The Reason 30 Years of Cybersecurity Has Failed - and What Actually Fixes It | Trent Telford, Qanapi

Every major data breach in the last 30 years shares the same root cause: the data inside the wall was never protected, only the wall. And AI frontier models are now making that wall easier to breach than ever, scanning codebases externally to discover undisclosed vulnerabilities and write exploits before anyone knows the hole exists. Trent Telford, Chairman, CEO & Founder of Qanapi, joins Craig Smith to explain why the entire architecture of conventional cybersecurity is structurally broken, and what a genuinely different approach, built from the opposite assumption, looks like. Rather than trying to build a better wall, Qanapi starts from the baseline that the data will eventually be exposed, and encrypts it at the individual word, paragraph, or database cell level, tying each unique key to a verified identity and a set of conditional policies that must all be met simultaneously before anything can be decrypted. The most commercially urgent application of this architecture is one that unlocks AI adoption for enterprises that have been sitting on the sidelines: Qanapi's gateway service encrypts sensitive fields before data reaches Claude, ChatGPT, or any other frontier model, and the model simply reports it cannot read the encrypted sections, while still reasoning over everything else. Trent discusses how Qanapi's Fathom tool confirmed in testing that Claude could not read the encrypted sections. He describes two major retailers - one using AI heavily, one abstaining entirely because of data security concerns - and asks the question every enterprise leader should be sitting with: how long can you last off the train before you get blitzed? The episode also covers drone security in denied wireless environments, the post-quantum encryption mandate that federal agencies have no practical plan to execute, and why Qanapi's business has exploded in the last six months as the AI gold rush has finally turned its attention from models to infrastructure. Subscribe to Eye on A. I. for weekly conversations with the people building and deploying the future of AI.

• 0:00Speaker 00

  As you point out, once you're inside the wall, you can see everything. People started building or buying security systems that were effectively a wall around firewall with doors that would go into it. And they kept the doors locked unless you had the proper permission.

• 0:16Speaker 01

  It doesn't matter how high you build the wall, the bad guys go get a bigger ladder. Well, they find out how to pull a few bricks out of the wall in the bottom of the castle wall and crawl through. And you don't notice that they've crawled through, right? They pull a rock back over and you don't know they're in there. So that's been a really big issue.

• 0:29Speaker 00

  We'll get to AI, but particularly with these Mythos classes that can scan a code base or a piece of software externally and find undiscovered holes that then they can write exploits for. What's the solution that you guys have come up with?

• 0:48Speaker 01

  We start from a baseline assumption that the data will be exposed.

• 0:54Speaker 00

  Let's start by having you introduce yourself to listeners and explain how you got to Quantapi. And for listeners, it's Quantapi as in quantum API. Is that right?

• 1:09Speaker 01

  That's right.

• 1:09Speaker 00

  So you can explain that, but but give us your background. I know you had a startup before this.

• 1:16Speaker 01

  Yeah, thanks, Craig. Thanks for thanks for having me on and it's great to be here. Probably twenty odd years now, twenty five years in in in the tech game, particularly in software and internet related. So I come from that background of of twenty five years originally in the late nineties, working for the big, big investment banks, but on big transformation around e-commerce and technology projects, you know, building internal intranets, as we call them, as we remember in those days for trading systems and things around a banker's trust was where I started. And then when they fell over, I ended up with Deutsche Bank.

• 1:57Speaker 01

  And then in the two thousands, I was working in e-commerce consulting. So it was a great place to learn all about, you know, the full stack of what happens across as you want to design the internet-based systems, which we all take for granted today. But of course, twenty five years ago, that wasn't the case. And then I almost fell into the data security side. Probably twenty years ago now, nearly twenty years ago, nineteen years ago, I was doing some implementations of big identity and access control systems across banks and government. And then came into the the very specific area around data security. And that that happened back then. So I've sort of been looking at this problem around,

• 2:41Speaker 01

  you know, data security and all these issues for so many years now that what now people are looking at and thinking, wow, that's a great idea. That's that's logical, right? That's logical. That's simple. It's something that simplicity has taken us a long time to to get there over various iterations and various startups over time. So. So, yeah, and I'm now, despite the Australian accent, here in the US and I live in Washington DC.

• 3:06Speaker 00

  Okay, let me give a little recap as I understand the history of of cybersecurity or protecting enterprise systems. You know, initially, it well, for a lot of people, it was just making sure your databases, all the ports were closed and the proper permissions were in place. I know that S three buckets, which is a popular data storage thing. You know, there's, if I had a hacker that was showing me, you can scan the world for S three buckets that are misconfigured

• 3:49Speaker 00

  and pop right in and see all their data. You know, it was amazing to me. And then people started building or buying, you know, security systems that were effectively a wall around the, you know, a firewall with doors that would go into it and and they kept the doors locked unless you had the proper permission. But as you point out, once you're inside the the wall, you can see everything. So then I've seen, I I mentioned to you a few years ago, I was talking to a company that was developing data layer security,

• 4:30Speaker 00

  but they were kind of a guardian that sat at the door to any particular data store and would verify anything or anyone going in or out. But they didn't protect the data itself that was going in and out. It again was a permissioning thing. So you guys are taking a completely different strategy or talk. Can you tell us what that is? And and and ultimately how it works and why?

• 5:13Speaker 00

  Quan, I mean, what is the quantum in Qanapi?

• 5:18Speaker 01

  Yeah, look, I think if we take a step back, I mean, now you're hearing a lot about zero trust at the data level, right? We've seen zero trust as a a general what started as a trend that then became best practice in applications. So if we take a step back, like you say, I think what people need to remember is the internet was never designed to be secure. It's actually designed to share information from when, go back to the DARPA days of invention and then the initial, you know, putting up a webpage for for information, you know? And then people for, you know, just for a bit of a quick history lesson. I mean, SSL, you know, the encryption of the connection from your browser to a server that we all take for granted now with the little padlock.

