AI Compliance Software for RIAs: 6 Tests Before You Buy

AI compliance software pairs language models and document retrieval with rules engines and workflow automation to answer regulatory questions, review marketing, monitor activity, and document a firm's compliance program. For an RIA, the evaluation comes down to six tests: source citations, corpus currency, grounding in your firm's own policies, an examinable audit trail, RIA-specific workflows, and human-in-the-loop controls.

This guide is the buyer's version of that sentence. We build AI compliance software, so read the product sections with appropriate skepticism, but the evaluation tests below apply to every vendor in the category, including us.

What is AI compliance software?

The term covers two different product categories, and most of what ranks for it serves the other one.

AI for compliance is what an RIA is usually looking for: software that applies language models to the firm's compliance work. Ask "can we accept this gift under our Code of Ethics?" and get an answer grounded in your own policy. Submit a draft advertisement and get it screened against the Marketing Rule before a human reviewer signs off. Ask "which clients don't have a recorded delivery of the updated brochure?" and get the list with the delivery records behind the answer.

Compliance for your AI is the other category: governance tools that document and audit a company's own AI systems under frameworks like the EU AI Act and NIST's AI risk framework. Drata, Vanta, and IBM watsonx.governance live here. If that's what you need, this post isn't it, though the SEC's interest in how advisers use AI (more below) means an RIA adopting AI tools should document that use regardless.

This post evaluates the first category, for RIAs specifically.

Why are RIAs looking at AI for compliance now?

The economics are the honest answer. Commonly quoted market ranges put ongoing compliance consulting for an RIA at $8,000 to $15,000 per year and an outsourced CCO arrangement at $30,000 to $125,000, with wide variation by firm size and scope (our consultant guide breaks down what those engagements include), and solo principals commonly report spending a large share of their week on compliance work rather than clients. Most of that spend buys three things: knowing what the rules require, applying them to specific situations, and producing documentation. Language models are strong at the first and third, and useful, with supervision, on the second.

The regulator has noticed the shift from both directions. The Division of Examinations listed firms' use of artificial intelligence and other automated technologies among its fiscal 2026 examination priorities. And the SEC has already policed exaggerated AI claims: in March 2024 it charged Delphia and Global Predictions with making false and misleading statements about their use of AI, with $400,000 in combined penalties. The message cuts both ways. Advisers can use AI, but both the adviser's claims about AI and the vendor's claims to the adviser deserve scrutiny.

The six tests for evaluating AI compliance software

1. Does every answer cite its sources?

A compliance answer you can't verify is a liability with good grammar. Language models produce fluent, confident text whether or not the underlying claim is true, and the failure mode that matters in compliance is the plausible wrong answer. The test: ask the tool a question and check whether the answer shows the specific rule text, policy section, or firm record it relied on, as a link you can open, not a vague "based on SEC regulations." Then ask it something it cannot know. The right behavior is "I don't have a record of that," not an improvisation.

2. Is the regulatory corpus current, and who maintains it?

The quiet failure mode in this category is staleness. Regulations move: the SEC adopted major Reg S-P amendments in 2024, staff FAQs clarified important Marketing Rule positions in 2025 and 2026, and in June 2025 the Commission withdrew fourteen outstanding rule proposals in one notice. A tool trained on a 2024 snapshot will confidently walk you through complying with proposals that no longer exist. Ask the vendor directly: what is in the corpus, how often does it update, and how do withdrawn or vacated rules get removed? A vendor without a specific answer is telling you the answer.

3. Does it answer from your firm's policies, or just the regulations?

The regulation is the floor. Most firms operate above it: a Code of Ethics with a lower preclearance threshold than the rule requires, a gift limit tighter than the industry norm, a marketing approval process with steps no regulation mandates. Your supervised persons are held to the firm's manual, even where it is stricter than the regulatory floor.

That makes this test the sleeper. A tool that answers only from the rulebook will tell an advisor a $300 gift is fine when your policy caps gifts at $100, and it will be right about the regulation while creating a policy violation. Every one of those answers turns into remediation work later: the exception memo, the coaching conversation, the log entry, all CCO hours spent cleaning up after a technically correct answer. Grounded in the firm's own policies, the same tool becomes the opposite: the place advisors get the firm's answer instead of a generic one, which means fewer violations reaching the CCO's desk at all.

The test in practice: load your compliance manual and Code of Ethics into the trial, then ask a question where your policy is stricter than the rule. The right answer cites your manual. An answer that cites only the regulation tells you the tool will be working against your program, one confident answer at a time.

