Common signs include controls that inspect prompts but ignore tool calls, policies that mention safety but not permissions, and red-team tests that expose actions the policy never expected. If the system can still leak instructions, retrieve sensitive data, or execute unsafe workflows, the policy is describing intent rather than enforcing boundaries.
What it looks like when policy and execution have diverged
An AI policy is usually out of sync when it describes intent in broad language but never binds the system’s actual decision points. That gap shows up when the policy is written around prompts, content, or user statements, while the real risk is created by tool invocation, data access, model outputs, and downstream actions. The clearest signal is simple: the policy sounds strong, but the system can still do the thing the policy claims to prevent.
One common failure mode is control misalignment. A policy may prohibit unsafe answers yet leave permissions, connectors, and action execution untouched, so the model can still retrieve sensitive data or trigger workflows. Another is scope drift, where the policy covers the chat interface but not the agent runtime, orchestration layer, or external services that actually carry the impact.
That is why policy quality should be judged against observable behaviour, not wording alone. If the system can still leak instructions, make unauthorized calls, or cross a trust boundary the policy never named, then the document is acting as governance language, not an enforceable control.
Evidence that the policy is not controlling the system
The strongest signs are behavioural mismatches. Prompts are inspected, but tool calls are not. Safety rules are stated, but permissions are never defined. Red-team tests show the model taking actions that the policy never anticipated, especially when those actions happen through connectors, plugins, APIs, or delegated credentials.
A second sign is selective enforcement. The policy may apply in low-risk demos but fail in real workflows, where the agent has access to production data, internal documents, or task automation. If the control only works when humans behave perfectly, it is not controlling the system path that matters.
Another indicator is missing boundary language. Good policy ties the allowed behaviour to specific identities, tools, datasets, approval points, and escalation thresholds. Weak policy stays abstract, which makes it hard to test, hard to audit, and easy to bypass through ordinary product changes. For agentic systems, a useful starting point is a Agentic AI Security Policy Template that treats registration, tool access, oversight, and retirement as control points rather than prose.
How to tell whether the policy is enforceable, not aspirational
Test the policy at the same layer where the system makes decisions. If the system can call tools, read files, send messages, or invoke APIs, the policy should constrain those actions directly, not just the surrounding conversation. If a red-team prompt can persuade the model to exceed its intended scope, the policy is probably targeting content moderation rather than operational authority.
Policy also needs a traceable enforcement path. Someone should be able to show which rule blocked which action, which approval was required, and which exception allowed a high-risk operation. Without that evidence chain, the policy may still be useful for training or governance, but it is not yet a dependable operational boundary.
For mature programmes, alignment should extend to audit evidence and legal or regulatory mapping. A policy that governs agent behaviour, human oversight, recordkeeping, and high-impact actions is much more likely to hold up under review when it is paired with a structured governance model such as the Agentic AI Compliance Guide and with external governance expectations like ISO/IEC 42001:2023 AI Management System Standard and the NIST AI Risk Management Framework.
Risk and Threat Considerations
When AI policy does not match real behaviour, the organisation gets a false sense of control. The risk is not only that the policy is weak on paper, but that attackers or careless users can exploit the gap between stated intent and actual authority to reach data, tools, or workflows the policy was supposed to constrain.
Failure mechanism: The policy is written at the conversational layer while the real exposure sits in runtime permissions, connectors, and tool execution, so unsafe behaviour remains possible even when the text looks restrictive.
Impact: Sensitive data exposure, unauthorized actions, failed audits, and missed escalation conditions become more likely, especially when the system is allowed to act through integrations the policy never explicitly governs.
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 addresses the attack surface, NIST AI RMF and NIST SP 800-53 Rev 5 set the technical controls, and ISO/IEC 42001:2023 defines the regulatory obligations.
| Framework | Control / Reference | Relevance |
|---|---|---|
| OWASP Agentic AI Top 10 | ASI03 — Identity & Privilege Abuse | Policy drift often leaves agent actions and authority unconstrained. |
| ASI02 — Tool Misuse | The mismatch shows up when tools execute outside the policy's intent. | |
| Recommendation — Bind agent permissions to explicit action boundaries and approval rules. Restrict tool invocation to approved use cases and enforce per-tool controls. | ||
| NIST AI RMF | GOVERN — Govern | This is an AI governance gap where intent must map to operational controls. |
| Recommendation — Translate policy into measurable governance, accountability, and review mechanisms. | ||
| ISO/IEC 42001:2023 | 4.1 — Understanding the organization and its context | AI policy must reflect how the system actually operates in context. |
| Recommendation — Align policy scope to real system behaviour and operating context. | ||
| NIST SP 800-53 Rev 5 | AU-6 — Audit Record Review, Analysis, and Reporting | Behaviour-policy gaps are exposed by logs and review of real actions. |
| Recommendation — Review audit records for actions the policy should have prevented or flagged. | ||
Practitioner Guidance
What to verify: Validate policy against actual system actions, not just prompt responses. The minimum test is whether the policy can explain, constrain, and prove control over tool use, data retrieval, and any action that changes state outside the chat window.
Decision rule: If the policy does not name the system’s real authority boundaries, treat it as incomplete even if it reads well. If the policy cannot survive a basic red-team exercise without surprise actions, it needs control redesign before it needs better wording.
Practitioner takeaway: The right question is not whether the policy sounds safe, but whether the system can still do unsafe things when the policy is assumed to be in force.
Related resources from NHI Mgmt Group
- What are the signs that an AI system has not been tested against real attack patterns?
- What are the signs that an AI system is failing to represent real-world users accurately?
- What are the signs that youth AI safety controls are missing real-world behaviour?
- What makes agentic AI an NHI governance issue?