Red Oak's Podcast
Red Oak's Podcast
The Problem With “AI-First” Thinking in Compliance
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Welcome to the Deep Dive. Today we are wrestling with a topic that is well, it's causing some genuine tension across pretty much every regulated industry.
Speaker 2It really is.
Speaker 1The question is: how exactly do you take the lightning speed of artificial intelligence and successfully integrate it into workflows that absolutely positively cannot fail? We're talking about compliance. Yeah. We took a deep dive into a really powerful stack of sources, articles, research papers, expert notes, all detailing this critical friction between the enthusiasm for AI-first innovation and the non-negotiable need for regulatory governance. So our mission today is to give you a genuine shortcut to understanding the necessary shift in mindset. You really need to stop thinking about AI as the goal and start seeing it as a tool that has to be, well, compliance grade.
Speaker 2That is the essential reframing right now. I mean, AI, machine learning, basic automation. These things have been operating in regulated systems for years. They aren't brand new concepts. Right. But the scale and the mandate are totally different. Today, there's just this enormous pressure to embed complex, probabilistic AI models everywhere.
Speaker 1Everywhere.
Speaker 2And it's often outcasing the governance needed to manage them safely. That gap, that's where the exposure is. It creates enormous unnecessary regulatory exposure.
Speaker 1So what we're going to do is unpack this concept that kept coming up in the source material: compliance grade AI.
Speaker 2Exactly. It's a defensive, deliberate framework. It's about ensuring every AI application you use in a regulated process is built to prove its work, not just, you know, predict an outcome.
Speaker 1Okay, let's dig into that. Because this really is the core of the problem, and it's almost it's almost philosophical, isn't it?
Speaker 2It really is.
Speaker 1Compliance is fundamentally built on determinism. You need a process that when you run it today, tomorrow, next year, it delivers the same auditable outcome. It has to be predictable.
Speaker 2Exactly. And AI, particularly modern machine learning, it operates on the total opposite principle.
Speaker 1Prediction and probability.
Speaker 2Exactly. It gives you the most likely answer, not necessarily the required answer.
Speaker 1I get the promise of prediction. I mean, it's faster, it scales, it can spot patterns. No human ever could. But when you're dealing with something critical, like finalizing a suspicious activity report or mandatory quarterly disclosure, speed just doesn't trump certainty.
Speaker 2It absolutely doesn't. And the source material spells this out in black and white. In compliance, you need precision, repeatability, and above all, unimpeachable auditability. Right. If you run your internal review process today and the AI gives you an answer, but then you run it again tomorrow with the exact same inputs and get a significantly different result.
Speaker 1Or no results.
Speaker 2Or a contradictory result. Yeah. That is not innovation. That is a documented, career-limiting regulatory exposure. You just you cannot defend that process.
Speaker 1And this leads right to what the experts are calling this backwood approach. It's driven by that excitement you mentioned. Firms adopt these AI-native solutions because they're fast and they're cool. They start with the model, the shiny new tech, and then they try desperately to bolt compliance frameworks and audit trails onto the side as an afterthought.
Speaker 2It's like designing a sports car and then realizing you forgot the airbags and the seatbelts after you've already hit 150 miles an hour.
Speaker 1Oof. Yeah.
Speaker 2In highly regulated environments, finance, healthcare, energy, that approach is just fundamentally indefensible.
Speaker 1So tell us about the reframing. If the goal isn't to be AI first, what is the core defensible principle that they outlined?
Speaker 2The experts are uh emphatic on this. The approach must be compliance first.
Speaker 1Compliance first.
Speaker 2You start with the compliance outcome you're legally required to achieve. You start with the regulation, the policy, your existing workflow. Then you look at AI as a potential tool to increase efficiency or accuracy within that predefined defensible structure.
Speaker 1So the technology serves the obligation.
Speaker 2The technology serves the obligation, not the other way around. It's a simple reframe, but it changes everything.
Speaker 1That makes perfect sense. But I do want to push back on this a little bit. If we're being purely defensive, isn't there a risk that we slow down innovation so much that we lose that competitive edge?
Speaker 2That's the essential balance. And the sources spend a lot of time on this.
Speaker 1Because AI does have a meaningful, powerful role to play in compliance. So where does the material draw those clear boundaries? Where is approximation okay or even useful?
Speaker 2So we have to accept that AI has a massive role to play in the preliminary stages, you know, the heavy lifting. Think of AI as the world's greatest research assistant.
