AI compliance tools have landed on the same marketing layer. The architecture underneath hasn't. If your system can't produce a complete, timestamped, attributed record of every AI action, the feature set doesn't matter much in an exam. Here's what the structural side of a defensible compliance AI setup actually requires.
TLDR:
- "Technology neutral" means existing rules (Rule 3110, Rule 2210, Rule 17a-4) apply fully to your AI tools.
- The SEC charged two advisers a combined $400,000 for AI disclosure gaps, not AI failures.
- Defensible AI architecture needs tamper-evident logs, attribution chains, and named principal sign-off. A review interface alone isn't enough.
- Greenboard's AI supervision scores 100% of captured communications; random sampling only covered a fraction of messages.
- Greenboard builds archiving, supervision, marketing review, and trade pre-clearance into one attributed, exam-ready audit trail.
Why the regulatory baseline matters more than the AI features
FINRA's position on AI has been consistent since Regulatory Notice 24-09: no new rules. Supervision, recordkeeping, communications, Reg BI: the existing rulebook applies to AI the same way it applies to any other tool a firm uses.
That sounds reassuring, but "no new rules" doesn't mean "no new exposure." It means the burden falls on firms to map fast-moving tech onto a rulebook that wasn't written with it in mind, and to do that before an examiner asks how. The FINRA 2026 Annual Regulatory Oversight Report went further, adding a standalone GenAI section and, for the first time, explicit guidance on autonomous AI agents.
"Technology neutral" is regulator shorthand for your problem, not their problem. The question isn't whether your AI vendor has impressive features. The question is whether your AI architecture holds up when Rule 3110 or Rule 2210 applies to it.
The SEC's AI examination posture in 2026
The SEC's FY2026 Examination Priorities make the ask explicit: examiners will review whether firms have AI-related compliance policies, whether disclosures about AI use are accurate, and whether those disclosures match what the firm is actually doing. The document directly calls out investment advisers and broker-dealers that use AI in their investment processes or client-facing operations, flagging the risk that conflicts of interest and investor protection gaps go unaddressed when AI use outpaces the governance around it.
That last part is where firms have gotten into trouble. In March 2024, the SEC brought settled charges against two investment advisers, Delphia and Global Predictions, for making false and misleading statements about their AI capabilities. The firms paid $400,000 in total civil penalties. The charges weren't about the AI failing to perform. They were about the gap between what firms claimed and what they built.
Exam staff aren't asking whether you use AI. They're asking whether you can show them the governance behind it: written policies, testing records, disclosure review, and supervisory controls that map to the specific ways AI touches your compliance program. If you can't produce that documentation, the feature list on your vendor's website won't help you.
What compliance-first AI architecture actually looks like
Most AI compliance tools are built around a general-purpose LLM with a compliance-themed interface layered on top. The underlying architecture wasn't built for a compliance environment. That gap matters when an examiner asks you to produce an audit trail. See our detailed look at LLMs.
A defensible compliance AI architecture has a few non-negotiable structural properties:
- Tamper-evident audit logs that record every AI action, input, and output in an immutable, timestamped format
- Access controls scoped to individual operations, beyond login sessions alone
- Attribution chains that tie every AI-generated flag, suggestion, or decision to a named human who reviewed and approved it
- Hallucination controls that ground AI outputs in your firm's actual policies and records, with cited sources instead of generated summaries
The attribution chain is the piece most vendors skip. If your AI surfaces a supervision alert and a reviewer dismisses it, that dismissal needs to be logged to a specific person, at a specific time, with a reason. "The system reviewed it" is not a supervisory procedure. Rule 3110 requires a principal. Your architecture needs to know who that is and prove it.
Human oversight isn't a UX feature. It's the load-bearing wall.
The recordkeeping constraint: what rule 17a-4 requires of AI systems
SEC Rule 17a-4 governs how broker-dealers retain electronic records. The 2023 amendments preserved traditional WORM (Write Once, Read Many) storage while opening a second path: an audit-trail alternative that logs every record creation, modification, and deletion in a tamper-evident format, as detailed in the SEC's final rule on electronic recordkeeping. Firms choose between these approaches, but the underlying requirement hasn't changed. Records must be preserved in a non-rewriteable, non-erasable format, or under a system that makes any alteration immediately detectable.
