LLM Governance Tools for Broker-Dealers and Wealth Managers: Evaluation Criteria

What examiners expect LLM governance to evidence

FINRA has stated that its existing rules apply to generative AI, whether a member firm builds a tool for proprietary use or relies on a third party's technology, including embedded features in existing third-party products. Regulatory Notice 24-09, published in June 2024, is a reminder of existing obligations rather than a new rule set. As of October 2026, FINRA's 2026 Annual Regulatory Oversight Report continues to address GenAI supervision and governance practices.

The practices FINRA describes are specific. Firms should evaluate GenAI tools before deployment and confirm they can still comply with the rules that apply to the business use. They should run a formal review and approval process involving business and technology experts. They should maintain a supervision, governance or model risk management framework covering development, implementation, use and monitoring, with comprehensive documentation. Notice 21-29 adds that the supervisory system, including written supervisory procedures, extends to vendors and sub-vendors. It also describes Regulation S-P Rule 30 safeguards for customer records, which bear on what data an LLM workflow can reach or expose.

Two kinds of evidence follow from this. One shows design and approval: what was reviewed, tested and authorized. The other shows operation: what the LLM was allowed to do and what it did. Tool categories differ sharply in which they produce. A tool can support the framework but does not replace it, and due diligence remains the firm's responsibility. Retention periods and recordkeeping and communications mapping should be set with counsel against the firm's own obligations.

Six governance functions and what each leaves open

These categories are evaluation lenses, not regulatory classifications, and products frequently span more than one. The useful question for each is where it operates, what it can control and what evidence it yields.

• Inventory and GRC: Operates outside the request path. Records use cases, owners, approvals and documentation. Evidence shows design and approval decisions, not individual interactions.
• Pre-deployment testing: Operates before release. Documents test sets, metrics and results. Evidence is point-in-time and does not constrain production behavior.
• Observability: Operates after the fact from traffic and telemetry. Detects, alerts and supports investigation. Shows what happened, but not necessarily what was permitted, and cannot stop an action.
• Gateway routing: Operates as a central route to models. Coverage depends on all traffic actually passing through it, and policy depth varies by product. It may not see vendor-embedded LLM features.
• Runtime policy enforcement: Operates in the execution path. Can allow, redact, block or escalate for human approval before an action takes effect. Evidence ties a policy decision to the action taken.
• Evidence generation: Often a feature of the other categories rather than a product of its own. Quality depends on durability, retention, completeness and exportability, and varies widely.

Six criteria for evaluating LLM governance tools

Enforcement point. Establish whether the tool acts before an action takes effect or reports on it afterward. For supervision, the difference is between preventing a disallowed output and documenting that one occurred. Ask whether it can escalate to a human approver and hold the action until a decision is recorded.

Scope of control. Compare what the tool governs: prompts, retrieved content, model outputs, downstream agent actions and any thresholds on automated actions. OWASP's RAG guidance frames controls across the whole pipeline, from ingestion to output validation and agent integration, so a tool that governs one stage leaves the others open. Also ask whether coverage reaches vendor-embedded LLM features, given FINRA's position on third-party products.

Evidence durability. Ask what is recorded about what the LLM was permitted to do and what it did, how long it is retained and whether it can be exported in a form a supervisory or examination team can use. Records that link a specific policy to a specific action are more useful than raw activity data.

Deployment model. Determine whether the tool can run in the firm's own environment or only as a hosted service, and what that means for customer-record safeguards and vendor supervision.

Data residency. Establish where prompts, outputs and evidence reside, and whether location and retention are configurable. The firm should define its own requirements first.

Regulatory mapping. Ask which obligations the vendor claims to help evidence, such as supervision, documentation, model risk management and customer data protection, and request the documentation behind each mapping. A mapping to a different framework does not establish coverage of FINRA expectations.

Gaps a single category leaves open

Each category answers a different part of the model risk management question. Inventory and testing tools support documentation and pre-deployment evaluation, which FINRA and the NIST AI RMF both emphasize. NIST Measure 2.1, for example, calls for documenting test sets, metrics and the tools used. Neither shows what happened in production.

Observability shows production activity but generally after the fact. Gateways narrow the number of paths to models, but only for traffic routed through them. Runtime enforcement decides at the moment of use and records the decision, but it does not replace the inventory, the approval process or pre-deployment testing that FINRA expects around it.

FINRA's broader AI report also notes thresholds and guardrails on automated actions, using an ML trading example. It is not about LLMs, but it shows regulators pay attention to limits placed on what automated systems can do. For agents that call tools, that points toward controls on individual tool calls, not only on text output.

Where runtime enforcement fits

Trussed operates in the runtime control category. It enforces policy in the execution path across models, agents, tools and MCP servers, scoped to individual interactions and tool calls, with actions that include allow, redact, block, rate limit and escalate for human approval. Enforcement is at runtime, not reviewed after the fact, and it generates audit-ready evidence of what the AI was allowed to do and what it did. Trussed is not an inventory, GRC, gateway, observability or pre-deployment testing product, so it would sit alongside those functions.

Deployment is self-managed in the customer cloud, with configurable retention and data residency. Evidence mappings cover HIPAA, NAIC, NYDFS, the EU AI Act and NIST. Insurance is the regulated financial sector where Trussed is deepest, and the same enforcement and evidence model applies to the examiner scrutiny risk leaders at broker-dealers and wealth managers face. Any mapping to FINRA expectations should be tested against the criteria above, as with any vendor.