Building Ardence: Combating Modern Crime On-Chain and Off-Chain with AI

Ari Redbord (00:01):

I'm Ari Redbord, and this is TRM Talks. I'm Global Head of Policy at TRM Labs. TRM is an AI investigations and threat intelligence company combining on-chain and off-chain data to combat modern crime and build a safer world. We use our AI investigations platform, Ardence, along with Chainabuse and the Beacon Network to bring together the public and private sectors to defeat scam networks, ransomware gangs, terrorist financiers, and other threats. Prior to joining TRM, I spent 15 years in the US federal government, first as a prosecutor at the Department of Justice and then as a US Treasury Department official, where I worked to safeguard the financial system against terrorist financiers, weapons of mass destruction proliferators, drug kingpins, and other rogue actors. On TRM Talks, I sit down with business leaders, policymakers, investigators, and friends from across the blockchain, AI, and technology space who are working to build a safer world. On today's TRM Talks, I sit down with TRM's Simon Warren and Lexie Nappi to discuss building Ardence, TRM's AI investigations tool.

(01:03):

But first inside the lab where I share data-driven insights from our blockchain intelligence team. Today's TRM Talks is an Ardence takeover. Ardence is TRM's AI investigations platform. But AI is not new to TRM. TRM has a library of AI content. We have spent two years mapping how criminals use AI and how investigators answer. We have written on the rise of AI-enabled crime, AI-enabled fraud, how AI is changing the scale and speed of fraud, autonomous AI agents in financial crime, fake AI trading bots, and how deepfakes are used to commit crimes. Sextortion and deepfakes show the financial infrastructure behind digital exploitation. And we have written about how terrorist groups use AI. We have talked about the response, how law enforcement uses AI, TRM's support for Microsoft's Evil Tokens disruption, my House Judiciary testimony, the first hearing on AI crime, and the global conversation on AI governance.

(02:07):

TRM has an entire library on AI crime. Give it a read at our website at trmlabs.com. And now, TRM's Simon Warren and Lexie Nappi. Today, I have an episode that I am so incredibly excited about. Last week, TRM announced Ardence, our AI investigations platform that takes our blockchain data and combines it with the data of 60 partners so we can provide a picture of holistic risk. Think on and off chain on one graph, so investigators don't have to worry about blockchain here, off chain data here, but they now have a full picture of risk to investigate and disrupt. And the reason I am so excited is not only to talk about Ardence today, but to bring on two of the people that were absolutely instrumental at TRM, building Ardence and now really working with our partners, to enable really awesome outcomes. Simon Warren and Lexie Nappi, thank you so much for joining TRM Talks. Before we dig deep on Ardence, would love to hear a little bit about your journeys. What you did before TRM and really what you do at TRM today. Lexie, why don't I start with you?

Lexie Nappi (03:23):

Thanks again, Ari, for having us. It's a real honor and pleasure to be here today. Prior to my time at TRM, I was a former army officer that had an opportunity to trace crypto on behalf of the United States Cyber Command and, at that point, I met some TRMers and a dear friend of mine and colleague referred me to the company, and the rest is history.

Ari Redbord (03:42):

Amazing. Let me take you slightly deeper than that. Tell me about your time in the Army and sort of like really the types of things you were working on and maybe how that has impacted, I think, the work that you're able to do today.

Lexie Nappi (03:53):

My entire background helps me with what I do day-to-day on the evals team with Ardence. Mostly because Cyber Command sat so closely with the US intelligence community, we had to look at so many different data types, analyze them with rigor, provide operational options to leadership, weigh risk. That all of those basics and those fundamentals help me with where I am today with Ardence, which is an all-source investigations platform. Without it, you know, I probably wouldn't be able to do the job that I did today. But I, you know, I'm also grateful to be able to say that I, you know, supported Cyber Command, which was also like Ardence, was kind of at its genesis when I joined. And so here I am now again at the genesis of using AI in investigations as well. So I'm still kind of somehow riding this like wave tops of like these new things that keep you know, introducing themselves to the intelligence community. And so I'm grateful to be here today. And so, you know, and just knowing how to continuously evolve and adapt with technology changes as they come.

Ari Redbord (04:48):

Absolutely awesome. Simon, tell me a little bit about your journey and what you're doing now on the Ardence team.