The problem AI creates here is straightforward. When an AI system generates a flag, a review summary, a policy interpretation, or a supervision decision, that output is a record. If the system producing it wasn't built on a compliant retention architecture, the record it creates may be unfixable after the fact. You can't retroactively WORM-certify an AI output stored in a mutable database.
The failure mode regulators are increasingly flagging isn't firms ignoring recordkeeping. It's firms deploying AI tools that were never architected to produce 17a-4-compliant outputs in the first place, then finding the gap during an exam.
The explainability problem in securities AI
Regulators don't want to see what your AI decided. They want to know why. A September 2025 BIS paper on AI explainability identified the limited explainability of complex AI models as a real challenge for both financial institutions and supervisors, a finding that carries particular weight in a securities compliance exam. When a supervised communications flag or a marketing review decision can't be traced to a specific, articulable reason, the entire output becomes suspect in an exam.
The issue is structural. Many AI models produce outputs that even their builders can't fully explain after the fact. In a compliance context, that's untenable. "The model scored it high" is not a supervisory rationale. The architecture needs to surface the reasoning, and the result alone is never enough.
Human review doesn't fix a black box. It wraps one.
How AI supervision replaces random sampling in communications review
Random sampling was never a clean solution. It was a practical one. Under Rule 3110, firms built supervision programs around reviewing a percentage of communications, accepting that the method would miss things, and hoping the things it missed weren't material.
AI supervision changes the denominator. Instead of sampling from the population of communications, the system scores the full population and surfaces only what clears a risk threshold. Reviewers spend their time on flagged content, with AI doing the initial triage pass across every message instead of a random slice of them.
The practical difference is real. A compliance team that previously reviewed 5% of emails is now working from a ranked queue covering 100% of captured communications. A problematic string of messages between an advisor and a client that never appeared in the random draw will surface because the model saw all of them.
The tradeoff firms often underestimate is false positive volume. A poorly tuned AI supervision model trades sampling gaps for alert fatigue, flooding reviewers with low-quality flags. The architecture question isn't whether the model scans everything. It's whether the model's scoring is calibrated well enough that reviewers can trust the queue. A high-recall, low-precision system looks like this in practice: every queue clears because reviewers learn to dismiss flags without reading them, and the supervisory sign-off that Rule 3110 requires becomes a formality, not a review. That accountability gap still lands on the named principal.
Human review stays the accountability layer regardless. AI changes what gets in front of reviewers, not who owns the decision.
Data pipeline and capture architecture for eComms compliance
AI supervision starts at capture, not at the model. Whatever the system can't ingest, it can't score, and whatever it can't score won't appear in the review queue regardless of how well the model is tuned.
The practical gap most firms are sitting on is channel breadth. Email capture has been standard for years, but registered representatives communicate across WhatsApp, iMessage, Slack, Microsoft Teams, LinkedIn, and Telegram. If those channels aren't integrated into the capture layer, the AI supervision architecture has a blind spot that's structural, not correctable through better models.
Regulators have been explicit here. Supervision obligations under Rule 3110 extend to all business communications, regardless of medium. A firm that captures email but not WhatsApp has a defensible program for one channel and an unexamined exposure everywhere else. The AI can't fix that. The capture layer has to.
Integration depth matters as much as channel breadth. A capture integration that pulls messages but drops attachments, strips metadata, or introduces ingestion delays creates records that may not hold up under Rule 17a-4 review. Complete records means complete records: content, context, timestamps, and sender attribution intact from channel to archive.
Written supervisory procedures and AI governance documentation
WSPs written before an AI tool was deployed are the most common documentation gap examiners find. The procedure describes a workflow that no longer matches how supervision actually happens, and that mismatch is visible the moment an examiner asks a reviewer to walk through a flagged communication.
When AI is doing any part of the supervisory work, the WSP needs to describe that work in detail. Generic language about "reviewing electronic communications" won't cover an AI-assisted triage queue. Examiners want to see the procedure match the actual process.
A WSP for an AI-assisted supervisory workflow should include:
- A description of what the AI system does and does not do in the review process
- How the firm tested the model before deployment and how testing is ongoing
- Who reviews AI-generated flags, at what frequency, and under what escalation path
- How the firm handles edge cases where AI output conflicts with reviewer judgment
- The named principal, by role, who signs off on supervisory reviews
Model governance documentation sits alongside the WSP, covering version history, configuration changes, threshold adjustments, and known model limitations. If a firm adjusts the sensitivity of a flagging model, that change needs a documented rationale. Examiners are increasingly asking about this, particularly for firms that have tuned down alert volume without a written record of why.
