Using AI Responsibly: A Compliance Guide for Financial Professionals

The temptation is obvious. An AI tool can draft client emails, analyze portfolios faster, summarize research, and spot patterns humans might miss. But the question every advisor needs to ask isn't "Can I use AI for this?" but rather "Should I, and how do I document it?"

The regulatory landscape hasn't fully caught up to AI adoption. The SEC, FINRA, and state securities regulators are still defining boundaries. But that doesn't mean there's a compliance vacuum—it means the responsibility falls on you to implement thoughtful controls today, before regulators enforce stricter rules tomorrow.

Here's what you need to know about using AI responsibly without paralyzing your practice.

SEC and FINRA AI Compliance Requirements: What Regulators Care About Right Now

Regulators aren't trying to ban AI. They're concerned with three things:

1. Accuracy and competence: If an AI tool recommends something incorrect, you're still liable. The tool didn't make the recommendation—you did. Using AI doesn't transfer your fiduciary duty; it layers another layer of potential failure.

2. Conflicts of interest: If an AI tool is trained on biased data or incentivized toward certain products, you need to know it and disclose it. This is particularly important with vendor-provided AI that might benefit the vendor.

3. Client data security: Feeding client information into cloud-based LLMs creates data residency and privacy issues. Where does that data go? How long is it retained? Can it train the AI?

The common thread: documentation and transparency. Regulators are more forgiving of controlled AI use with clear records than they are of black-box automation.

Three Core Compliance Principles

How to Maintain Human Oversight and Final Authority with AI Tools

This is non-negotiable. AI should enhance your decision-making, not replace it.

What this means in practice: Never let an AI recommendation go directly to a client without review. This includes research summaries, portfolio analysis, and personalized advice. You review, you verify, you approve—or you don't send it.

Set up a simple rule: Every AI-generated output needs human sign-off before client contact. This can happen quickly—often in minutes—but it must happen. Document these reviews in your CRM or compliance system.

Why regulators care: If a client disputes a recommendation, you need to show that a qualified advisor—not a machine—made the final call. Your review demonstrates competence and diligence.

Understanding AI Limitations and Documenting Risk Assessments

Every AI tool has boundaries. It can hallucinate. It can miss context. It can misunderstand complex financial rules. Your job is knowing where those boundaries are and building processes around them.

What this means in practice: Before deploying any AI tool, conduct a risk assessment. Ask yourself:

• What types of tasks is this tool good at? (Summarizing research, organizing data, drafting client communications)

• What is it bad at? (Making complex fiduciary decisions, understanding unique client circumstances, interpreting regulatory nuance)

• What's the cost if it gets it wrong? (Low-cost tasks like email drafting carry less risk than portfolio recommendations)

• What client data am I feeding into it, and what's the privacy implication?

Document this assessment and share relevant limitations with your team. If you use an AI for client communications, consider noting in your CRM that it was used (not necessarily disclosing it to clients, but having it documented for compliance purposes).

Why regulators care: This demonstrates competence and risk awareness. You're not blindly trusting a tool; you're making an informed decision about its role.

AI Disclosure Requirements: When and How to Tell Clients

Disclosure rules around AI use are still evolving, but transparency is always safer than opacity. The question isn't always "Do I have to disclose AI use?" but rather "What would a reasonable client want to know?"

When you should disclose AI use:

• If AI-driven analysis is material to your recommendation (e.g., "Our AI-powered portfolio analysis suggests...")

• If a client could reasonably expect human-only analysis (e.g., a customized financial plan should note if portions were AI-generated or AI-assisted)

• If AI is used to assess their individual circumstances or eligibility (e.g., suitability analysis, risk tolerance scoring)

• If there's a potential conflict (e.g., you're using a vendor's AI that also benefits that vendor)

When you likely don't need to disclose:

• Using AI to summarize published research

• Using AI for internal efficiency (drafting emails that you heavily edit, organizing documents)

• Using AI for general educational content you share with clients

What to say: Be straightforward. Something like: "We use artificial intelligence tools to enhance our research and portfolio analysis, but all recommendations are reviewed and approved by our advisory team before being presented to you. We maintain strict controls over how your information is used."

This signals competence and control, not negligence.

Practical Implementation: The Compliance Workflow

Here's a concrete workflow that balances efficiency with risk management:

Step 1: Categorize Your AI Use

List every AI tool or task you're considering:

• Low-risk: Drafting client emails, summarizing news, organizing research

• Medium-risk: Portfolio screening, performance analysis, client segmentation

• High-risk: Generating personalized advice, suitability analysis, risk assessment

Step 2: Set Controls by Category

• Low-risk: Document that it was used; review before sending to clients

• Medium-risk: Require advisor verification; maintain records in CRM

• High-risk: Require advisor review plus a second set of eyes; disclose if material to recommendation

Step 3: Audit Your Data Flows

Map where client data goes. If you're using ChatGPT or Claude for client work, you're uploading client information to a third party. This might be fine for anonymized analysis, but it's not fine for sensitive personal financial data.

Consider:

• Are you using data residency-compliant tools (many enterprise LLM options have this)?

• Are you stripping identifying information before using LLMs?

• Do your vendors have appropriate data security agreements?

Step 4: Document Everything

Your compliance system should show:

• What AI tool was used

• For what purpose

• What reviewer approved it

• Any client disclosure

This creates a record that demonstrates diligence.

The Vendor Risk Angle

Be careful with AI solutions built specifically for financial advisors. Vendors have incentives. They may:

• Use your data to train their AI (which helps competitors)

• Guide you toward certain recommendations (even subtly)

• Have business model incentives you're not aware of

Due diligence questions:

• How is my client data used? Is it used to train the AI?

• Can I get my data out if I leave?

• What conflicts of interest does the vendor have?

• Can they provide SOC 2 compliance documentation?

• What's their data retention policy?

A vendor that can't clearly answer these questions isn't ready for client-facing use.

Regulatory Trends to Watch

The SEC has signaled it will crack down on inadequate AI oversight. FINRA is developing guidance. State regulators are asking more questions. The trajectory is clear: more regulation, not less.

Stay ahead by:

• Following SEC Staff Observations on AI (they publish public guidance)

• Joining industry groups discussing standards (your custodian or industry association likely has resources)

• Building documentation now so you're not scrambling if new rules arrive

• Treating AI as a tool requiring governance, not magic

The Bottom Line

AI is genuinely useful. It can make you more efficient, help you scale, and improve client outcomes. But it only stays useful if you treat it with the seriousness it deserves.

The advisors who win in the next three years won't be those who adopted AI fastest. They'll be those who adopted it most thoughtfully—with clear controls, good documentation, and transparent communication with clients.

Responsible AI use isn't a compliance burden. It's how you protect your practice, your clients, and yourself.