Simon Warren (04:54):

So I run the go-to-market strategy for Ardence across EMEA and APAC. My background is actually somewhat similar to Lexie's in some ways. I started off my career in the military as well. I was in the British Army, then I transferred to the Australian Army, ended up in Australian Special Operations, which is weirdly where I ended up discovering crypto because I was doing research on darknet markets and the ability to buy explosives and weapons on darknet markets, and saw people using Bitcoin, and that was a long time ago. So that's sort of what got my interest in crypto originally. I then got out of the military and went into a cyber threat intelligence role, where I was actually delivering cyber threat intelligence, running investigations myself. And I continued in that role until last year when I was approached by TRM, and the topic of this new AI investigation platform was put to me.

(05:39):

And there were a couple of things that, like, pulled me away from my previous role, which was also a very mission-focused, very exciting company. The product itself is something that I would have loved to have had when I was an investigator, when I was doing cyber threat intelligence. So I was already very intrigued by like the sound of this product and also the mission that we're working on at TRM. To me, like I said, I came from very mission-focused organizations, the military, other mission-focused private sector organizations. But the mission we're working on here is just so big and so important, and is the potential to have such enormous impact, not just with the Ardence product we're talking about today, but more broadly as a business.

(06:16):

So that was what really sort of pulled me into TRM just over a year ago.

Ari Redbord (06:20):

It is extraordinary. I love so much about that answer, but I think it is also belies like, we have been working on this for a year. We sought you out in the world to come join this team to build this product, that we launched last week. So obviously just like speaks to the hard work and dedication that's been going into this for a year or more. What's so extraordinary to me is, you know, I joined TRM six years ago. We were eight people. And I've always said things like the ability to link on and off-chain insights is the holy grail of investigations, meaning it's something you're never going to find, right? It's this idea that we lose the trail when funds move off chain, when they go through OTC brokers and into bulk cash smuggling and Chinese money laundering networks.

(07:02):

And now all of a sudden, because of this transformative technology, we can run AI over all of these different data sets to actually be able to build one graph that shows what's happening on and off chain. It's amazing. It's amazing for me to see. I can't believe, you know, this, that what you and the team have built here. Lexie, for people hearing about Ardence for the first time, right, at launch, which is almost everyone, what do you want them to know? Like what should they understand about this tool from a high level?

Lexie Nappi (07:30):

Honestly, you really hit on it with what you just said truthfully, Ari, is that what makes Ardence very cool and was something that Simon and I wish we had when we were still back in, you know, in our, in government service particularly, is that Ardence is an all-source investigations platform. On and off chain, any type of data. And what's cool about it is it allows multiple disparate datasets to come together and interact soundly and safely. And that's part of my job here at TRM is making sure that data interaction is true. It's a sound and it's a safe data interaction so that we make the most fair, honest assessments of our threat cases that we're looking at.

Ari Redbord (08:06):

That's awesome. Simon, same question to you. What should people understand about this from a high level?

Simon Warren (08:10):

What we're trying to do is level the playing field. So everyone knows about the threats from AI in association with crime. So we think about how people are scaling scams and fraud using AI, how ransomware groups are using AI to, you know, improve the speed and the scale of their attacks. So Ardence is just one way of leveling the playing field, giving the defenders the same sort of tools that the attackers have been using for years. And the other side of it is trying to solve some of these challenges that, like Lexie already mentioned, that we had and analysts and investigators still have today, which is switching between multiple different platforms, having dozens of different data feeds, trying to pull it all together into one place, analyzing it manually, producing reports manually.

(08:48):

It's incredibly time-consuming, and then you miss things as well. If you're looking at 10 different platforms and trying to find that needle in a haystack, really hard to do that as a human. Those two problem sets combine to create this massive problem that we have as defenders defending against all this crime and Ardence is our solution to that problem.

Ari Redbord (09:05):

Can you walk us through like, well, what does this look like? How does this work?

Lexie Nappi (09:08):

The thing that I think that's really cool about Ardence as well, and unique because it's something that I haven't seen quite yet, at least with my experience with other tools like in the wild at the moment, is Ardence's flexibility. The beautiful thing about Ardence is that you can actually start from multiple start points in your investigation. It doesn't matter what you have or what you don't have, the data meets you where you're at as an analyst. And what I mean by that is, for example, you can do a bottom-up approach. You can start with a seed or a selector. Like I might have a particular email address of interest, or a phone number of interest, or a name of interest, or a company of interest. Again, any data type at this point. And I can pull strings and see, you know, and derive the meaning and, like, and see what that, what the influence of that, selector of interest I have has in an operating environment.