The useful framing: if your AI vendor updated the model last quarter, does your WSP reflect that, and can you show an examiner that someone reviewed the change before it went live? If the answer to either part is no, the documentation layer has a gap that no AI feature set will close.
How employee compliance workflows intersect with AI architecture
Employee compliance workflows carry the same defensibility requirements as communications supervision, but they're often treated as a separate system with separate logs and separate documentation. That gap is visible in exams.
Personal trading pre-clearance, a core piece of personal trading compliance software, is the clearest example. When an employee submits a trade request, AI can check it against existing positions, firm compliance policy, restricted lists, and blackout periods before any human sees it. Clear cases can be auto-approved; ambiguous ones route to a principal. The requirement is the same as in communications review: every step needs a timestamped, attributed, tamper-evident record. Who submitted the request, what the AI checked, what it returned, who approved or denied it, and when. "System approved" is not sufficient documentation. The named principal responsible under your supervisory structure needs to be in that log.
The same logic applies to attestations, outside business activity disclosures, and gifts and entertainment forms. AI can pre-check a submitted OBA against firm policy and flag conflicts before the reviewer opens it. It can verify that a gift disclosure falls within thresholds, or surface a pattern across multiple submissions that individually look fine. The human reviewer who signs off on the outcome remains the accountability anchor.
Where fragmentation creates exam risk
Firms run into trouble when pre-clearance lives in one system, attestations in another, and communications supervision in a third. When an examiner asks for a complete picture of how a specific employee's compliance posture was monitored over a period, pulling that record across three systems with three separate audit logs is slow, incomplete, and hard to defend. A unified architecture, operating as a true system of action, produces a coherent record that holds together under scrutiny.
Marketing compliance and rule 2210: where AI review intersects with advertising obligations
FINRA Rule 2210 and the SEC Marketing Rule share a common structure: communications must be reviewed and approved before use, disclosures must be present and accurate, and the record of that review must be retained. The AI question is whether the tool assisting that review is actually creating a compliant record, or just helping a reviewer catch issues before they submit the piece somewhere else.
AI pre-check in marketing compliance software review works as a first pass before human approval. The system reads a draft ad, social post, or pitch deck against Rule 2210 standards, flags potentially unbalanced claims, identifies missing required disclosures, and returns specific reword suggestions before a human reviewer opens the document. Reviewers then spend time on judgment calls, not scanning for disclosure gaps the AI already caught.
The structural distinction that matters for exams is what the AI creates, not what it flags. When an examiner pulls the review history for a specific piece of marketing material, that record needs to show the complete sequence: who submitted it, what the AI flagged, what a reviewer decided, and who gave final approval, all in one timestamped, tamper-evident log. A pre-check that lives in a separate system from the approval workflow produces two disconnected records. A gap anywhere in that chain is a recordkeeping problem, regardless of whether the underlying review was thorough.
Rule 17a-4 applies to the approval record, and to the final piece of content. If your marketing AI sits outside your archiving infrastructure, the record it participates in creating may not meet the retention standard.
Third-party and vendor AI: the due diligence gap
FINRA Regulatory Notice 24-09 applies to AI "built in-house, licensed from a third party, or embedded in an existing third-party product." That last category is where most firms have the least visibility. When the AI is inside a vendor product, the firm still owns the regulatory exposure through its third party compliance obligations.
Standard vendor due diligence questionnaires weren't written with AI architecture in mind. SOC 2 certification and data encryption matter, but they don't tell you whether the vendor's AI produces a 17a-4-compliant audit trail, how the model is versioned, or whether a subprocessor is handling your firm's data to train a shared model. Most procurement processes aren't asking these questions yet.
