent can stay within scope on one task and exceed it on another, even when the declared use case has not changed. That is why runtime governance has to look at the actions actually taken, not just the access that was pre-approved.
For healthcare AI, that distinction matters because an agent may touch records, tools, and downstream services in ways that are legitimate in one context and excessive in another. A policy written around intent is too coarse if the agent can assemble new action paths on demand. The control question becomes whether each action is authorized, observable, and attributable at the moment it occurs.
Where static policy breaks down in practice
The minimum-necessary standard assumes you can bound access in advance. Non-deterministic systems undermine that assumption because the effective scope depends on what the model retrieves, which tools it calls, and whether it re-frames the task mid-execution. If the agent can discover new context or new functions at runtime, then the boundary is no longer just configuration, it is behaviour.
That is why broad “allow the agent to do its job” permissions are risky. A system that can read a record, call an external service, and then decide on the next step is not one permission decision, it is a sequence of permission decisions. The enforcement problem is to keep each decision aligned to the original purpose and to stop hidden privilege expansion through chaining.
Practitioners often underestimate the role of delegation. Once an agent is allowed to act on behalf of a user or workflow, the question is not only what it can reach directly, but what it can trigger indirectly. A small initial allowance can become a larger effective scope if downstream tools trust the agent too much.
How to think about control boundaries instead of intent statements
The practical answer is to treat minimum necessary as a runtime control problem, not a documentation exercise. The declared scope should be translated into explicit action boundaries, so each retrieval, tool call, and data access can be checked independently against the task. Behavioral attestation is valuable here because it tests what the agent actually did, which is the only thing that proves the scope stayed narrow.
That is also where authorization design matters. AI Agent Authorisation Guide is directly relevant because it frames per-action decisions, task-scoped access, and just-in-time approval as the right control pattern when an agent’s runtime choices are not fully predictable. Zero Trust for AI Agents reinforces the same principle by requiring verification of the agent, principal, and request before each action. For agent-specific identity mechanics, Agentic AI Identity Guide shows why delegation, registration, and retirement have to be explicit if scope is going to remain enforceable over time.
Risk and Threat Considerations
Non-deterministic agents create enforcement gaps because excessive access may emerge only after runtime decisions, not at provisioning time. That raises both accidental overreach and adversarial abuse risk: a prompt, retrieval result, or tool response can steer the agent into actions that exceed the intended minimum necessary scope.
Failure mechanism: The control boundary is defined by policy, but the effective access path is assembled dynamically from prompts, retrieval, tool selection, and delegated actions, so the system can legitimately pass through wider privilege than intended.
Impact: In a regulated healthcare setting, that can produce unauthorized data exposure, overbroad record access, or actions that are hard to reconstruct after the fact because the decision chain was assembled at runtime.
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 OWASP Non-Human Identity Top 10 address the attack and risk surface, while NIST SP 800-53 Rev 5 and NIST Zero Trust (SP 800-207) set the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| OWASP Agentic AI Top 10 | ASI03 — Identity & Privilege Abuse | Runtime agent scope can expand through delegated authority and per-action authorization. |
| Recommendation — Enforce per-action authorization and constrain delegated privileges for agent requests. | ||
| OWASP Non-Human Identity Top 10 | NHI-05 — Overprivileged NHI | Agents can exceed minimum necessary when their effective access is broader than intended. |
| Recommendation — Reduce standing access and scope each agent to the minimum privileges needed. | ||
| NIST SP 800-53 Rev 5 | AC-6 — Least Privilege | Minimum necessary access is fundamentally a least-privilege control problem. |
| IA-5 — Authenticator Management | Runtime agent access depends on credential handling, rotation, and containment. | |
| Recommendation — Apply least-privilege controls and review whether each action needs the granted access. Manage and rotate agent credentials so runtime access stays bounded and reviewable. | ||
| NIST Zero Trust (SP 800-207) | Zero Trust Architecture | Non-deterministic agent actions require continuous verification of each request. |
| Recommendation — Verify every agent request before granting access to sensitive data or tools. | ||
Practitioner Guidance
What to prioritise: Prioritise runtime authorization and action logging over static scope statements. If the agent can choose among tools or data sources, treat each choice as a separate access decision that must be reviewable.
What to verify: Verify that the agent cannot silently widen its own working set through retrieval, hidden tool chaining, or inherited permissions from upstream services. The key test is whether you can explain each sensitive action after execution without relying on the model’s stated intent.
What good looks like: The observable state is narrow, attributable action execution, with clear evidence of what the agent retrieved, which tool it used, and why that action was allowed at that moment.
Practitioner takeaway: For non-deterministic agents, minimum necessary is enforced by bounding behaviour at runtime, not by trusting a pre-approved scope statement to stay true across unpredictable execution paths.