(09:54):

And I can pull strings out, and I can extrapolate that meaning. I can go bottom up and present those results to leadership and render operational decisions and have mission impact. Or what I think is also really cool about the flexibility of Ardence is you can start top-down. And what I mean by that is, you know, maybe, for example, you're in law enforcement, and your leadership has given you the charter of, you know, go solve crypto. Well, I don't know. I don't have a seed. I don't have an address. I don't have a name. What you can do in Ardence is actually, for example, enter a prompt, you know, something along the lines of like, "Show me indications of pig butchering in the state of New York." Well, now what I've done is the data has now met me at where I am with my investigative intent, and I can start broad and scope down into tactical leads that I can then present and offer to leadership and have mission impacts, so I like how you can go both directions in Ardence. You can start with a seed and, you know, extrapolate forward and outwards, or you can start broad, you can start strategic, you can start, you know, as a leader more so in the space and narrow down and refine down into tactical leads.

Ari Redbord (10:56):

I love that. Simon, kind of jump in here. I would love to hear kind of your take on the workflow. I mean, what's amazing to me, right, is some of the ease. For someone who is a lawyer, not a technologist, right? I can jump into this tool the same way I would jump into Google for a Google search, the same way I would now jump into Claude, and you're prompting throughout, and essentially asking this tool to go deeper and deeper for you.

Simon Warren (11:15):

Yeah, I think that's a really good point, Ari. It really lowers the barrier to entry on all sorts of different investigations. The idea is that the tool helps you through the process, and the way that the team have built it is codified all those analyst investigator skillsets into the tool. You could honestly go in and say, "I'm doing an investigation into a certain subject. Help me do the investigation." And Ardence will write the prompt for you, and then hold your hand through the process and suggest the next step. So it really lowers that barrier to entry. So that means that, you know, new investigators can do more detailed investigations. It means that maybe if you're used to doing crypto investigations, you can go and do a more detailed ransomware investigation.

(11:54):

So that's one side of it, is the, that how it allows everyone to do a lot more investigations. But the other interesting thing to me is just the access to all the different data sources you can do an investigation, which would with a traditional method be basically impossible. We have 50 different data sources, more than 50 different data sources in Ardence at the moment. No one in the world is paying for 50 different subscriptions to 50 different data sources. And if they were, they're in 50 different portals all over the place. With Ardence, you ask that question at the beginning, and Ardence decides, "I need to call these 15 different data sources." You then start your investigation, then it might call another 15 different data sources, and it's just a really efficient and effective way to run an investigation.

(12:35):

And the interesting thing for me is thinking back to the days when I was running investigations, there are data sources that we have in Ardence that I would never have paid for them because I would might even use them in one investigation a week or a month. But being able to pivot into something like sanctions data, or I wanted to go and have a quick look on the dark web about this person's name or this username, and just being able to call that as and when you need it is such a fantastic way to run investigations.

Ari Redbord (13:00):

That's the watershed piece to me, right? Is that, wow, I have now access to information that I would never have had access for, and I can see it all in one place as I'm building this thing. I think to me that's probably the coolest piece of this. I think a very close second is the community that we've built around it. When I think about TRM and what we've built, I often think about the community, right? And so much of the technology even we have built is around building that community. When we think about deconfliction within our original platform, when I think about the Beacon Network, when I think about chain abuse. For me, TRM Talks is about building that community. Our TRM Run Club is about building that community out there, and I love that you guys led when you built Ardence with this idea of building community. So talk to me a little bit about the data partnerships, what that looks like, how you've thought about that throughout this building process.

Simon Warren (13:47):

Yeah, it's a really critical part of what makes Ardence what it is. It's the way we've built all the different data partnerships into the platform. And the data partnerships team have done a truly phenomenal job there. And it's not a simple process. It's not a simple thing that these guys have done. But it... I can break it down to sort of three main areas. We have a team of pathfinders who go out and speak to the market. So that's really important to us, is that we're building something that the market, our clients and potential clients are interested in. So we have a team going out, meeting clients, doing demos, having discussions, bringing that back to the partnerships team. We also have all our internal teams, so Lexie and her team. We have other, investigators and analysts internally as well, who are providing that feedback and saying, "It would be great if we had this data source in there and maybe this data source as well." And then all that is pulled back into the partnerships team, who are also doing their own proactive research and identifying, you know, a client has said, "We want dark web intelligence."