• 6:02Speaker 01

  Mark Andreessen, the famous, obviously, American venture capitalist, was one of the first and correct, I'm sure I'm quite correct in saying he was the one that invented that back in the back in the early days, right? And and, you know, we still use that today. It's still a network-based security. So if you think about it, it was never designed to be secure. So therefore, what we've spent, let's call it twenty five years, thirty years doing now is kind of retrofitting, constantly chasing our tail to get ahead of the bad guys to try and put more walls up, put more moats up, put more protection, better locks on the doors, all that sort of analogy. The problem is, you know, it doesn't matter how high you build the wall,

• 6:46Speaker 01

  the bad guys go get a bigger ladder. Well, they find out how to pull a few bricks out of the wall in the bottom of the the castle wall and crawl through. And you don't notice that they've crawled through, right? They pull a rock back over and you don't know they're in there. So so that's been a really big issue. And as you say, something needs to change because we we, if you think about where we came from with traditionally PCs and on-prem and big mainframes, we then moved to internet. And then in, you know, oh, seven, along comes Steve Jobs and, you know, he has a smartphone. And now, and we'll talk about this a little bit more, but all that AI side of things and all that autonomous and drones and landscapes, everything else out there. I mean, anybody, whether you're a tech person or not, you can get the picture.

• 7:31Speaker 01

  Like you can visualize all those drones in the air, all this AI, everything. And they these are borderless. They are literally borderless. There is no boundary. So the old, you know, put the bigger lock on the door, that that model's in real trouble.

• 7:43Speaker 00

  Yeah. And also, with regard to being borderless, you've got multinationals. I mean, there's no longer a locality that you build the wall around. It's dispersed. The we will get to AI, but particularly with these Mythos classes, what some people are calling them, these new models, Fable and Obus Five and Mythos Five, that can scan a code base or or a piece of software externally and find undiscovered

• 8:28Speaker 00

  holes that then they can write exploits for. So so that castle wall and the guy pulling the brick out of the base and crawling through, that's becoming a real problem. Because not all the bricks are secure or they're discovering that. So so tell me, what what's the solution that you guys have come up with?

• 8:53Speaker 01

  So we looked at it differently and we start from a baseline assumption that the data will be exposed. You know, I mean, I think there's nobody who works in cybersecurity who, well, certainly nobody who's credible in cybersecurity that that wouldn't say that it's not a matter of if, it's when you get hacked or there's a data leak, either intentional or unintentional. And by the way, more often than not, it's unintentional, right? But if you look at the scenarios, we assume that there's going to be an Edward Snowden event, right? We assume that, you know, remember Edward Snowden was a very trusted person. He had all the credentials. And he just decided this this needed to be out in the wild.

• 9:28Speaker 01

  So whether you come in uh uh and and attack through a vulnerability found in the wall or through through trusted sources, we just assume that the data is gonna have to um be we we will be exposed at some stage. So the first thing we did, we looked at encryption and and most encryption is not done um at the data level. Yes, you can encrypt a file, but mostly people don't do it because it's useless once it's encrypted, right? And it tends to be if people do it at the data level, which is rare, it's it's it's a wrapper. So typically, you you know, all content and and data sits in databases um open, because otherwise the databases can't read it, they can't do their query job. Or unstructured data sits on, you know, big servers like S three, if if you like, you

• 10:12Speaker 01

  know, AWS is as you rightly put out, storage. So we looked at it differently and thought, well, what if I can encrypt individual parts of a file? So if you've got a Word document or you've got um large flat files or um in unstructured data, we can get down to individual words, paragraphs. In databases, we can do individual cells and we apply a unique key, unique key for every word or paragraph. So we're right down at that level. So we assume straight away that um if you've got sensitive data, you can you can almost like portion it into all those encrypted, you know, fields or data sets. But the big problem with encryption, first of all, people hadn't thought of it at that level.

• 10:51Speaker 00

  Yeah.

• 10:51Speaker 01

  The second issue is that people hadn't thought about key management tied to identity and policy. So I try and explain that and say, well, when you walk out your front door in the morning, Craig, do you get ten keys and just scatter them all over the footpath? Or hide them under the mat, right? Under the mat or pop and hope that, you know, that the bad person doesn't find those keys? No, you you give one to your cleaner or your, you know, wife or your your best mate and say, look, come past the house. Um. And when I put all of that, people say, oh, that's logical. Well, that's what we did. So we said, let's tie identity to that key. So identity can be a human, obviously, and you present your credentials as you do today, two factor or whatever it is, or machine, a drone. We

• 11:36Speaker 01

  know what a drone is. It has its hardware credentials on there or a machine or a server. They all have hardware credentials. So if we tie the key to that to that identity, then we know that only the proper person or machine can have access to that key for that data set. And then we realized the third thing was, well, we could set policies around it. So it might be you prove your Craig, you prove that you um the the the system says you you are allowed to have that key for that data set, but we're gonna add some conditions. Could be time, only between ten AM and midday. In the case of drones and autonomous landscapes, it could be temporal, you know, um, for example, GPS plus altitude plus drone identity equals

• 12:17Speaker 01

  yes. If all those things come together for an encrypted payload on the drone, encrypted payload fifty six, you can get key number fifty six simplistically. So tying those three things together is unique and nobody else has done it. Um. And uh that's that's what allows us to build all sorts of solutions and deliver all sorts of interesting things that nobody else has done.

• 12:38Speaker 00

  Yeah, uh on key management, I mean, these are not keys that people are storing in their Apple Wallet or something. These are these are keys in the system. How how does the person, if I'm, I have a a key tied to my identity and I'm working on a system, is it? How how do I?

• 13:03Speaker 01

  How does that work?

• 13:03Speaker 00

  Give the key to the system, or does it have the key stored for me and my identity's validated?

• 13:11Speaker 01

  Right. So, yeah, that's that's a very good question. So um so that's the coming back to the name, quantum API. So it's an API service. So so our customers are, you know, large enterprises, um, you know, US federal type markets, defense markets, where they integrate it into either existing platforms. So they integrate it with the software that runs the drones, for example, or they integrate it with the software that uh manages all the files, for example. So when you set it up, you might have, you know, you wanna protect certain data sets, you can start by adding, you know, you might point our service to Ping or Okta, who are your identity credential um multi-factor providers for your organization.

