Name: Norm AI
Uploaded: 2024-12-19T06:06:28.355Z
Duration: 2 min 33 s
Description: An AI-powered platform that automates regulatory compliance by converting complex regulations into executable computer programs.

norm-ai

An AI-powered platform that automates regulatory compliance by converting complex regulations into executable computer programs.

Norm AI

Joining me today on NYSC floor Talk is John Nay. He is the founder and CEO at Norm AI. John, it's fantastic to have you here. Thanks for joining me today. 

Thank you for having me. So now, John, the last time you were at the New York Stock Exchange, you were hosting fireside chats with Larry Summers and the Attorney General of New Jersey. Um, fill me in on the insights with the AI agents and the law Summit that Norm and the NYC put on earlier this year in February. 

Yeah, it was a great time. And um, it was sort of our launch party for the company at Norm AI. And what we learned there is um, as AI is being deployed more broadly, there's a lot of concerns about once it's more agentic. That's the agents part of that. Um, it's doing autonomous things. Is it going to be doing things that are in compliance with the law? 

Because as humans, we're out there doing stuff all the time in our companies. Everything we do is subject to thousands of

Regulations and laws and as AI agents are doing similar things in companies in the future as they get more advanced they will be subject to all the same laws and regulations. So with the Attorney General of New Jersey with Larry Summers um with other legal experts like that we really dove into those issues and started to think through what are the existing frameworks we already have around copyright law privacy um many other regulatory frameworks that could apply here and how they might need to be adapted and evolved over time.

Now you and your company have pioneered the concept of regulatory AI. Tell me how does it work? Good question um it's definitely a new thing and the way that we think about it is that regulations are extremely complex. So for example SEC marketing rule it's a 430 page PDF uh don't quote me on the exact number of pages but something like that and that is something that every day every investment.

Adviser has to make sure that everything they put out into the world is compliant with that. And so what we do is we take regulations like that which are very complicated, very standards and principle based, and we turn them into AI agents. 

So when we say regulatory AI agent, we mean an AI agent that can pursue an action, in this case assessing whether something's compliant or not with a specific regulation. And so the way that we do that is we break down the regulation into all of its different requirements and different nuances. 

And then we build that into a decision tree. The decision tree is something that represents it legally but also enables from the technology perspective an AI to go very specific in each of its determinations. 

And then over time, ultimately say at the end of the decision, is this compliant or not with that regulation? And if not, why specifically is it not? And then what can I do to improve? So that's a

Pattern that we've built out more broadly not just for SEC not just for fenra but we're doing this over time. The reason we call it regulatory AI is for regulations more broadly. So this could be anything that a company is subject to. We're over time turning that into an AI version of the regulation and what type of team is required to build this. 

So as you can imagine from the nuance of that and the technical legal aspects to that, it does require lawyers on staff. So we have people with law degrees from Harvard and Stanford and Yale. Um but then of course it requires AI engineering and software engineers and even AI researchers. So that's part of my background. I have a PhD focused on AI research. Um but I was always applying the methods at the intersection of AI and law and so trying to figure out how to get machine learning systems to better understand law and legal concepts. And then over time, um you know.

Recruited a lot of other people to that cause. And what we have at the team now, we have AI engineers from Google and Meta and places like that. We have software engineers. Um, we have the lawyers. And then, um, we're now building out a new function called a legal engineer. 

So this is a new role that we invented. Um, and so the way that this works is you are someone that has the legal training or the regulatory training, and you have the domain expertise, but you don't necessarily want to be coding all day programming. And so you use our tools that we've developed internally to take a regulation, take a corporate policy, convert it into a regulatory AI agent by like clicking and dragging and dropping. 

So it's a no code tool. So a legal engineer is someone who can develop the product, develop new regulations, but not need to code. So what we built over time is a team that's very interdisciplinary, as you pointed out, across law, AI, software.

Engineering research and now legal Engineers as well. And then it also requires thinking about this from a bigger picture. So not just in the weeds but how would the current Regulators think about this and how would over time, uh, many in the industry think about this. 

So what we've done is we built a regulatory Advisory Board. We have a former SEC commissioner, former SEC enforcement lawyer, other former heads of different agencies. And so what that allows us to do is to say let's take a step back and let's look at the whole regulatory landscape. What are the areas of Regulation that are most admittal to this approach for now? All will be soon. 

Um, and then also how would the current Regulators think about the output of what we're doing and can we begin to collaborate more with them? And finally, John, tell me what's the 5 to 10e vision for Norm Ai and how does that tie in with AI advancements more broadly? 

Yeah, so this actually.

Circles back to the the the talk that we had here at the New York Stock Exchange in February um where we explored two sides of the coin. So one was um how do you use AI to encode regulations and and make it more efficient and effective to comply with those regulations. 

But the flip side of that is as AI gets more advanced and it's deployed more broadly as gbt 5 comes out from open AI after gbd4 and then new and new advancements come out from other companies as well. That is enabling the more autonomous deployments of AI. 

So AI generating suggestions to share AI Agent Store with colleagues from scratch. AI making decisions talking to clients on your behalf at a bank for example. 

So as that happens and it happens in higher and higher Stakes situations, it it's still subject to all the same laws and regulations. So part of our vision is we will be sitting across from that. 

So the AIS everyone's developing their own AIS or they're deploying some other one but they could.

Leverage us to make sure that the outputs and the proposed actions and proposed content coming out of the AI is is uh compliant with any of the relevant regulations so that we have a two-part vision. It's how can we U make companies and and people more compliant effortlessly with less work but also as AI more deployed. How do we make it more compliant as well? John, it's been great to talk with you. Thanks for joining me on floor talk today. Thank you so much for having me.

Note, that there may be slight differences in text and video. Watch video for the original content.

Norm AI Video