(14:41):

Well, what are the top dark web intelligence providers out there? Let's go and speak to Flashpoint. Okay, we're gonna bring Flashpoint in. That makes sense for our clients and what we're building here. But the key point is it's really driven by that client demand. So we're not just going out and just choosing our favorite partners that we know are our friends. We're building what the clients want. And that sort of links into a second part when we're talking about partnerships. It's not just about the data partnerships. It's about those design partners that have supported us over the past year. As we've been building this product, we've gone out to trusted clients we've worked with in our, you know, the last eight, 10 years as a business, and said, "Look, we've got this new product.

(15:20):

Will you have a look at it and give us some really honest feedback?" And we've got some very honest feedback over the past year, but that's helped us to build this product that people absolutely love. And it's so exciting to have these people that are, you know, big federal agencies and, you know, law enforcement, defense, whatever, who are willing to come and try this product out for us and give us really great feedback that's allowed us to build it into what it is today.

Ari Redbord (15:39):

As we've been building Ardence and now at launch, we're talking about, a lot about this idea of mission programs. Can you talk to me a little bit about what that means?

Simon Warren (15:49):

Yeah, absolutely. I think we're all very excited about the mission programs at TRM. It's a real evolution of where we've come from the BlockInt business into the Ardence AI investigations platform into this idea of mission programs. And mission programs, it sounds like quite a vague term, but these are much bigger, more outcome-focused programs in that they're made up of three key elements. The first is what we call services, basically people, so analysts or investigators that we will give to a client to achieve some kind of outcome and help them to solve one of their problems. The second part is Ardence. We've spoken about that a lot today, but it's the foundational platform that allows these mission programs to operate. And the third part is how we build on custom additions or custom apps built on that foundation Ardence, but customized for those particular outcomes that a client is working towards. So it might be a command center, it might be something to do with disruptions, disrupting scams, something like that.

(16:48):

So we'll customize the platform, add on some specialist apps to support that mission program. So it's a really... These are big projects. These aren't just, you know, a software agreement where you buy it and you log in. These are big transformational programs that are solving really massive problems about how, you know, how do we solve scams for a country? You know, how do we stop fraud coming out of another country? So these are huge, exciting, and impactful missions.

Ari Redbord (17:15):

What's so cool is that, you know, you get to focus on outcomes, right? How do we stop bad actors? How do we warn victims? How do we stop a cyber attack from happening? Which is potentially, look, I mean, this is like transforming investigations, but maybe at the end of the day it's more, it's transforming what software as a service really looks like. It's not a license, it's an outcome. And also just love the focus on wins, right? Like, hey, our whole job as a company is to enable wins for our clients, whether that means law enforcement, regulators, national security agencies, compliance teams. So I love this idea. Lexie, how are you thinking about mission outcomes, mission programs at TRM right now?

Lexie Nappi (17:53):

Yeah. It's a great question, and it's really an ode to the professionals that work at this company. You know, our colleagues, our friends that we know are very good at what they do. You know, how Ardence enables them and scales them first, we're listening and we're asking these professionals: How do you think about this problem? How do you frame it? What are you thinking about? Where are the scopes? We're providing that guidance to Ardence and continuing to drill that. So what's cool is we're now, again, we're bringing the expertise through the data and even into the agent itself so that we're fusing the professionals in TRM into the data and into the platform itself. The outcome comes with you as you use Ardence.

Ari Redbord (18:33):

Lexie, one question, you know, I've been talking about Ardence nonstop, since launch, and one question I've got is a really good one, and that is this idea of sort of, look, AI investigators can go down all kinds of different roads. We saw Anthropic provide a lead, on a homicide investigation wrongfully to the Philadelphia Police Department. You know, an AI investigator can be confidently wrong, and a wrong name or a wrong wallet address, there are real consequences. Talk me through how TRM, how you have thought about making sure that Ardence's findings hold up, and build that sort of guardrails around these investigations.

Lexie Nappi (19:12):

Yeah, I think that's a great question. I mean, Ari, I think that's the million-dollar question that we're constantly hearing from, you know, our CTO, and that's the technical question of the AI game is how do you know your sound? Really sound is the word. How do you know your assessments are sound? And that's the number one goal of the evals team, which is my team, and I'm really grateful to work on the team, is that our number one goal is to de-risk the platform from offering uninformed or unsound analytical statements. And the number one way we do this really is watching how the agent thinks. Not necessarily opposed to what the agent is thinking, like the outputs that it's giving, but how it thinks. And the reason we're watching how the agent thinks is because our team hypothesis is that stronger analytic form will lead to more precise and accurate outputs. That sounds super technical.