• 13:53Speaker 01

  So now, when that data is encrypted across your organization at, you know, individual levels, um, you don't actually, it's kind of transparent is the word to you. You just know, for example, in Google Docs, I'll give you an example, in Google Docs, we can do multi-level classification in Google Docs. So we already know who you are when you're logged into Google Docs in your enterprise, right? We know it's Craig. Then we might know that you have um, you know, um, top secret clearance, right? But your pal Billy only has sensitive clearance. So when you're in our docs, you you see individually in encrypted um different paragraphs, it will give you, it will automatically, when you go over it, decrypt that part that you can read, but Billy goes in, he can't see that

• 14:36Speaker 01

  part. He can see the the next layer down, but he can't see the other one. So it's integrated as part of the processes as an API service, uh and that's the secret sauce. It's not the individual running around trying to manage the keys. It's a it's enterprise grade.

• 14:50Speaker 00

  Yeah. And uh when we were talking earlier, you it sounded like, so you have a document uh that's in a data store of some sort. And when you call up that document, it it automatically encrypts down to the cell level or personal identifiable information or or different strings in the document. That happens on the fly? Or is it encrypted in the data store?

• 15:26Speaker 01

  And um it it can be either or. So um so it can happen on the fly as part of a gateway service at at massive volume. Or uh so it depends, you can do it on the fly through a gateway service, or you might be looking to do an uplift because you're a federal government agency and there's mandated uh timing for post quantum now between now and twenty thirty. So you might go in and say, I've got, you know, a million files sitting in there where there's sensitive information in these these files. Uh because it's an API, we can write a bit of code, an SDK code, keep it simple. It might be Java or Python or whatever. And it will go through that and you can run it almost like a batch, if you like. And you can go back and over time, depending on how long you've gotten the compute

• 16:11Speaker 01

  power, it'll go through those million files and automatically, you know, encrypt all those individual cells or fields or words.

• 16:17Speaker 00

  In the.

• 16:18Speaker 01

  In the data store. So in S three, for example, and then obviously that data at rest is then protected. So if you come in through the um ways we talked about, so either if the the insider tries to take it, well, good luck. What are you gonna do? You're gonna walk out with a million files with, you know, millions of little encrypted words? Bad guy gets in, same problem, uh same scenario, sorry. Um. And equally, it covers that's data at rest, but it also covers the scenario of data in in transit, because now it doesn't matter. You can send that file over open networks, right? Untrusted networks. Because assuming you've protected the sections you wanna protect, the clear text you've assumed is public anyway, it's the everything else is encrypted. So now data in transit is covered

• 17:00Speaker 01

  and data in use is covered because, yes, it was stored, it was transited, but then it gets used. So let's say the Google, uh the Google Doc example, because you've got a hundred people looking at it with different clearances or, you know, in an enterprise, that might be board level or C suite or whatever, you know, research versus sales in in in investment banking. In use means literally while it's being used, you can only see the right bit. So it covers all three status, uh all three um states of data too, which is highly unusual.

• 17:29Speaker 00

  Yeah. And and that uh decryption, it happens. Where does that happen? So I'm at a workstation, I call up a document out of the data store. As you said, the Google Doc, when I see it, my credentials allow me to see certain things. Someone else would not see certain things. Does that happen on my computer?

• 17:58Speaker 01

  Yes.

• 17:58Speaker 00

  Yeah.

• 17:59Speaker 01

  It happens in the browser. So if we continue on the thread of Google Docs, it actually brings up sort of a pane on the right-hand side of the screen. So actually, what you see is encrypted text when you sort of click or roll over it. On the right-hand side in a little window, you'll see that same um paragraph, for example, in plain text format. And the reason we do it like that is that means if you, because if you decrypt it, A, it turns up in the document history, which you can clear, but it's in the history. Secondly, it means it's touched Google's servers. Imagine that that's the Google Doc connected to Google that way and our um add-on service is now connected to our service. So it never, it never lives in clear form on that Google Doc, it happens client-side.

• 18:41Speaker 01

  So when you encrypt, it's it's send, it's creating brand-new keys, it's sending them down to the browser. It's a hundred keys for a hundred different paragraphs, for example, it's automatically encrypting all those client-side, so now it's fully protected. But there's no footprint, that's an important point, there's nothing to install, there's nothing to run, uh which means it runs across mobile devices, it runs across anything.

• 19:02Speaker 00

  Yeah.

• 19:02Speaker 01

  So, and that's the beauty of being an API service.

• 19:05Speaker 00

  Yeah, yeah, and and I was asking just before this, what about false positives? I mean, wha- what's the accuracy in in reading the keys or in encrypting uh specific data?

• 19:21Speaker 01

  Data sets, um, you know, uh, look, uh, it depends on the data sets. The short answer is um, we don't make a ju- well, we, our tool doesn't make a judgment on the efficacy, if you like, of of that. Obviously, if you've got um structured data, it's much easier, if you've got a big database, you know what the different fields are. As long as you, if you're using A I, you're using our tool on the on the client side in A I to go and look at the training of that data to make sure it protects all the sensitive fields. If it's in, you know, structured format, it's much easier, if it's unstructured and there's millions of files. But every file's the same, so let's say it's an insurance form, forms are a good example in P D F, full of P I I S S N numbers, all that sort of stuff.

• 20:06Speaker 01

  That's pretty easy, right, because you can train it, make sure if every document's the same, now I'm a, now I'm a, I wanna push all that into a big frontier public L L M, I'm gonna have high efficacy. If you've got a million files that are all slightly different, obviously the chances of of missing something gets bigger. Uh but that's really just an exercise in in in training in the in the A I example.

• 20:26Speaker 00

  Yeah, uh, in that training, so when an enterprise uh calls the A P I, your model is already trained on on every configuration of of sensitive data that that could exist, right?

• 20:45Speaker 01

  So we have a uh, we have a small footprint, um effectively a A I agent that can sit out um uh on on the client side. So let's call it on-prem or in the client's environment, um and we don't have very specific ones um in terms of um knowing what sensitive is not. You train it, so you go through and, you know, you put the document in and you train it to make sure that it captures everything, you look at the before and after, you make sure it's all happy. And then effectively you save that scenario and then it it can then automatically go through. And any data that's being pushed up um towards L L M's is automatically encrypted. So, you know, you and I talked about this previously at another time, but um that's a big issue right now with all the public L L M's, right?

