Teams should separate read-only assistance from any action that can move money, expose data, or change identity state. If the assistant can be manipulated by untrusted content, it should not be allowed to execute sensitive actions without stronger gating, narrower scope, and explicit approval controls.
How to draw the line between advice and action
The trust decision should start with the consequence of the action, not with how fluent the assistant sounds. Read-only help can tolerate much looser controls than actions that transfer value, disclose sensitive data, or change account state. The more irreversible the outcome, the more the assistant should behave like an untrusted request source until it is proven otherwise.
A practical way to frame this is to separate suggestion from execution. An assistant can draft, summarise, search, and prepare, but the moment it can commit a change, your team should ask whether a human would be comfortable delegating that act to a junior operator with the same inputs and the same audit trail. If the answer is no, the assistant needs a tighter decision boundary.
That boundary is especially important when the assistant can be influenced by untrusted content, because the model may faithfully follow malicious instructions hidden in documents, tickets, emails, or web pages. NIST AI Risk Management Framework is useful here because it treats trust, validity, and harmful outputs as governance questions, not just model-quality questions.
What controls should exist before an assistant can act?
Before any sensitive action is allowed, the assistant should be constrained by narrow scope, explicit approval, and strong authentication around the action path. The key question is whether the assistant is merely preparing a request or actually operating with authority to complete it. If it is the latter, the action path should be treated as privileged and bounded just like any other sensitive automation.
Good control design usually means three things: the assistant can only reach the minimum systems needed, it can only perform a small set of approved actions, and high-impact steps require a separate human decision. That becomes even more important for money movement, secret handling, account changes, and any action that changes who or what can access a system.
For teams building these controls, NIST SP 800-53 Rev 5 Security and Privacy Controls provides the clearest control vocabulary for access restriction, authentication, auditability, and configuration discipline. When the assistant issues API calls, OWASP Agentic AI Top 10 is the more specific lens for tool misuse and identity or privilege abuse.
How teams should make the trust decision in practice
The decision should be based on blast radius, not novelty. If the assistant can expose regulated data, alter identity state, or trigger downstream automation, treat that capability as a privilege that must be justified, reviewed, and monitored. If the assistant only produces text or recommendations, the risk is materially lower and the approval bar can usually be lighter.
Teams should also test the assistant’s failure mode before expanding access. Ask what happens if a prompt injection, poisoned document, or deceptive user request tries to redirect the assistant toward an unsafe action. If the answer is that the assistant could still reach the sensitive operation, then the control design is too permissive.
For the underlying access model, NIST SP 800-207 Zero Trust Architecture reinforces the idea that no request should be trusted just because it came through a familiar interface. If the sensitive action depends on secret handling or machine credentials, OWASP Non-Human Identity Top 10 is relevant because the real risk often comes from overprivileged, long-lived, or poorly governed access material.
Risk and Threat Considerations
When an assistant can be manipulated by untrusted content, the main risk is not just wrong output, but authorised misuse of a legitimate path. A prompt injection or similar influence can turn a helpful system into a conduit for unauthorised disclosure, account changes, or other actions that appear valid on the surface.
Failure mechanism: The assistant accepts malicious instructions or unsafe context, then uses its available tools or permissions to complete an action the user did not truly intend, especially when approvals are weak, scopes are broad, or the action is easy to trigger from ordinary text.
Impact: Sensitive data can leak, transactions can be misdirected, identity state can be altered, and investigators may struggle to distinguish legitimate automation from attacker-driven misuse because the action came through an authorised channel.
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, OWASP Non-Human Identity Top 10 and MITRE ATT&CK address the attack and risk surface, while NIST AI RMF and NIST Zero Trust (SP 800-207) set the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| NIST AI RMF | Govern | AI trust and harmful-action governance are central to deciding when an assistant may act. |
| Recommendation — Define decision gates for trusted actions and require human oversight for high-impact uses. | ||
| NIST Zero Trust (SP 800-207) | Zero Trust Architecture | Untrusted content and requests should not be implicitly trusted in action paths. |
| Recommendation — Verify each action path explicitly instead of trusting the assistant or its context by default. | ||
| OWASP Agentic AI Top 10 | ASI03 — Identity & Privilege Abuse | The question is about when an assistant’s authority and privileges become unsafe. |
| Recommendation — Limit agent authority and require separate approval for privileged operations. | ||
| OWASP Non-Human Identity Top 10 | NHI-05 — Overprivileged NHI | Assistant actions often depend on machine credentials or service access that can be overextended. |
| Recommendation — Reduce the assistant’s access to only the credentials and scopes required for the task. | ||
| MITRE ATT&CK | Credential Access | Manipulated assistants can be used to abuse legitimate access paths and exposed secrets. |
| Recommendation — Hunt for abuse of delegated access and credential misuse in assistant-driven workflows. | ||
Practitioner Guidance
What to prioritise: Classify every assistant capability by the worst credible outcome, then restrict anything that can move money, reveal sensitive data, or alter identity state until you have explicit approval and a narrow action scope.
What to verify: Confirm that the assistant cannot convert untrusted content into a high-impact action without a separate decision point, and verify that logs show who approved the action, what context was used, and what was actually executed.
Decision rule: If the assistant can be tricked by external text, treat it as untrusted for sensitive actions unless the tool path is strongly constrained, individually authorised, and easy to revoke.
Practitioner takeaway: Trust the assistant’s judgment only after you have bounded its authority; fluency is not a control, but scoped execution with human approval can be.
Related resources from NHI Mgmt Group
- How do security teams decide whether a coding assistant is suitable for sensitive work?
- How do IAM teams decide whether an AI security assistant needs its own access controls?
- How do teams decide whether to keep weak fallback methods in sensitive flows?
- How can teams decide whether to automate actions from a live digital twin?