Not All AI Is Created Equal For Compliance

Everyone is rushing to add AI to their products. For broker-dealers and RIAs, that can be dangerous. When your audience includes SEC and FINRA examiners, the real question is not whether you have AI, but whether you are using the right kind of AI inside your compliance program.

At GiGCXOs, we think about AI in terms of what it actually does for supervision, surveillance, and recordkeeping. Most practical AI that matters for compliance falls into three big categories: classification AI, generative AI, and agentic AI. Each is useful in different ways, and each has limits that matter a lot in a regulated environment.

Classification AI is the workhorse of surveillance. It is not trying to be creative. It looks at data and decides how to label it. For example, it can decide whether a communication is risky or routine, whether a message requires principal review or can be archived, whether a piece of text contains promissory language, or whether a transaction appears consistent with a client profile. Under the hood, it is trained on lots of labeled examples and learns patterns that match specific categories, such as high risk or routine.

This kind of AI aligns directly with daily obligations in a compliance department, including communications surveillance, marketing and advertising review, trade and account monitoring, and code of ethics or personal trading review. Because it produces clear labels and risk scores, you can measure it and tune it. You can track false positives and false negatives, adjust thresholds to match your firm’s risk appetite, and show examiners that your monitoring is reasonably designed and periodically tested. In other words, classification AI is often the engine that decides what needs human attention and what can safely be archived.

Generative AI is what most people think of when they hear the term AI today. These models create new content such as text, code, or images. In compliance work, generative AI is best seen as a drafting co-pilot rather than an autonomous decision maker. It can help draft sections of policies and procedures, turn long regulatory updates into plain language summaries, prepare first drafts of exam response letters, and suggest edits or clarification language for marketing and disclosure documents.

The limitation is that generative AI can be confidently wrong. That is acceptable when a human is always going to review and edit the output. It is not acceptable to allow a generative model to decide whether a communication is misleading or whether a disclosure meets regulatory standards. Those are classification and judgment tasks, not drafting tasks. At GiGCXOs, we see generative AI as a helpful assistant for writing and summarizing while humans keep full control over the actual compliance decisions.

Agentic AI is an emerging category that focuses on taking actions and orchestrating workflows. Instead of just labeling or drafting content, an agentic system can pull data from multiple systems, run a series of checks, create review cases, assign tasks to specific reviewers, and send reminders for overdue items or required attestations.

In a compliance setting, this kind of AI can be extremely useful for keeping a compliance calendar on track, ensuring surveillance alerts do not go stale, automating follow-ups with representatives, and coordinating complex multi-step reviews. The key is to design these systems so the AI orchestrates and routes work around humans while humans retain authority to clear exceptions, close alerts, and make final decisions on risk.

For broker-dealer and RIA compliance, the best mix usually looks like this. Classification AI sits at the core and drives surveillance and risk scoring. Agentic AI coordinates workflows and keeps tasks from slipping through the cracks. Generative AI assists with drafting, summarizing, and documentation, but never serves as the final word on what is compliant. Used together, these tools can shorten review times, reduce alert fatigue, improve consistency in risk identification and escalation, and strengthen documentation for regulators.

At GiGCXOs, we use this framework to evaluate any AI-powered tool that claims to support compliance. Our consistent conclusion is that classification-first AI is the most reliable and defensible foundation for SEC and FINRA-compliant technology. In the companion article, we explain how this plays out in practice by looking at how Hadrius uses classification AI for communications, marketing, and employee oversight, and why that model fits broker-dealer and RIA programs so well.