• 21:29Speaker 01

  The big argument is we're gonna have to build this stuff, you know, on-prem, and we're gonna have to go and buy our old infrastructure like the old days and suddenly have racks all through our offices of servers. Um, and of course, the biggest topic in the stock market right now is have we over-invested in A I data centers and A I capacity, well, this is one of the big topics, right? If you can, if you can um find a way to have enterprises move vast amounts of data up to public L L M's, then, and we can talk about the other usages of that and what other drivers will be, but that that certainly helps a lot, right? So um, nobody wants to go and buy their own hardware.

• 22:08Speaker 00

  Yeah.

• 22:09Speaker 01

  Or have to go and rent their own, because even if you go build your own or rent your own private cloud infrastructure, you know, I can go to Google Cloud and, you know, rent that A I um infrastructure, um, you can run your private models there, but that's gonna be a compromise, right, because your context models are much smaller, your context windows are smaller, your grounding data, all sorts of things mean you've got a a very different output to what you do in the big public L L M's. And so that's one of the big issues uh we we think we've solved is allowing companies to, we call that negative prov- negative provenance, so you can encrypt all the individual bits of data. It actually comes from the from the organizations on-prem up through our gateway, so we check all that. And we don't let it go into your A I service, whether that's Anthropic or all the main ones.

• 22:54Speaker 01

  Um unless all that is ticked off, we're a gateway service. But the second thing we do is um the keys to encrypt the originating data. Um, we know that identity, right, we know it's your data lake or this human's machine. So we call that positive provenance, that means it's all hashed, it's encrypted with an identity when it was created. So we know when it goes to the gateway, that it came from your trusted source. That's a massive issue in A I right now, what's a trusted source and what's not, what's poison data, what's not poison data? So solving the positive pros- provenance, it is my data and making sure it can't be tampered with. And then secondly, solving the the negative provenance, making sure sensitive or classified data didn't get into big commercially sensitive, um, you know, data.

• 23:39Speaker 01

  Public L L M's is a fundamental issue, but because we've got that foundational identity, key and policy, we know when we send the keys down, the request came from a trusted identity, data lake or trusted S three. We sent the keys down, with all the policies meet and away we go.

• 23:56Speaker 00

  Yeah, um, uh, we were talking, or I mentioned to you, there was a story the other day about uh chat logs from public L L M showing up in search results.

• 24:08Speaker 01

  Yeah.

• 24:10Speaker 00

  Which is pretty shocking, uh, but in this case, if that happened, uh, and I I think early on, right at the beginning of Chat G P T three, Samsung, there was an incident where some of their private data was uploaded to an L L M and then exposed. Uh, this would, if if anything was exposed, the sensitive cells or strings in that data would be encrypted, so no one would.

• 24:45Speaker 01

  So it's it's it's useless, yeah, I mean.

• 24:47Speaker 00

  Yeah.

• 24:47Speaker 01

  And that's the thing, you've got to make a business decision what's sensitive and and what's not sensitive. We will encrypt it, but you know, you can literally see it goes through the gateway, it goes into, you know, we've got a demo example for people to play with, it's Claude, for example, and Claude literally says, I found this, this, this and this. I couldn't read the data in this section because it appears to be encrypted with a an encryption key. And then it goes on to give you the results from the clear data, so it literally can't read it. So there is nothing to be concerned about then, that literally is, that problem um is not there any longer, it's that simple.

• 25:22Speaker 00

  And in terms of the model's performance of the reasoning, uh, whatever you're doing with it, it it can still do that, it's got all of the language around the encrypted strings or encrypted cells.

• 25:37Speaker 01

  Right, yeah, and I mean, of course, that's always gonna be a trade-off if you, look, if if what if what you're wanting the A I to achieve for you, uh in terms of insights, um, you know, if the data you're encrypted has nothing to do with those in-fa- insights, then obviously you'll still get the same essentially efficacy of outcome. If you have to protect certain fields, so for example, if you were looking at, if you're a large insurance company or bank and you wanted to use public L L M's, but you're not now, or retailers with loyalty programs. Yeah, the social security number of of Billy or Mary is gonna have absolutely no bearing, obviously, on the outcome of what you're doing. So no problem encrypt it, um, if you were to protect lots of other information that may have a bearing, were you gonna have an adjustment in that weighting?

• 26:23Speaker 01

  But um, you know, I think we've got a a customer that's a very, very large retailer, um, and they have a competitor that's a very, very large retailer, and I know one of them's using it very strongly, A I, and the other one is simply not using it because of these issues, um, and I think, you know, the question for the big enterprises is how long can they last abstaining or staying off the train, right, before they get blitzed? What's the what's the trade-off between competitive advantage or, you know, death quite quickly by not moving with those insights? And I think there's a lot of companies struggling, rightly so, with that issue right now, right?

• 27:00Speaker 00

  Yeah, the the, this is an A P I service and this is for enterprising enterprises that are using foundation models uh through an A P I, right? It's not desktop.

• 27:17Speaker 01

  Right, it's not, yeah, it's not for me sitting, you know, or or someone sitting just banging it into Chat G P T. Um, we are looking at that, but uh that's where it goes, you're typing straight into your content, you're straight into your chat window. You're going straight into Claude, for example, on your desktop. We built a gateway service, this is big enterprises or organizations and the data passes through us, you give us, when you sign up, you you put your A P I key for, say you use four different A I services, and that data goes in and you get a visual representation of that chain, if you like, of everything being providence chain encrypted.

• 27:53Speaker 00

  Yeah, it it could.

• 27:54Speaker 01

  You sit in front of it.

• 27:55Speaker 00

  It could work. By even on a a desktop or working with one of the model uh apps uh you could have the system check the prompt before it hits the model.