(20:04):

Anyone that's ever played a sport or practiced an instrument or practiced anything will understand what I just said in the sense that good practice begets good results. Good practice begets good results. Ari, pretend I am a Duke basketball player, and I gotta make free throws. I don't drill, you know, making free throws. What I drill is my form. You know, is my elbow in? Are my hips square? You know, stance, where are my eyes? You know, is the entire setup to the free throw consistent and stabilized so that no matter what circumstance, no matter what environment, no matter what ailment you might be having as a team at the moment, you can still trust that your form is good and you'll get good outputs.

(20:45):

I know Simon knows what I'm talking about, too, because he's drilled these things in Special Forces as well. It's just you drill your form. And so how, you know, how we make sure that our assessments are sound is we're watching how the agent thinks, because what we're doing is calibrating its form. And, you know, you might wonder, you know, ask the question: How are we grading our form? Or like what's our checklist to grade how good the agent form is or how sound the agent form is? And, you know, actually what we've done, one of our early design decisions was to anchor our standard to the US Intelligence Community directives. Exactly. And we anchored specifically to ICD 203, which is the analytic rigor standard that human US IC analysts are held to.

(21:23):

So, you know, we hold that standard. We hold Ardence to that standard, the goal being that good form, that repetition that we drill in our human analysts will also beget the same good results we get from our human analysts with Ardence.

Ari Redbord (21:36):

Extraordinary. I love that answer so much. First of all, you're the first person in the history of TRM Talks to use a Duke basketball analogy on me. Love that. That is amazing. That's usually my job here. I love it. Amazing answer. And I really love the idea of like, all right, let's go find a standard that is at the very highest level, and let's meet that standard through a really rigorous evaluation process. I think just extraordinary. Simon, kinda same concept, as you've thought through building and deploying Ardence, talk me through the sort of the human in the loop piece to how we thought about kind of building investigations.

Simon Warren (22:09):

So Ari, we start with human in the loop. It's an integral part of how we've designed the product, even down into the fine detail of what the experience looks like in the platform. You can determine every investigation how in the loop you want to be. So we have an option where you can just say, "Here's my guidance. Go off and I'll come back in five minutes. Just give me an answer." Or if you want to just sit there and watch every single step, you wanna watch Ardence pull a data source, bring it back, and then you say, "Okay, I'm happy with that. Next step, please." And you can sit there and watch it and say, "Okay, next step, please." So you can be completely in the loop, you can be somewhat in the loop, and we've designed the entire platform. It's one of our key design principles is that the human is always in the loop. And even when the investigation is finished, you can still go back into the results and see very clearly what has been surfaced and from where as well.

(22:55):

So before the final judgment or the deductions or the action is taken, that human is still in the loop to be able to see how reliable is this output before any further action is taken.

Ari Redbord (23:06):

Really awesome. I think there's so many takeaways from this conversation, but one for me is, look, I mean, Simon, you came on literally to build this. We've been working on it for a year. I love the fact that we're getting feedback from partners, but also it's been in the hands of law enforcement for months now, and we're able to sort of get that feedback as well. So like, you know, we do this big public launch from a marketing perspective, yes, but really we've been, using this, getting feedback for quite some time now. So to that end, what would you, what do you want to hear from an investigator a year from now as they've really dug in, really used Ardence?

Lexie Nappi (23:41):

I would want to hear, "Ardence is so sound that it taught me, the human, where my assumptions were, where my blind spots were, where my tradecraft errors were, and it helped me render sounder assessments that led to lasting operational impact." That's what I would want to hear. The soundness is it's keeping me honest as a human.

Ari Redbord (24:04):

Amazing. Simon

Simon Warren (24:05):

I want analysts to say, "I can't live without this." I mean, we've seen this in some of our early trials. They've tested it and said, "When can we get this? Because I don't wanna go back to how I was doing things before where I had all these different platforms I was accessing." So I want analysts to think, "This is how I work. This is where I work. I spend all day, every day just sat in Ardence running everything in here, and I can't live without it anymore."

Ari Redbord (24:25):

Absolutely awesome. This was an incredible conversation, I am so privileged to be part of this and to work with you both. And it's just incredible amount of work that you have both put in on this the last year. I know how hard it is. In the very few minutes that you weren't working on Ardence over the course of your life right now, what are you doing for fun? What are you doing outside of Ardence and TRM?

Lexie Nappi (24:49):

Man, I'm sure Simon will have something much cooler and impressive to say, and I wish I could say... Yeah, I wish I could say running. I do wish that. I'm working on that, Ari. But instead, the opposite, my husband and I are really into smoking meat these days. So we'll buy a brisket and, you know, try something different, and then I need to go for a run after we eat our weight in meat. Yeah.