Speaker 1So we're talking about massive data reduction. Exactly. If I'm a review officer and I have 500 pages of documents I need to vet for disclosures, the AI can cut that down to the seven paragraphs that truly matter.
Speaker 2That's a perfect use case.
Speaker 1The approximation is useful there because it's helping me triage.
Speaker 2Precisely. Early stage document analysis, identifying potential disclosures buried deep in complex files, or flagging patterns and anomalies in surveillance data. In those cases, approximation, the probabilistic output of the model is fine.
Speaker 1Why?
Speaker 2Because the output is just a signal, it's a suggestion. It goes straight to a human review team who makes the final deterministic decision.
Speaker 1So the key isn't the technology, it's the placement of the technology in the workflow. The moment a human is still in the loop and responsible for signing off, AI is just accelerating the process. It's not increasing the risk of the final record.
Speaker 2That's it. But, and this is a big but, this is where we have to draw the regulatory red lines. We need to detail the critical points where approximation is completely unacceptable.
Speaker 1Or determinism is mandatory.
Speaker 2Mandatory. These are the areas you just cannot afford to delegate to a probabilistic model. Think about the moment of truth in a compliance workflow.
Speaker 1Okay.
Speaker 2We're talking final approval decisions like signing off on a new product or a trade. You need certainty. Yeah. Regulatory record keeping, books and records obligations, the SEC or FINRA. They're not interested in your AI's most likely estimate of a transaction date.
Speaker 1They want the fact.
Speaker 2They require the exact auditable fact. Critically, the end-to-end audit trails have to be deterministic. The firm's obligation is to know, without a shadow of a doubt, what happened and why.
Speaker 1This highlights why a technical failure immediately becomes a massive regulatory liability. You mentioned model drift and hallucinations earlier. Let's define those and tie them to the compliance risk because they sound like engineering problems.
Speaker 2But they're compliance disasters waiting to happen.
Speaker 1Exactly.
Speaker 2Okay, let's start with hallucinations. That's when the AI essentially makes something up. It generates confident but totally false information.
Speaker 1Right.
Speaker 2If you ask an AI to summarize a policy and confidently cites a paragraph that doesn't exist in your firm's official documents, and an officer acts on that hallucinated info, the firm is now exposed. It's an accuracy failure leading directly to a policy violation.
Speaker 1And model drift, that sounds even scarier because it's often silent, right? It's when the AI's output starts to change subtly over time as it sees new data.
Speaker 2Correct. The model begins to behave differently because its underlying probability weights have shifted. In a compliance context, that means your risk assessment criteria are quietly changing without any human oversight or formal change management.
Speaker 1So a client that was low risk six months ago might be high risk today.
Speaker 2Purely because the model drifted and no one explicitly signed off on that change in risk methodology.
Speaker 1So these issues, inconsistent outputs, drift, hallucinations, they are not just technical nuisances for the engineering team to solve later.
Speaker 2No. They are significant, documented, regulatory risks that demonstrate a lack of governance and expose the firm to fines because you can no longer prove you're adhering to your own policies.
Speaker 1So the problem isn't using AI.
Speaker 2The problem is using it in the wrong places without the right controls, and that drives us to the necessary mandate, designing compliance grade AI. The core concept here is that this technology has to be designed specifically to perform compliance, to adhere to rules, not to learn compliance over time through some opaque, unauditable training process. Governance has to be embedded from the very first line of code.
Speaker 1This is the action plan. So instead of just listing them, let's discuss the four practical requirements the experts outlined for creating this compliance grade AI. What's the first non-negotiable pillar?
Speaker 2The first pillar is all about the record. Every single AI interaction, no matter how quick or preliminary, must be captured, stored, and tied directly to the compliance record of that transaction or review.
Speaker 1So even if the AI surfaces a pattern and the compliance officer immediately dismisses it, that initial suggestion and the reason for the dismissal have to be permanently logged.
Speaker 2Absolutely. You need the complete history. It's like a detailed transcript of the system's thought process. Otherwise, your audit trail is incomplete. If the AI looked at a document and said, I rate this disclosure risk at 72%, that number, the context, and the human action all become part of the mandatory record.
Speaker 1And that leads directly to the second pillar, which is that every output has to meet the standard of auditability.
Speaker 2Correct. Every output, every suggestion or flag must be reproducible, auditable, and ultimately defensible under regulatory review later on.
Speaker 1So if a regulator asks why did this transaction get flagged, you can't just say the model decided.