• 28:14Speaker 01

  Yeah, they would, we we haven't looked at um just yet some of those, but but yes, is in theory, there is, there is no problem with doing that, and that would be helpful as more of a ma- massive consumer market, if you like, so that everybody is using it. You know, it um so I'll give you an example in Google, in Google Docs, you can set it up as enterprise. So that if I start typing certain things in there, it it it will say you can't share that because they're known sensitive words like in our world, for example, with the the Department of War with the Pentagon and you go down across um CMMC, Cyber Matur- Maturity Model Certification, which is the whole supply chain cyber scenario for for Defence Industrial Base. Um, you know, we have it set so that you can't, if you put certain um terms in like

• 28:59Speaker 01

  um unclassified but sensitive, it'll pick it up. The same principle you can apply to the chat window, so it protects you from doing something silly yourself, like putting your social security number in or some of your medical data in or whatever it might be.

• 29:11Speaker 00

  Yeah, uh it is, how is the market? I mean, this must, this is a new world with AI has just expanded. Uh, the attack service, everyone's paying attention to how this is going to play out, how do you secure things? Uh, is is this, how are you guys growing now? Is is, I mean, obviously being on podcast, but uh is this a solution that uh people are banging down your door for, or is it is it one of many solutions out there and enterprises are trying

• 29:57Speaker 00

  to decide which solution is best for them?

• 30:01Speaker 01

  Yeah, so to answer the first part of the question, um it's really probably the last six months it's exploded. I think if we look at we, because we all sort of almost forget how quickly um how nascent um um mainstream use of AI is, you know, let's call it two and a half years or thereabouts to three years. Um, and everybody sort of went racing after all AI company, AI services and which one's best and which model's best and all this sort of stuff. Now people are realizing, oh, hold on, you know, this is a big problem. So so, yeah, the door is being banged down and whether it's across defence departments who are trying to use AI but be very sensitive in how they use it. Right? We saw what happened with Anthropic and um

• 30:45Speaker 01

  the uh the ethical, moral, philosophical, whatever you want to call it, argument. Um, enterprises are sitting here going, well, you know, we need to use it now for competitive advantage and I think the value curve is now coming towards infrastructure and it always has in technology, you know, it's the old picks and shovels analogy, isn't it, of the gold rush, the ones that made the money in the gold rush with the picks and shovels and the Levi jeans, not necessarily uh the miners. Um, so we've found that that's now the attention's turning towards who are, who are those in the infrastructure? And in our case, it's software that can, that can be agnostic to the big AI models, but can have customer bases of very large enterprises. So for us,

• 31:26Speaker 01

  um, it's an interesting time, Craig, because it is such a new field, you know, when I was doing the early stages of this encryption, there were lots of other encryption players and we had to keep telling people, here's why we're different. Now, um, now there is nobody out there doing what we do, not in the same way. Most people are just trying to do key management well, they haven't thought about tying it all together with identity and policy. So it's a huge opportunity. Uh, we've had yeah, businesses uh is growing very quickly, um because people are now starting to realize this is a big problem. Um, and you know, we we charge for, for example, in the AI side, um just on similar to, just on tokens, just like you do with your AI. So because when the data is going through our gateway into the AI service, we can see that a certain data set,

• 32:11Speaker 01

  for example, um you you burn whatever, ten thousand tokens.

• 32:16Speaker 01

  So we can charge you a thousand tokens, two thousand tokens, depending on the nature and complexity. But but as a a a percentage, if you like, of that AI spend. Uh, and that's really exciting for us as a business, but for the customers, they like that model because it means that if they've got a a million dollar or a hundred thousand dollar, whatever it is, AI budget, they just know that um a certain percentage of that now goes towards the security and they can open up AI to their teams and you know, across their organization without having to worry certainly as much um about what's gonna happen.

• 32:48Speaker 00

  Yeah, so that's a usage, uh

• 32:51Speaker 01

  Right.

• 32:51Speaker 00

  You're paying pay as you go or?

• 32:54Speaker 01

  Yeah, it's, yeah, it's a consumption base.

• 32:56Speaker 00

  Consumption base.

• 32:56Speaker 01

  Yeah, yeah, that's right. Yeah, it's purely consumption based. Um, and big enterprises is a bit different. Sometimes they pay a well, often the big enterprises, sorry, will pay a platform fee like because we charge a fee to access the platform because if you sit and do nothing with it, you know. But typically, um, yeah, it's all, it's all consumption based.

• 33:14Speaker 00

  Yeah, when you say platform, I mean, in that it's an API, is?

• 33:18Speaker 01

  Right, right. Yeah, so we're, we're in our platform, you, there's the AI, AI and the AI Security Service, uh which, which we call Fathom, um like, like, you know, like the nautical term of looking down and uh at depth to see what's going on. Um, you also get the uh the E- Echo product, which is our key distribution, which takes a little key management and can break it into pieces and distribute keys across um disrupted edge environments like military and drones and all that. And the, and then the core service is the API service, so you can use it to go back and quantum uplift, for example, twenty year old systems. I mean, it's you know, I was sitting with a previous director in the White House a couple of years ago. And he said, Trent, I don't know how the whole federal government's going to attack this, you know, um

• 34:03Speaker 01

  you, you, big organizations still have old systems, right? There's all the, you know, you know this, right? There's still all the sexy new products out there, but a lot of them are still running, you know, ten year old, fifteen year old, big, big, big, might be IBM systems or other, other products, as well as all their new stuff. You, if you're mandated, which, which now there's a hu- is the case to to post quantum uplift all this data. I mean, I say to people, big CIOs, so you've got a new encryption library from NIST, what are you going to do with it? There's sort of a blank face. I said, how are you going to take that encryption library? And go back to those twenty year old systems or all those cloud systems or all those endpoints? How are you going to roll it all out? How are you going to do the uplift? This is, you know, it's Tweety Birds, so it's silence, right?

• 34:48Speaker 01

  Pe- people haven't thought about it. They're worried about the libraries. They're worried about the libraries. And you're looking at all the all the um investment that's going into quantum computers, um you know, that's terrific, but equally, how's this going to get rolled out? People aren't just going to throw everything out and start afresh across your IT environment. You have to be able to go back to history as well as all the future stuff.