Ari Redbord (25:12):

Love that. Any fun stories about that process? Or like how did you get into that?

Lexie Nappi (25:16):

Oh God. Well, you know, we live in Texas, so part of, I mean, part of the fun is how good can we make our barbecue taste like an actual real barbecue joint. You know, I'm

Ari Redbord (25:25):

Where are you in that? Where are you in that process?

Lexie Nappi (25:28):

You know, we made a brisket this last Sunday and it rivaled some of the Texas joints I've been to. But I'm from Kansas City, so I think the sweet and smoky barbecue's better. So I don't think it's as good as KC barbecue yet, but we're... You know, he did a good job. I'll give

Ari Redbord (25:42):

See, that's... All right, Simon, tell me what are you up to?

Simon Warren (25:44):

I have a lot of hobbies. Probably my main one is rugby, which is why my face looks like this. I also have recently started coaching one of my kids in rugby, which is an amazing amount of fun. We're a big rugby family. We're also all learning to skateboard, which is a pretty stupid thing to do for me in my 40s. But that's a lot of fun as well. And then we go snowboarding a lot. So, well, I snowboard and the kids ski. So we have a ton of hobbies that end up with us all getting

Ari Redbord (26:14):

The

Simon Warren (26:14):

Broken bones a lot. Yeah

Ari Redbord (26:15):

That's so interesting. Do you think that your snowboarding capabilities can help with sort of getting into skateboarding?

Simon Warren (26:21):

I think they did actually. I think, like, typically it helps a lot the other way around. So people who are good at skateboarding when they're a kid and go into snowboarding are very good at snowboarding. I think I could stand on a board more easily than others, but the worst thing... Falling on snow is pretty bad. Falling on concrete when you're, like, in your 40s, it's not good. Like, normally it's... If I fall, I'm just going home. I don't get up and get back on the horse. I just go home for the day. I'm done.

Ari Redbord (26:45):

It's amazing that you are skateboarding. That is, that's absolutely incredible. Go

Simon Warren (26:48):

Like, I felt like I had to because like look at me, I look like a skateboarder, and people assume that I can skateboard and I can't, and it's kind of embarrassing

Ari Redbord (26:55):

That is amazing. You guys are amazing for so many reasons, to include your hobbies and interests. Thank you so much for joining TRM Talks and really excited to have you guys back to have like, "Hey, what does the feedback look like a year from now?" Thank you, Simon. Thank you, Lexie. Yeah. A couple of huge takeaways for me. One I just really want to start with. When we started TRM six years ago, every episode had to be people who are experts in the world on the topics we were talking about, digital assets, blockchain. And soon thereafter, we started hiring people or bringing people on board at TRM, the Christian Cheskys, the Kyle Armstrongs, Sujit Raman, who were guests on TRM Talks. And there's nothing that makes me prouder in the world than to be able to have the foremost experts in the world on the intersection of AI technology investigations in Lexie and Simon at TRM. So that's my biggest takeaway. I'm going to give you a few that are probably more your takeaways, the first is the mission piece, and that is this tool enables us to transform the way we think about software and investigations. It enables outcomes. How many victims can we save from being the victim, losing their life savings, being the victim of a scam? How many cyber attacks can we potentially thwart?

(28:11):

So I love this idea that this platform enables outcomes an entirely new way. Then two, for years we've always said it is impossible, it's the Holy Grail to combine off-chain insights with our rich blockchain data. And now because of AI, because of this transformative technology, we can run it over all of these different datasets to provide really a true picture of holistic risk. And then that last piece that I loved so much was the fact that, yes, we've been building this for a year, and we've hired amazing people like Simon to come on and really build it. Lexie sort of has really jumped in to help us transform it, to perfect it. But we've been getting that feedback from our partners, from law enforcement who have already hands on the tool.

(28:55):

So really cool conversation and just so proud of what the team has built at TRM and excited to get it in the hands of everyone. On the next TRM Talks, I sit down with Cahill Next practice leader Lewis Cohen. If you love the show, leave a review wherever you're listening to it and follow us on LinkedIn to get the latest news on crypto regulation, compliance, and investigations.

TRM Labs (29:24):

TRM Talks is brought to you by TRM Labs, the leading provider of blockchain intelligence and anti-money laundering software. This episode was produced in partnership with Voltage Productions. The music for this show was provided by iKOLIKS.

Ari Redbord (29:40):

Now let's get back to building.