Speaker 2You can't. You have to be able to show the specific data points the model used and how it arrived at that specific output. And you have to be able to do it again, maybe years later. That's transparency married to rigor.
Speaker 1Okay. The third requirement focuses on the human element, which I think is where a lot of firms might try to cut corners for efficiency's sake.
Speaker 2Yes. The third pillar demands that workflows using AI must include mandatory governance and controls. So especially human validation were required.
Speaker 1So no, set it and forget it.
Speaker 2Absolutely not. Automation is great, but there are certain final decision points, those deterministic red lines we talked about, that require a human signature. You need review gates, error reporting, mandatory steps built directly into the workflow itself.
Speaker 1And the fourth pillar. This feels like the most important foundational rule, tying back to our initial distinction. It dictates the relationship between the AI and the policy itself.
Speaker 2This is the principle of alignment. Every deployment must align with the firm's existing policies and regulatory obligations. The AI must serve the policy. You do not, under any circumstances, rewrite your firm's regulatory obligations just to accommodate a new AI model that isn't quite meeting the standard.
Speaker 1So if the AI isn't working, the policy remains.
Speaker 2The AI is either fixed or it's removed.
Speaker 1So what does this all mean for the standard of proof? When the regulator comes knocking and asks about a decision that involved AI, what exactly does a firm need to be prepared to show them?
Speaker 2You need to show detailed, step-by-step accountability. Firms must be able to show exactly what input was asked of the AI, what specific output was returned, and how that output was weighed and used by the compliance team in the final signed decision.
Speaker 1You can't just point to the final approval.
Speaker 2You can't just point to the final approval document and say the system signed off. If you cannot reconstruct that entire journey, the question, the AI's answer, the human intervention, the final result, you have incomplete governance.
Speaker 1And in the eyes of a regulator, incomplete governance means increased risk. This really drives home the idea that governance isn't some bureaucracy you layer on top. It's the required safety net. The source material had a great analogy for this from Rick Grashel, Red Oak's CTO.
Speaker 2Yeah, that was a good one.
Speaker 1He reminded everyone you wouldn't fly a modern airplane without redundant systems and a black box to record every action. And you wouldn't jump out of that plane with only one parachute and no backup. We just accept redundancy and high-stakes fields.
Speaker 2And yet, many AI tools entering compliance workflows today are single point of failure systems. They are the single parachute. Right. They lack those equivalent safeguards. Backup controls, human validation points, and resilient systems designed to handle model failure. We are rushing to deploy the cool technology without first building the compliance black box around it.
Speaker 1That perspective is a fantastic anchor because it forces you to ask the necessary failure questions before you deploy anything.
Speaker 2Right. It's not about hoping the model is 99% accurate. It's about planning for the 1% failure.
Speaker 1So we have to ask what happens when the model fails? When it just stops running? What does the system do when it produces conflicting outputs? And most importantly, what are the mandatory fallback mechanisms when regulatory policies change, which you know they constantly do?
Speaker 2The core design principle of a compliance grade platform has to be the assumption that failure is possible. It's built in. It has to be. Therefore, the system must be designed to handle that failure gracefully, pushing the workflow back to a proven, deterministic human process if necessary. That means configurable workflows and clear fallback mechanisms.
Speaker 1And without those.
Speaker 2If you deploy probabilistic AI without building these robust governance structures into your operations, the AI doesn't reduce risk. It quietly compounds it. It creates this complex, opaque operational dependency that you simply cannot defend when things go sideways.
Speaker 1So if we zoom out to the macro picture, what is the biggest risk facing compliance teams today? Is it the failure to adopt AI and falling behind?
Speaker 2Surprisingly, the experts argue that's not the biggest risk. The biggest risk is adopting AI too quickly, under intense pressure from above, without fully understanding its precise effect on your existing, non-negotiable regulatory obligations.
Speaker 1So the technology has to reduce risk. Which brings us full circle back to the mandate. The outcome has to remain compliance grade. Our obligations don't change just because we have a faster tool.
Speaker 2Exactly. AI is just another powerful tool in the box. And like all powerful tools, it has to be used deliberately and governed rigorously. Determinism has to win out over probability in every critical area that forms part of that final auditable record.
Speaker 1We've really distilled this huge concept down to a single distinction for you today. If AI is on your compliance roadmap, and chances are it probably is. The most important question isn't whether to use it, it's how.
Speaker 2The final provocative thought you should walk away with and explore is this. When your firm's reputation and your career depends on proving that the process works.