• 35:08Speaker 00

  Yeah, uh, you've mentioned drones a couple of times. I'm interested in drones. I was in Ukraine a month or two ago, uh, talking to drone companies. Um, specifically autonomous drone companies and fully autonomous. Uh, yeah, to, so why, well, tell me about the drone issue, I'm interested.

• 35:32Speaker 01

  Yeah, the drone issue is persistent everywhere and I I don't know if you you saw them over there, but people will say in the evening, I haven't been uh in in in close to the edge of the theater in there in Ukraine, but if you look up particularly in the afternoons or evenings with sunlight, it looks like spider webs in the sky. Because they're literally running those filaments, you know, for

• 35:49Speaker 00

  That's right.

• 35:50Speaker 01

  to connect it. Now, they're doing that because you can't trust.

• 35:53Speaker 00

  Right.

• 35:53Speaker 01

  Moving the data to the drone is a problem, right? Because um if the let me take a step back. First issue is internet connectivity. So often those drones will have intermittent. Well, if you look at the way the Defence Department, the Pentagon calls it D deal, D D I O, denied disrupted intermittent um uh en- environment. So you might have Starlink at some stage, you might then have a um trusted satellite network at a different stage, like a government one. But you might also be having to rely back on RF on radio frequency. So you have to be able to move the payloads out. And the only way to move them out is if they're encrypted, because if they're not encrypted, well, then the bad guys can A, read it, B, put false data in there, which is even worse, like if it's targeting data.

• 36:37Speaker 01

  So it's a, and secondly, they haven't been able to distribute the keys because the keys go out. And if you grab the keys, you've got the same problem. So um, so what we did was thinking about it completely differently. And new patents and everything on it, but we basically take keys. So if you've got pre, you can preload the payloads now on the drones encrypted. So let's say there's four payloads.

• 36:59Speaker 01

  You then distribute the keys. We break the keys into pieces and we can distribute them on the ground. So they could be some in certain Humvees, certain in the backpacks of the operators or Ford Operating Base or whatever. And then we apply identity and policy to those shards or those fractions of keys. So a drone says, hey, here I am. Here's my identity. Here's my temporal conditions, GPS. Um, altitude, those things where it says, can I have a key please for payload two? And it just comes down and we've got it all separated out. But as long as a certain um number of those fragments come back together and meet all those conditions, then you'll get the decrypt. Now, key sharding's been around for twenty years. The problem was if the threshold was, you know, five parts out of ten to come together.

• 37:44Speaker 01

  Bad guys grab, your adversary grabs those five thresholds, you're done. And so that's how we solved it. We did it a completely new way because of the platform innovation around identity and policy. We can apply all these other aspects to identity and temporal conditions. We can run it over uh low latency networks. We can run it over multi networks. If you've got three networks, you can make sure there's enough shards available to any one of those, whether it's RF or whether it's over common, sorry, open star link networks. You've got availability for all those pieces and nobody even comes close to achieving that. Um, and that's how we've done it very differently. But again, it's not just about key management. It's tying the identity and the policy to it as a, an organic bound transaction.

• 38:26Speaker 00

  Yeah, and but that uh that applies to transmit, I mean, open air transmissions, not to the fiber optic, fiber optic.

• 38:37Speaker 01

  Correct. Now we can use anything. So, you know, the drone, sorry. So our customers in this case are really the defense departments. It's not.

• 38:43Speaker 00

  Yeah.

• 38:43Speaker 01

  Because that, that's the other, that's the other thing, Craig. I mean, I was down at um the special operations uh soft week in, in Tampa a couple of months ago. And the, the number of drone companies is expanding, as we all know, we, whether that's undersea, you know, boats, planes, tanks, all that sort of stuff. They need a control plane to control all the data because when you're sending the data out there, it's not just going to an Android platform. This one or that one, it's got to go, it's got to be agnostic, like any control plane, right? So, so that's what, that's why it's important, right? And we, and you've got to be agnostic to, to the internet connectivity or the carriage, if you like. We can also send new data to those drones now because it's an encrypted blob. So now we can send it over. I mean, it could go across.

• 39:26Speaker 01

  Quite frankly, it could be on the edge of Ukraine with the encrypted payload coming across a Russian telco network. Doesn't matter. It's an encrypted blob. It hits the drone. Um, you can't put false data in there because it's encrypted and, you know, only the right combination of all those things. I said at the moment I will allow it to be decrypted. So that's the big difference. It changes the whole landscape for um for autonomous. And for us, that's obviously got a much bigger play beyond defense into, as we come down the road with, you know, autonomous and robots and general intelligence in AI converging with big, fast chips at the end of the decade and, and robotics. Same principle is going to apply. I mean, we've all seen

• 40:05Speaker 01

  Elon Musk's uh robots, right? I mean, they're incredible humanoid robots. How are you going to protect all them? How are you going to protect the data to stop the robot turning into an awful, awful sci fi movie and, you know, doing all the bad things they can do? It, it, it's, it's a huge problem that hasn't been solved.

• 40:20Speaker 00

  Yeah. On the drones, um, how, how, how, how much penetration do you have into that market? I mean, that's such a new market. And was that a, a completely separate engineering project uh working with drones? Or is it essentially you're just porting what you do for a database over to?

• 40:44Speaker 01

  Yeah, it's, it's, it's the same API platform, the cast. We call it cast, K A R S T. So cast is a geographical term for when water flows, you know, through mountains, so goes anywhere. Um, so we built it on our platform. We did call it a new product, Echo, because it breaks the keys up. So our, our, our, again, sticking with geography, we thought of it like when you stand in a cave as a kid and you you know, get the echo coming back. It's only back at one piece when it comes back to you. Um, so we built it. Yes, it was something that we specifically built, if you like, for that use case of drones. Uh, but would we have built it if it only applied to drones? Maybe, yes, sure, that's a big market. But we realized it had much, much bigger um, um applicability in commercial and enterprise, whether that's critical infrastructure.

• 41:30Speaker 01

  You know, uh autonomous landscapes, be it, you know, in warehouses or other sensitive locations. So it's become a core part of the product now.

• 41:38Speaker 00

  Yeah. Um, and, and in terms of using foundation models for the enterprise, so much of the use now is through agents, is agentic systems that are calling different uh models for different tasks within their workflow.

