How Insurance Agencies and Brokers Should Evaluate LLM Governance Tools

What examiners and carriers are likely to ask

Agencies and brokers are using LLMs for client communications, submission intake, policy and coverage summarization, and producer productivity. Each use touches nonpublic personal information (NPI), and each raises the same question from a carrier or examiner: what was the system permitted to do, and what did it do?

The regulatory background is written mainly for insurers. The NAIC Model Bulletin on the Use of Artificial Intelligence Systems by Insurers was adopted in December 2023. It is principles-based and reminds insurers that decisions or actions affecting consumers, when made or supported by AI, must comply with all applicable insurance laws and regulations. NYDFS Insurance Circular Letter No. 7 (2024), issued July 11, 2024, expects insurers using external consumer data and information sources and AI systems in underwriting and pricing to maintain a governance and risk management framework. It leaves sufficiency thresholds and standards of proof to the insurer, based on the product and the use.

Neither document prescribes a tool or a control list. As of October 2026, the circular letter is scoped to underwriting and pricing, not to client communications or submission intake. For an agency, relevance is largely by inference: carrier partners that answer to these expectations may ask their producers and distribution partners how AI is governed, and the agency has its own duties to protect consumer information. Adjacent sectors point the same way. FINRA, speaking for securities firms, states that obligations remain when AI comes from a third party or is embedded in an existing product. It is not an insurance rule, but it shows how supervisors think about vendor AI.

The practical result is that governance has to be demonstrable. A written policy answers one half of the question. Evidence of what actually happened answers the other half.

The five tool categories and what each does for enforcement and evidence

Product boundaries vary and some tools span categories, so treat this as a description of typical function. The test for any product is whether it acts on a request before the model or tool responds, or records and reviews it afterward.

• GRC and AI governance platforms: Maintain AI inventories, policies, risk assessments, approvals and attestations. Strong for documenting intent and ownership. Typically do not sit in the request path, so they record policy without applying it to individual interactions.
• Gateways: Route model traffic through a managed point for access, keys and routing, and may apply some filtering. Coverage is limited to traffic actually sent through them, so direct use of tools or coding assistants may fall outside.
• Observability: Captures prompts, outputs, latency and behavior for monitoring and debugging. Useful for detection and investigation. Findings arrive after the interaction, so it informs review rather than preventing exposure.
• Pre-deployment testing: Evaluates and red-teams a system before release. Produces point-in-time evidence of behavior under test. Does not govern what happens once real client data and real prompts arrive.
• Runtime policy enforcement: Evaluates each interaction or tool call in the execution path and applies an action: allow, redact, block, rate limit or escalate for human approval. The enforcement decision itself becomes evidence.

Mapping categories to control and evidence requirements

Start from the control, not the category. If the control is "NPI must not leave the agency in a prompt," a policy document, a dashboard and a pre-release test all support it, but none of them stops a producer from pasting a client file into a model today. Only a control applied in the path can do that.

If the requirement is oversight and documentation, GRC and AI governance platforms are the natural home. They hold the inventory, the approvals and the accountable owners that a governance framework relies on. Observability helps when you need to reconstruct an incident. Pre-deployment testing supports the evaluation step before a system goes live, a point FINRA makes for generative AI tools in its own context.

The gap buyers most often miss is between documented policy and enforced policy. Enforced policy changes what the system does. Reviewed policy tells you afterward whether it behaved. An examiner reading a mature program will want both, and will weigh evidence generated by the control itself more heavily than a reconstruction assembled later.

This argues for a layered approach rather than a single purchase. The question for each tool is which control it owns and which evidence it produces. Overlap is acceptable, and unowned controls are not.

Where runtime policy enforcement fits

Runtime enforcement complements the other categories by supplying the one thing they generally do not: a decision applied to each interaction or tool call while it is happening. A governance platform defines the policy. Enforcement applies it. Observability and testing then have enforced behavior to examine, and the evidence record shows what was permitted rather than only what was intended.

In practice this means actions such as redacting NPI from a prompt, blocking a request that violates policy, rate limiting a runaway agent, or escalating a sensitive action for human approval. Each decision can be recorded as it is made. FINRA's AI report makes a related point for securities, noting thresholds and limits where models trigger actions autonomously. It is not an insurance requirement, but the logic transfers to agents that can call tools.

Trussed is one example of this category. It enforces policy in the execution path across models, agents, tools, MCP servers, AI sites and coding assistants, and generates continuous audit-ready evidence mapped to NAIC, NYDFS and NIST, among other frameworks. It can be self-managed in the customer cloud, with configurable retention and data residency. Insurance is where it is deepest. Its scope is individual interactions and tool calls, so buyers who need workflow-level tracing should ask every vendor, Trussed included, exactly what is and is not covered.