Proving LLM Governance Policies Are Actually Enforced at Runtime

Documentation, detection and enforcement are different controls

Most regulated enterprises have a written position on LLM use: standards, acceptable-use documents and review boards. These establish what should happen. They do not show what did happen on a specific request at a specific time.

Detection sits one step closer. Output screening, log review and anomaly flags observe behavior, but they act after the fact. OWASP guidance describes output screening as catching the consequences of an attack after the fact, and input screening as not a security boundary, because LLMs do not enforce a separation between instructions and data. Screening can flag content for logging, review or rate limiting, but a flag is a detective signal, not proof that something was prevented.

Enforcement means a component outside the model decides whether an action proceeds. The model generates text; it does not enforce policy. The agent can propose an action, but a policy service or execution component should independently validate scope, privilege and approval state before anything runs. Only in that arrangement is the decision itself a fact you can present, rather than a reconstruction.

What each common approach can and cannot prove

The test for any control is whether it can show the decision made on one specific interaction. The table below applies that test.

• Policy documents and standards: Show stated intent and ownership. Cannot show that any single interaction was evaluated against them.
• Pre-deployment review: Shows a configuration was assessed at a point in time. Cannot show behavior in production after prompts, tools or models change.
• Application-level checks: Show a check exists in one application. Coverage varies by application, model and tool, so consistency is hard to demonstrate.
• Post-hoc log review: Shows what was observed and reviewed later. Cannot show a violating action was stopped, and logs may be incomplete or reconstructed.
• Manual attestation: Shows a person's assertion that controls operate. It is not evidence generated by the control itself.
• Enforcement in the execution path: Produces a decision record per interaction or tool call, generated at the moment the policy was applied.

Mapping the evidence to regulatory expectations

The EU AI Act ties verification of compliance to record-keeping and traceability for high-risk AI systems. Recital 71 describes records and technical documentation as the information needed to assess compliance, and provider financial institutions must keep automatically generated logs of high-risk systems as part of the documentation maintained under financial services law. The Act does not state that logs must contain a per-interaction policy decision. Treating decision records as the strongest form of that evidence is a reasonable engineering reading, not a stated legal requirement. Confirm with counsel whether a system is high-risk, and check current obligations as of October 2026, since the Regulation has since been consolidated with amendments.

NIST AI RMF is framed around accountability mechanisms, roles and responsibilities. In practice, that means named owners for policy definition, enforcement operation and evidence review, so someone is accountable for each part of the chain.

For HIPAA, NAIC and NYDFS, work with compliance counsel to map each field in your evidence standard to the specific control statements that apply to you. The useful discipline is the same across frameworks: each control claim should point to a record produced when the control operated, not to a policy page or a reviewer's recollection.

Where Trussed fits

Trussed enforces policy in the execution path across models, agents, tools, MCP servers, AI sites and coding assistants. Decisions are allow, redact, block, rate limit or escalate for human approval, applied to individual interactions and tool calls and enforced at runtime, not reviewed after the fact. That enforcement generates continuous audit-ready evidence, mapped to HIPAA, NAIC, NYDFS, the EU AI Act and NIST.

Trussed deploys self-managed in the customer cloud, with configurable retention and data residency. In production, it has run continuously for 18+ months in a regulated enterprise at 99.95% uptime on live traffic, with 100+ models under one policy plane and 30+ GB of audit evidence generated per day. Customers span four regulated verticals: insurance, pharma, healthcare and education.

The criteria above apply to any approach you evaluate. They are most useful when you run each one against your current controls first: if a control cannot produce a per-call decision record, it is documentation or detection, and should be described that way to an auditor.