• 41:56Speaker 01

  Mm hmm.

• 41:56Speaker 00

  Uh, uh, how, is it, is it just the same thing or, or is there some other layer of engineering that you need?

• 42:07Speaker 01

  No, fundamentally, it's the same thing. It's an API service. So you go and pick up the SDK for it and um add it to your agent. So that agent out there can say, hey, I'm about to send uh data from, from this endpoint where the uh agentic uh footprint is up to the, the main um, you know, uh models. Um, and it can just come to us and request a key, prove who it is through whatever uh software based uh or maybe it's passing a hard, hardware credential from the, from the hard, like, for example, we've got it down to running on Jetson devices. We've got a partnership with Nvidia. Um, so it can run on the Jetson device. So when it makes a request of that agentic tool can be sitting there doing whatever it's doing in a workflow, but it comes to us and says, hey, I want to encrypt this information.

• 42:52Speaker 01

  Whether it's periodic for like training data or whether it's constant through um being fed inference data. Uh, and then we know it's come from that Jetson device and off it goes to us and you just build it into your agentic uh workflow.

• 43:06Speaker 00

  Yeah, and it, it, it works on real time data, right?

• 43:10Speaker 01

  Oh, yes, absolutely, absolutely.

• 43:12Speaker 00

  Uh, does that introduce any latency or?

• 43:15Speaker 01

  There, it, a little, but, but um in most cases, it's um it's uh not to use the pun, but indecipherable, yeah, but the indeterminable, you know, fraction of a second type stuff. Um, obviously, if you've got a very large file, we have to take that time. If you had a, you know, gigabyte, petabyte, it would take time. Um. But typically, no, it's uh, it's, it's, it's quite sub second type scenario. And certainly to work in the drone and warfare space, you've got to be sub second because if that says decrypt fire with targeting information or whatever it might be, um, it has to be fast.

• 43:50Speaker 00

  Yeah.

• 43:50Speaker 01

  Same in banking systems.

• 43:52Speaker 00

  The um on, on, uh, besides agents calling uh L L M's, agents are passing data uh between themselves. And this is something I've talked to people about. I mean, you don't know. You, you look at what happened with Anthropic and OpenAI. Uh, you know, that there were, the models were going out or the agents out onto the open internet hacking into hugging face in one case. I can't remember what the other case was, but.

• 44:25Speaker 01

  But hugging face was a good one. Well, that's a good example, isn't it?

• 44:28Speaker 00

  That's right. Stealing uh credentials, you know, writing code into their systems. Uh, how, and, and that's an anxiety I've heard among a lot of business leaders that you get multiple agents or this fabric of agents uh working unseen, largely unseen. Uh, who knows where they're passing the data? And what I thought was remarkable about the uh Anthropic case um is they didn't know until it happened to OpenAI and they went back and read through the logs. And the companies that had been hacked didn't know either, or at least a couple of them didn't.

• 45:12Speaker 01

  Hmm.

• 45:13Speaker 00

  Uh, is that, does this apply to that agent to agent to agent, not, not necessarily agent to model?

• 45:21Speaker 01

  Yeah, that's a really good question. Um, we've seen that from some customers starting to pop up now. Um, originally, you know, the gateway is built to, to connect directly to, to your AI services, as I explained. But yes, we've had, we've had some, some um people using it in that use case where they said, well, we're going to move data around effectively a connected group of agents. So, yeah, it might be the source data gets encrypted here. And that's fine. Now it can be sent and another agent or another um, um footprint, uh piece of software can pick it up. And as long as it's got the permissions and the credentials that it presents, it will allow it to be decrypted partially or just a subset of data. Which is what we've seen where, where uh it might be, you know, a full, a full data set

• 46:05Speaker 01

  encrypted with different policies because that agent needs to use, you know, data set A, that agent needs data set B, that agent needs data set C. But A, B, C are all in an original source. So being able to share that same data set, but only have those other agents be able to read subsets of it is very powerful. Um, obviously it gets complex as you build it out bigger and bigger and bigger and more of a network, but um fundamentally, it's exactly the same tool. It is exactly the same.

• 46:33Speaker 00

  If, uh, uh, I assume that you've done all the red teaming and, and stuff to ensure that this is tight, that there aren't holes in it. Have you used uh Mythos or Fable or one of these super powerful models to try and disrupt or?

• 46:56Speaker 01

  Yeah, we um, so we see it uh actually uh with the US federal government. We sit in a FedRAMP high. So we are ATO'd at FedRAMP high. Uh, which only just came through recently. So as part of those boundaries and environments, obviously you're always having to do, you know, um, constant monitoring, literally constant, you've got to log everything. Um, yes, we throw, um, we throw those tools at it and we've got um access to another one, which I can't name. It is unbelievable in terms of what it can break into busted.

• 47:27Speaker 00

  Foundation companies or?

• 47:28Speaker 01

  Um. No, yeah, more specific one. And so it, I mean, it's extraordinary what some of these tools can do. Um, but it's, it's similar to capability to what that mythos is. It's just, um, yeah. So, look, we do, um. Look, at the end of the day, by design, where's our weakness? Our weakness is not so much that aspect. It's because we tie the identity and key management together. If you use poor identity and poor credentials, I mean, the system's still going to give you a green light, being frank. So if you turn around, it's no different to your online banking. Well, that's probably a bad example because they force you to have a complex password these days. But if your password's one, two, three, four, it's not the system's fault, it's your fault, right? Um, and we're kind of the same way. We,

• 48:10Speaker 01

  you know, if you've got multi-factor authentication and, and complex and pass keys and all that sort of stuff, it's very unlikely that our, uh, our system will be compromised. Or sorry, how you use our system to do everything we just talked about is, is very unlikely. You can never say never. Um, and I think you know this too. Anybody who, well, hopefully, uh, any, anybody who says that, you should get up and leave the room immediately if they said it's, you know, a hundred percent bulletproof. But I always say to people, and I've, over twenty years in, in data, data security and cybersecurity, I say, look, because you can always find a, you know, a weakness in some design, right? That, um, but is it materially better than what you have today? Are you leaps and bounds ahead of what you had today?

