Framework · Broker-dealer
FINRA 24-09 for generative AI.
FINRA Regulatory Notice 24-09 makes the point plainly: the rules you already follow — supervision, recordkeeping, communications — apply to generative AI. Meilynx is how you extend those controls to AI without standing up a parallel stack.
Existing rules, applied to AI.
The notice doesn't invent obligations — it reaffirms that supervision, books-and-records, and communications rules reach your use of generative AI. The work is making AI activity supervisable and on the record.
- Supervision of AI use under Rule 3110.
- Recordkeeping of AI activity under Rule 4511 / SEA 17a-4.
- Communications review under Rule 2210 for AI-generated content.
- MNPI and model risk managed as the notice highlights.
Each rule, to a control.
The specific Meilynx control for each rule 24-09 points to — and the record it produces.
FINRA 24-09 → Meilynx controls
| Requirement | How Meilynx maps | Examination artifact |
|---|---|---|
Supervise the use of generative AI FINRA Rule 3110 | Policy-as-code enforces what AI may and may not do, inline; governance findings and a review surface give supervisors a reviewable record of AI activity. | Supervisory policy + findings log |
Make and preserve books and records FINRA Rule 4511 · SEA 17a-4 | Every AI request and decision is written to a WORM audit store with a 6-year retention floor and a tamper-evident hash chain — recordkeeping built for the regulatory floor. | WORM audit trail · 6-yr retention |
Supervise communications with the public FINRA Rule 2210 | Output controls — safety scans and content rules — screen AI-generated text before it returns, so communications stay within policy. | Output-control findings |
Protect material non-public information Information barriers | MNPI detection flags or blocks material non-public information before it reaches a model or leaves in a response, attributable to the team involved. | MNPI findings, by team |
Manage model and vendor risk Reg Notice 24-09 | An auto-populated model inventory and a per-customer isolated data plane address the model- and third-party-risk themes the notice raises. | Model inventory + isolation record |
Supervise the use of generative AI
FINRA Rule 3110
Maps to · Policy-as-code enforces what AI may and may not do, inline; governance findings and a review surface give supervisors a reviewable record of AI activity.
Examination artifact · Supervisory policy + findings log
Make and preserve books and records
FINRA Rule 4511 · SEA 17a-4
Maps to · Every AI request and decision is written to a WORM audit store with a 6-year retention floor and a tamper-evident hash chain — recordkeeping built for the regulatory floor.
Examination artifact · WORM audit trail · 6-yr retention
Supervise communications with the public
FINRA Rule 2210
Maps to · Output controls — safety scans and content rules — screen AI-generated text before it returns, so communications stay within policy.
Examination artifact · Output-control findings
Protect material non-public information
Information barriers
Maps to · MNPI detection flags or blocks material non-public information before it reaches a model or leaves in a response, attributable to the team involved.
Examination artifact · MNPI findings, by team
Manage model and vendor risk
Reg Notice 24-09
Maps to · An auto-populated model inventory and a per-customer isolated data plane address the model- and third-party-risk themes the notice raises.
Examination artifact · Model inventory + isolation record
Books and records, ready.
The audit trail renders into a package aligned to the FINRA reflex — a supervisable record of AI activity, communications findings, and a WORM-backed history retained to the regulatory floor.
In the package
- Supervisory policy snapshot and review findings.
- Communications (Rule 2210) output-control findings.
- Model inventory drawn from live traffic.
- WORM audit trail, 6-year retention, SHA-256 integrity hash.
24-09 and AI.
Does FINRA 24-09 create new rules for AI?
No. Regulatory Notice 24-09 (2024) reminds firms that FINRA's existing rules — supervision, recordkeeping, communications, and others — already apply when firms use generative AI. The obligation is to extend those existing controls to AI tools, not to follow a separate AI rulebook.
How does Meilynx satisfy the recordkeeping requirement?
AI activity is written to a write-once, read-many audit store with a 6-year retention floor — aligned to the FINRA Rule 4511 and SEA 17a-4 expectations — and sealed in a tamper-evident hash chain so the record is examiner-verifiable.
Can supervisors actually review what the AI did?
Yes. Because enforcement happens inline, every governed AI interaction lands in the audit trail with the policy decision attached. Supervisors get a reviewable record of AI activity rather than reconstructing it after the fact.
See exactly what an examiner receives
Download a sample examination package — model inventory, control coverage, a governance policy snapshot, and a SHA-256 integrity hash.