Today on the podcast, I want to talk about a really interesting company in AI called Confident Security. So they're calling themselves the Signal for AI, which is kind of funny. Like, I think a lot of startups want to attach themselves to an already successful company and just say, we're the Uber for like shampoo, we're the, you know, whatever. It's funny.
But in any case, Signal has, I guess, now made it to a prolific enough place in the market where we're comparing them, we're using them in this way. But in any case, Confident Security, this is a really interesting company. They just raised $4.2 million. It came out of sales.
And they have a really interesting product that I wanted to bring up because I think it has broad implications for the entire AI industry. I think Apple is trying to do something very similar, but in their own ecosystem. So it's kind of interesting to see that there's going to be platforms, products, and players outside of just, outside of kind of some of those perhaps more narrow use cases. So I'll get into what they're doing.
They just raised $4 million, which is a huge kudos and congrats to everyone on the team. Before we get into all of that, I wanted to mention that if you want to try any of the latest AI models, I have a platform called AIbox.ai. This is my own startup, and we are currently in beta. We have the top 40 AI models on there, image, text, audio.
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There's a link in the description. All right, let's get into what Confident Security is doing. So the thing that I think is interesting, right, we have obviously all big AI companies, OpenAI, Anthropic, XAI, Google, and all of them are stacking up tons of user data. Two different places I would also mention, right, like they're going straight to the entire internet and getting everything they can there, but also we're talking to these AI models, and they're, you know, acquiring data that way.
Now some say that in certain use cases they're not using it to train, but others aren't so clear about it. It's kind of convoluted and crazy, and it's really hard to verify any of that in any case. So this is what's really interesting, I think, is that we have this from a consumer standpoint, we all understand this, we're like, oh yeah, I don't want to take my data and use it, but there's way, there's super regulated industries that have, you know, obviously healthcare, finance, these are areas that I believe could benefit immensely from AI, but the security, you know, the security risk that's, you know, tied to all these companies makes it very tricky for these companies to work with. So in any case, this is essentially the problem that Confident Security is trying to solve.
They have a product, it's called CONFSEC, but anyways, it's an end-to-end encryption tool, and it basically wraps around foundational models, right, CHIGBT, Anthropic, any of these models, and it guarantees that any prompts and metadata cannot be stored, they cannot be seen or used for AI training, even by the model provider or by any third party. This is something that's really important to us over at AIVox, my software startup I'm building, anytime that you're sending messages to AI models, we cannot see the models, everything is encrypted on the back end, if we wanted to look at your messages, we can't actually, and this is really important, and so this is something they're trying to solve, this is what their founder and CEO said, he said, the second that you give up your data to someone else, you've essentially reduced your privacy, and our product's goal is to remove that trade-off, so they just raised $4.2 million, this is their seed round, they raised it from Decibel, South Park Commons, Xanti, and SWYX, the company essentially is trying to be a bit of an intermediary vendor between AI companies and their customers, so like hypersalers, governments, enterprises, they're trying to be between these people, so even AI companies, I think, see a lot of the value here, the latest AI browser that's hitting the market, which is coming out of perplexious comment, you probably heard me talk about on the podcast here, they're essentially trying to give their customers a guarantee that their sensitive data isn't being stored on a server somewhere, that the company isn't, you know, and also no bad actors are going to be using this to train AI on their jobs, so this is something that big AI companies are very conscious about, myself included, right, like, it definitely took us much longer to build out a product than we would have liked, and a huge chunk of that was the security, making sure everything was encrypted, making sure everything was private and safe, and like, basically, I wouldn't make a product that I wouldn't want to use myself, and so, I mean, I can understand why this is such a big deal for those companies, it's a big deal for me, but I think this is really where this company shines, now, one area that I think is really interesting is they kind of compared themselves to with something that Apple is doing, Apple basically, if you've been following any of their updates, they have something called the Apple Private Cloud Compute, or PCC architecture, which, um, but basically, they're saying that they are 10 times better than anything out there in terms of guaranteeing that Apple cannot see your data, so they're like, look, Apple might say that, you know, they have this private cloud compute that no one else can see, but Apple can technically see it, and so they're saying, look, even Apple can't see what we have, um, what's interesting is, like what Apple's done with the PCC, uh, Confidence Security Systems is first going to, basically, how it works is they're going to anonymize all of your data, they encrypt it, and they route it through services like Cloudflare or Fastly, so basically, servers never see the original source or the content, next, they then use a bunch of encryption that also, um, that basically only allows decryption under really strict conditions, right, so, so the AI models that are taking this data, they can decrypt it, but they have to essentially agree to a bunch of rules, um, so, basically, what their CEO said about this, he said, quote, so you can say, you're only allowed to decrypt this if you are not going to log the data, and you're not going to use it for training, and you're not going to let anyone see it, so, after they've done all of that, the software that is running the AI inference is publicly logged, and it's open to review, so that experts, and anyone can actually verify those guarantees, like, it's all public, it's all logged, and so experts can go review that and make sure that they're actually, you know, being legit with this, and this is what one of their investors said about, and I always take everything investors says in the company, because obviously they're talking in the book, but, um, this is from Decibel, one of the lead investors, they said, confidence securities is ahead of the curve and recognizing the future of AI depends on trust built into the infrastructure itself. Without solutions like this, many enterprises simply cannot move forward with AI. I think this is actually true, although I know that they're just talking in their book for sure, but, like, there are a whole bunch of industries and areas where it's very tricky to use AI because of these types of issues, and, like, you know, that's not to like them and how they can use it.
I think with tools like this, um, really focusing on the security aspect of it, we're going to see AI integrated into more tools, it's going to become more useful, and more areas that was much more restricted in the past, so for that, I'm very excited, and I think that there's going to be some, uh, some fantastic implications. I'm excited to see, uh, what they're able to do. Um, it's obviously still very early days for the company, but I think it's a, it's a great step in the right direction, so I'll definitely keep you up to date, um, as this company continues to move forward, they've just raised $4 million, so we can expect some exciting things from them. Thank you so much for tuning into the podcast today.
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