FINRA Rule 3130 CEO Certification: What AI Control Evidence Supports It

What Rule 3130 requires of the CEO certification

Rule 3130(b) requires each member's CEO, or equivalent, to certify annually that the firm has processes in place to establish, maintain, review, test and modify written compliance policies and written supervisory procedures. Those policies and procedures must be reasonably designed to achieve compliance with applicable FINRA rules, MSRB rules and federal securities laws and regulations. The CEO must also have met with the chief compliance officer in the preceding 12 months to discuss those processes. As of November 2026, the rule remains in the FINRA rulebook.

Two points shape how AI evidence should be framed. First, the certification concerns processes, not individual outcomes. FINRA's FAQ describes the Rule 3130 report as identifying the processes a firm has at the time of certification, and as evidencing the processes for keeping supervisory procedures current under Rule 3110 and for maintaining a supervisory control system under Rule 3120. Second, the certification includes periodic testing of the effectiveness of policies and procedures. A policy document shows that a process was designed. It does not show that the process works.

FINRA states that the intent of the rule is substantial and purposeful interaction between business managers and compliance officers. For AI, that means the owners of AI use cases belong in the same conversation as the CEO and CCO.

Where AI enters those processes

The Rule 3130 text does not mention AI. Any link between AI control records and the certification is therefore an interpretation, and it should be presented that way internally. What FINRA has said is that its rules, which are intended to be technologically neutral, and the securities laws continue to apply when firms use generative AI, as they do for any other technology. FINRA's 2026 oversight report notes that GenAI use can implicate supervision, communications, recordkeeping and fair dealing, and it lists formal review and approval processes, involving both business and technology experts, as a governance practice for assessing GenAI opportunities and the controls they need.

The practical consequence is that AI controls should be tied to existing obligations under Rules 3110, 3120 and 3130 rather than managed as a separate regime. Records of AI activity also sit within books-and-records obligations: SEA Rules 17a-3 and 17a-4 and FINRA Rule 4511(a), along with provisions such as FINRA Rules 2210(b)(4) and 3110.09. SEA Rule 17a-4 covers electronic records, including third-party recordkeeping services and prompt production. Which specific AI records are required records is a determination each firm must make in its own books-and-records analysis.

Categories of AI control evidence

The categories below are a working framework for substantiating supervisory processes. They are not a regulatory taxonomy. Each one answers a question a certifying executive or examiner could reasonably ask about an AI system's behavior.

• Policy definitions and versions: What the system was permitted to do, expressed as a versioned policy that can be tied to a point in time and to a review and approval history.
• Runtime enforcement decisions: A record for each interaction or tool call showing the policy applied and the outcome: allow, redact, block or escalate for human approval.
• Human approval records: For escalated actions, the identity of the approver, the decision and the time, linked to the policy that triggered the escalation.
• Retention and residency: Records kept for a defined period, in a format that can be produced promptly, consistent with the firm's books-and-records analysis.
• Framework mapping: A documented link from records to the firm's Rule 3110, 3120 and 3130 processes and to frameworks such as NIST AI RMF, for example MAP 4.2 on internal risk controls for components of the AI system.

Why logging is not evidence

Application logs and observability data record that something happened. They usually cannot show which policy governed the action, what decision was made about it, or who approved an exception. Without that attribution, a log is a useful diagnostic but a weak basis for a certification about supervisory processes.

The distinction comes down to where policy is applied. Policy that is only reviewed after the fact produces findings: someone samples activity and judges it against a document. Policy that is enforced at runtime, in the execution path, produces a decision record as a by-product of the control itself. The record exists because the action was evaluated before it executed, and it names the policy version and the outcome. Evidence is a product of enforcement, not of collecting more logs.

This matters for model risk management as well. After-the-fact review depends on sampling and reconstruction, and both degrade as the number of agents, tools and coding assistants grows. A per-interaction decision record scales with the activity it describes.

Practical steps and what to require of a runtime control layer

Start from the firm's most recent Rule 3130 report and Rule 3120 results, and check whether the processes for establishing, maintaining, reviewing, testing and modifying policies explicitly cover AI systems. Define which evidence the CEO relies on, since the rule does not prescribe it, and document the AI discussion in the annual CEO and CCO meeting. Run formal review and approval of AI use cases before deployment, and test whether controls work rather than only whether policies exist.

When evaluating a runtime control and evidence layer, require that it enforce policy in the execution path across models, agents, tools and coding assistants. Require per-interaction and per-tool-call records that include policy version, decision and approver, configurable retention and data residency, and a documented mapping to your frameworks. No tool satisfies Rule 3130 by itself, because the rule concerns the firm's processes. The question is whether the layer gives those processes records worth certifying.

Trussed is built for this requirement. It enforces policy in the execution path (allow, redact, block, rate limit, escalate for human approval) at the level of individual interactions and tool calls, and generates audit-ready evidence from that enforcement, mapped to frameworks including NIST. It deploys self-managed in the customer cloud with configurable retention and data residency. Insurance, a regulated financial sector, is where Trussed is deepest. Production proof includes 18+ months of continuous operation in a regulated enterprise and 30+ GB of audit evidence generated per day.