• 48:54Speaker 01

  Yes. Well, okay, let's go with ninety-nine percent better and a one percent, you know, area we've got to focus on than having a hundred percent of nothing right now, which is certainly the case with a lot of, there are tools out there in the A I that'll go through and scan your data, but what they typically do is they separate the data. So they pull out, you know, those hundred paragraphs or words and they put that somewhere and then they hand that data up. The problem with that is you've now got different data sets. So when the A I does its training, if you've got, if you're working in regulated marketplaces and you need to come back and say, let, let's say, um, a good example's, um, uh, medical, uh, and, and, um, bio, uh, research, medical research. When you're putting vast amounts of data up there, if you're stripping the data and separating it,

• 49:39Speaker 01

  how do you make sure that the original data set was correct and right? Now, we don't separate the data. We encrypt those individual fields. You can spit it out that side of the A I service and send it back to where it started, back to your data lake, store it there. Now you're storing it with those pieces encrypted. You can prove that all that research you did and all the A I outputs had a chain of provenance that was untouched because the data never, you know, gets, gets, um, separated, if you like. And that's really important from a provenance and audit perspective.

• 50:08Speaker 00

  Yeah. Uh, where do you see this going? Because, you know, I, I, I was a, um, you know, a foreign correspondent, just, you know,

• 50:18Speaker 01

  Oh, yeah.

• 50:18Speaker 00

  not tech at all. Uh, and I got into this when I met Jeff Hinton in Toronto and spent a day with him. And I got very excited. This is in two thousand seventeen. So, uh, where do you see this going? Because the, the, the complexity of the uh, stack and the, and the data layers and the, and the ways that the data is, uh, is, uh, is retrieved and passed along. It just keeps getting increasingly complex.

• 50:58Speaker 00

  And as I said, I talked to a guy about data layer security, but now yours is even more specific. It's, uh, it's data layer, but it's, it's very granular.

• 51:10Speaker 01

  Granular is the right word, yeah.

• 51:12Speaker 00

  Uh,

• 51:14Speaker 00

  is this, do, do you feel like this is the end of the, of the development and, and this will solve everything? Or, I mean, within the problem set that you're

• 51:27Speaker 01

  I, I think it solves things for at least the next decade, at least. You know, when you look where we've come from with firewalls and everything, talk about this is, this is a really big step forward. Uh, we, we are library agnostic from a quantum. You can use any library. We don't care. And this comes along with a new quantum library, plug and play. So if you do this at the data level, at the granular level, great word, uh, with the right post quantum encryption keys, your data's going to be pretty good for quite a while, right? For, well, for decades under the NIST standard. But certainly, it'll, it'll, it'll, it'll solve the issues around autonomous, around A I, all the foreseeable things we can see as much as possible in this day and age, because it's pretty hard to see over the horizon like it used to be. And certainly, yes, we'll have to evolve it very quickly,

• 52:09Speaker 01

  particularly when it gets into vast, all sorts of connected landscapes. We will be constantly, but we're a number of years ahead of anybody on it. So we'll have to continue to stay ahead. But we're at a pretty good scenario compared to where, uh, most people are coming from.

• 52:22Speaker 00

  Are the foundation model companies doing any of this on their end?

• 52:27Speaker 01

  No. And, and, you know, I've spoken to all the big ones and they actually want companies like us because they know their big enterprise customers use multiple foundation models.

• 52:35Speaker 00

  Yeah.

• 52:36Speaker 01

  And we'll continue, that will only continue to get bigger and bigger, right? More specific models and content. So it's not, for example, it's not DeepMind or Anthropic's job to go build this because then actually it's proprietary to them. They don't want that. They want to have services where their customers can use it. And when they're pushing this data set to Anthropic, it you, means they use a lot, lot, lot more Anthropic tokens or over here. So we, we, we, we've spoken to a number of the big ones and we have their great support and they've validated that we're the only ones in the space. In fact, our partnership with Nvidia is literally, um, uh, about going and solving this issue because for Nvidia, obviously, solves a lot of problems, right? Um, in terms of massive uptake of usage of, um, all this forward spent

• 53:17Speaker 01

  infrastructure. So, um, being agnostic to the A I service is, is of critical importance.

• 53:23Speaker 00

  Yeah. You're a relatively small company, certainly a new company. You've got to scale this to handle a lot of A P I calls.

• 53:35Speaker 01

  Yes, yes.

• 53:36Speaker 00

  Is that, are there any, uh, difficulties in that or?

• 53:41Speaker 01

  Because it's a new platform, it's built highly efficiently, uh, with all the latest code development so that it's not heavy. I mean, it literally, it can run right down at the edge on a, you know, Raspberry Pi sized right up to massive cloud, hypervisor cloud. The issue for us is growing team and people quickly enough. Um, not so much the technology. We've got to evolve the technology, but hiring people at scale and, and rapid, um, is, uh, is, is, is the issue, is the, probably the biggest issue. Certainly access to capital's not the problem when you're in, when you've got a combination of A I security plus post quantum capability, um, and key management, um, you know, and, and we're in defense tech as well as enterprise. It probably ticks three of the four hottest buttons around at the moment. Um, so that's people.

• 54:25Speaker 01

  And trying to see, trying to see ahead, trying to look two years down the track, that's pretty hard. I've been in the game twenty years. It was easy, I think, to see five, ten years down the track. It's very difficult to see over the horizon today. And I think anybody says they see differently. I think that's an interesting comment because we all know what's happening in A I, right?

• 54:42Speaker 00

  Yeah.

• 54:42Speaker 01

  That's probably my biggest challenge.

• 54:44Speaker 00

  Is there anything I haven't covered that you want listeners to hear?

• 54:47Speaker 01

  Fantastic. So, no, it's been really good to talk about it. And I think it's, there's going to be more and more people talking about it, right? Because everybody's looking at the stock market to see what happens with all this future build. So solving the security issues and infrastructure issues are a big part of certainty and, and taking some of that, um, risk out of A I. So it's great to have, thank you for having me on. I really appreciate it.

• 55:07Speaker 00

  Yeah, no, it's been fascinating for me. I learn a lot in these conversations.