4. Is there an audit trail an examiner can review?

Rule 204-2 requires specified records of the advisory business, and the 2026 exam priorities put advisers' AI use, representations, and controls on the table. So when AI supports a regulated workflow or a compliance decision, the firm should be able to reconstruct how the result was produced and reviewed. If a marketing piece was AI-screened and human-approved, the record should show what the AI flagged, what version it reviewed, and who approved it. The rule does not impose a universal prompt-log requirement, but "show me how your firm uses this tool" is a question to expect, and the tool itself should be able to answer it: an exportable, time-stamped record of queries, the sources retrieved, outputs, and the human decisions that followed. If the vendor can't export that, you'll be reconstructing it by hand.

5. Is it built for RIA workflows, or is it generic?

A general-purpose compliance chatbot knows what GDPR is. It does not know that your advertisement needs net-of-fee performance with equal prominence, that an access person's trade needs preclearance against your restricted list, or that your ADV annual amendment is due 90 days after fiscal year-end. Category fit shows up in the workflows: Marketing Rule review queues, Code of Ethics and personal-trading monitoring, Form ADV tracking, vendor due diligence, exam-binder assembly. If the vendor's other customers are hospitals and crypto exchanges, the roadmap will never prioritize an RIA's problems.

6. Where is the human in the loop?

Rule 206(4)-7 puts the compliance program, and a designated CCO, squarely on the firm. That responsibility does not delegate to a model. The architecture question is whether the tool respects this: AI should read, draft, flag, and propose; a human should approve anything that takes effect, a review decision, a client communication, a policy change. Be wary of any pitch built on removing the human. The value is compressing the hours around the decision, not making the decision.

What should make you walk away?

Red flags, in rough order of how fast you should leave:

• AI-washing. Vague "AI-powered" claims with no explanation of what the AI actually does. The SEC fined advisers for this exact behavior; apply the same standard to vendors.
• No citations. If answers arrive without checkable sources, every answer becomes your research project.
• Unrestricted data use. The vendor can't clearly explain whether prompts, documents, or client information are retained, used for model training, or exposed to subprocessors. Client data in an RIA's systems sits under Reg S-P safeguarding and service-provider oversight; get contractual limits on use, retention, and training, and diligence the security behind them.
• No AI usage log. If the vendor can't show you the interaction record, neither can you show an examiner.
• "Replaces your CCO." A vendor that misunderstands Rule 206(4)-7 this badly will misunderstand other rules too.

What should you ask in a demo?

Seven questions that separate real capability from a rehearsed script:

1. Ask a question specific to your firm's documents, then click through every citation.
2. Ask a question where your firm's policy is stricter than the regulation, and check whether the answer cites your manual or just the rule.
3. Ask something the system can't know, and watch whether it admits it.
4. Ask what the staff clarified in the March 2025 Marketing Rule FAQs. (Currency test with a known answer.)
5. Ask to see the exported audit log for the demo session you're in.
6. Ask where your firm's data lives, who can see it, and whether any of it trains models.
7. Ask which workflows are RIA-specific, and which are the same screens every industry gets.

A good vendor enjoys these questions. That reaction is itself a signal.

How does RegFin approach this?

RegFin is an AI compliance platform built specifically for RIAs, and it is opinionated on exactly the tests above. Answers are grounded in retrieval against both the regulatory corpus and your firm's own policies and records: the assistant cannot answer a question about your firm without first pulling the documents it will cite, so when your manual is stricter than the rule, the answer reflects your manual. "I don't have a record of that" is a valid answer. The regulatory corpus is re-checked nightly and tracks amendments, withdrawals, and vacaturs, so a rule the SEC pulled last month, or a court struck down, is marked that way when it shows up in an answer. Every interaction, the query, the citations retrieved, any proposed action, and the human approval, is preserved in a version-stamped trail, so your AI usage is itself examinable. Write actions always require a human. And the workflows are the RIA's: marketing review against the Marketing Rule, employee trade monitoring and preclearance, Form ADV tracking, and vendor due diligence.

For the thinking behind that design, our companion piece on how AI is changing RIA compliance covers the technology side, and the RIA compliance guide covers the program the software supports. And if the real question is whether to hire a human instead of, or alongside, the software, our RIA compliance consultant guide breaks down what consultants cost and when each fits.

The bottom line

AI compliance software is worth evaluating on the merits, not the hype, and the merits are checkable in an hour: citations you can open, a corpus that knows what the SEC did last month, answers that know your manual is stricter than the rule, an audit trail you could hand an examiner, workflows that match how an RIA actually operates, and a human wherever judgment lives. Any vendor, including us, should be able to pass those tests live.

See RegFin against all six tests with your own questions. Book a demo and bring the hardest compliance question your firm has faced this quarter.