Questions worth adding to any vendor AI review:
| Due Diligence Question | Why It Matters |
|---|---|
| What records does the AI system create, and do they meet Rule 17a-4 retention requirements? | AI outputs are records. If the vendor's architecture doesn't produce 17a-4-compliant storage from the start, you can't fix that gap retroactively during an exam. |
| How are model updates versioned, and are customers notified before changes go live? | A model change can alter supervision outputs overnight. Your WSP needs to reflect what the system actually does, and someone needs to have reviewed the change before it went live. |
| Who are the subprocessors involved in AI inference, and what data reaches them? | A SOC 2 certified vendor may route inference through a third model provider with no contractual relationship with your firm, creating an undisclosed data exposure and a supervision gap. |
| Can the firm access a complete, timestamped log of every AI action taken on its data? | Examiners ask for audit trails, not vendor assurances. If you can't pull the log yourself, the record doesn't exist for exam purposes. |
| How does the vendor support explainability if a regulator asks why the model produced a specific output? | "The model scored it high" is not a supervisory rationale. The architecture must surface the reasoning behind every flag or decision. |
The subprocessor question deserves extra attention. A vendor may be SOC 2 certified while routing inference requests through a third model provider that has no contractual relationship with your firm. If that provider's model changes, your supervision outputs change, and your WSP may no longer describe what the system actually does.
Greenboard's approach to AI securities compliance architecture
Every architectural dimension covered above has a direct answer in how we built Greenboard.
The foundation is WORM-based, 17a-4 compliant archiving, SOC 2 Type 2 certified, covering the full range of communications channels where supervised activity happens. Email, Slack, Teams, iMessage, WhatsApp, LinkedIn, Telegram, and more feed into a single capture layer within our communications archive. Whatever the AI scores, the underlying record was retained correctly before the model ever touched it.
AI supervision across that archive replaces random sampling with a full-population review queue. Every captured communication is scored, and reviewers work from a ranked, flagged list. The attribution chain runs through the entire workflow: submission, AI scoring, reviewer decision, principal sign-off, all in one timestamped, tamper-evident log.
Marketing review follows the same structure. Our AI pre-checks ads, social posts, and decks against FINRA Rule 2210 and the SEC Marketing Rule before a human opens the document, returning specific reword suggestions and flagging missing disclosures. The pre-check and approval record live in the same system, producing one continuous, exam-ready log.
Personal trade pre-clearance auto-approves clear cases against firm policy, restricted lists, and existing positions, and routes ambiguous ones to a named principal. The record shows who submitted, what the AI checked, and who decided.
GreenboardGo ties it together. Every answer the AI returns is grounded in your firm's own policies, procedures, and books and records, with clickable source citations. "The model said so" is never the answer you give an examiner. The source is always there.
Final thoughts on making your compliance AI architecture exam-ready
AI changes what gets in front of your reviewers, but it does not change who owns the decision. Your architecture needs to prove that, record by record, at every step of the workflow. When it does, you walk into an exam with documentation that speaks for itself.
FAQ
How do AI securities compliance tools handle Rule 3110 supervision requirements?
Rule 3110 requires a named principal to sign off on supervisory reviews. "The system reviewed it" is not a valid supervisory procedure. A defensible AI supervision architecture scores the full population of captured communications, surfaces flagged items to a reviewer, and logs every step: submission, AI scoring, reviewer decision, and principal approval, all in a timestamped, attributed, tamper-evident record tied to a specific person.
Can I use AI to detect compliance risks in employee communications without creating Rule 17a-4 recordkeeping gaps?
Yes, but only if your capture and retention architecture was built for it before the AI touches the data. If the system ingesting and scoring communications isn't built on WORM-based, 17a-4 compliant storage from the start, the AI outputs it produces may not meet retention standards, and you can't retroactively fix that during an exam.
What does AI compliance defensibility actually require in a broker-dealer's written supervisory procedures?
Your WSPs need to describe what the AI does and does not do in the review process, how the model was tested before deployment, who reviews AI-generated flags and under what escalation path, and which named principal signs off on supervisory reviews. Generic language about "reviewing electronic communications" won't cover an AI-assisted triage queue. Examiners expect the procedure to match the actual workflow.
Greenboard vs. legacy compliance platforms for FINRA-registered firms: what's the structural difference?
Legacy tools were built to store information; Greenboard is built to act on it. The structural gap shows up in exams: legacy systems often produce disconnected audit logs across archiving, trade monitoring, and marketing review, while Greenboard runs all of those workflows through one unified, WORM-based architecture so the record of every AI action, reviewer decision, and principal approval is in one place when an examiner asks for it.
How do I manage employee compliance and marketing compliance on one platform for a broker-dealer?
The challenge with running these on separate systems is that each produces its own audit trail, and pulling a complete picture of an employee's compliance posture across pre-clearance, attestations, and communications supervision becomes slow and incomplete under exam conditions. A unified architecture ties personal trade pre-clearance, outside business activity disclosures, attestations, and marketing review approvals into one continuous, exam-ready record from the start.
