(0:00) Welcome to the BLTNT podcast. I’m your host, Matt Loria, serving up real stories of business, (0:05) life, technology, and transformations. You’ll hear from interesting people about big changes (0:09) from career shifts to life-altering decisions, and the innovations that help make it all happen.

(0:14) It’s about sharing those lightbulb moments, pivot points, challenges overcome, and the journeys (0:19) that inspire us to think differently. If you’re on the lookout for insights to propel you forward, (0:23) stories that resonate, or just a bit of inspiration on your next BLTNT move,(0:27) you’re in the right place. Let’s dig in.

All right, welcome to this episode of the (0:40) BLTNT podcast. I’m Matt Loria, and I’m sitting here with my friend Miklas Rambis. (0:44) Thank you for being here, Mik.

Thanks for having me. (0:47) I appreciate it. Mik is the CEO and co-founder of Hush, which is a new service dedicated to (0:52) help families take back control of their financial, physical, and reputational security.

(0:58) The reason I’m having him on, obviously, we’ve got the fondness for cybersecurity here,(1:04) but Mik is also one of the nicest guys that I’ve met, and exceptionally well-dressed, (1:09) and has been teaching me a little bit of fashion sense here, and very, very smart guy, (1:14) and also sat ringside during the Equifax breach. Yes. (1:20) He’s got some battle scars and war wounds and a lot of amazing stories of coming out of MIT (1:27) and emerging to where he is today with a number of different businesses.

We’re going to get into (1:35) stalkers, AI scams, and why, in the next five years, Googling you just won’t work. (1:41) Sounds good. Okay.

Well, let’s hit it off here. (1:44) Tell us about Hush and what you guys are doing and why it’s important right now. (1:50) Yeah.

We started this business, Hush, actually coming out of my last company, WealthX. I’ve (1:55) been a startup entrepreneur for most of my career. There was this business we built, (1:59) which is basically the Bloomberg for private bankers, right? Imagine all the information (2:02) you wanted to know about Warren Buffett or Jeff Bezos, whoever it is, in a database structure.

(2:07) It turns out there are a lot of bad people who want that information. (2:10) No kidding.(2:11) Right? So we found ourselves being approached by Organized Crime, who wanted to get access.

(2:16) We said, thanks, but no thanks. Didn’t go over very well. We found ourselves approached by (2:20) foreign intelligence that wanted to get access to that database of roughly several hundred (2:24) thousand people to find tax optimizers, we’ll say, right? But cross-border tax cheats.

We said, (2:31) thanks, but no thanks to the intelligence agencies. Didn’t go over very well. (2:35) And then we had some oligarchs, some Russian oligarchs say, hey, I’m in your database.

I (2:38) want out. And we said, well, we can’t do that. If you’ve ever dealt with Russian oligarchs,(2:41) you don’t want to say no.

Right? And so, unfortunately, that led to some safety and (2:45) security issues for myself and team. We had to figure out how to get our own information, (2:49) that of our families off the internet. And the reality is there was nothing good that did the (2:52) job.

I remember talking to people in law enforcement, Interpol, journalists who covered (2:56) sensitive areas of the world, and they’d all say, there’s nothing you can do other than one by one, (3:01) like manually go get rid of your information. And so that was the vision or the idea, I guess, (3:05) behind Hush was, well, there’s got to be a better, smarter way. Let’s see if we can build it.

(3:10) Right? Can we build this thing that actually removes our own, but also other people’s(3:15) information off the internet? What we didn’t know is I think how dangerous things have gotten, (3:19) right? That was 15 years ago, 10 years ago. Think about how toxic today is by comparison. (3:26) And that’s where we are.

Wow. (3:29) So, well, I don’t know where to start with the questions. Do you have all your fingers(3:33) after saying no to the organized crime families? Thankfully, currently, yes.

(3:37) Okay, good. Good. Glad to hear that.

What did you do before the system existed? How did you (3:44) keep yourself and your family safe during the time? (3:48) Yeah. So if you dig into the details, like let’s say you Google yourself and people have either (3:53) Googled themselves or they Google somebody else. Everyone’s Googled somebody else, right? And so (3:57) if you look at those websites where your data is held, it’s usually a data broker, right? Data (4:01) broker is a company who’s in the business of aggregating information and reselling it, right? (4:05) If you look into the fine print, go to the legal conditions, terms of use, et cetera, (4:10) almost always it will read, hey, if you can remove your information, if you submit this form, (4:15) send an email, prove that you’re you.

And you can do that one by one. It is time consuming, (4:20) mind numbing. It’s very particular and no one’s got time to do that.

So that’s how we did it back (4:26) then. Now we’ve built technology to do it a lot faster and better. (4:30) And I’m assuming artificial intelligence has been your friend.

(4:33) It has been, yes. Both on the removal. So we have bots that will do that, but also on search.

So (4:39) part of the problem is people think when they Google themselves and that’s the internet, (4:43) there’s a lot more of the internet than that is, of course, as we well know, right? So if you think (4:46) about the internet like an iceberg, you’ve got the surface web and everything above the water, (4:51) if you will, is surface web. Google only indexes 15% of that. There’s still a lot of information (4:58) about people for skilled threat actors who know where to look way more than Google out there.

(5:03) So yeah, AI helps us in finding the information about an individual in all these different places. (5:08) You know, I remember a time earlier in my sales career where I was Googling a prospect (5:15) and their name popped up on a spreadsheet and it had home address. I don’t remember (5:21) if it was last four or so.

It had something very identifying, but home address, cell phone number, (5:28) et cetera, et cetera. And two examples, one was it had their slip number at the marina that they (5:34) were located at. And then another was a very similar one, which was a marathon registration.

(5:42) So it even had their weight and age and all that sort of stuff. And I just couldn’t believe(5:48) such personal information was out there in a spreadsheet. Not only did I have that person’s (5:52) information, but I had the other 50 people that were in that race or the other 100 people that (5:57) were in that marina or whatever it was.

And I just couldn’t believe it that you’re thinking, (6:01) okay, how many times do I clicked on the opt out for a company? But you don’t think of these (6:06) kind of more just community oriented places that you’re dropping your information. (6:14) That’s right. And that assumes, by the way, those companies give you, or those organizations give (6:17) you a way to opt out, right? A lot of the smaller ones, they don’t even really think about.

(6:21) Yeah. These had no idea. I think this was somebody who probably had really good intentions to keep (6:26) the event organized, probably put it on a Google drive or something like that, left it all open, (6:31) no password, no security.

That’s right. And somehow I inadvertently found my way into it. (6:36) Was chatting with the CEO of a tech business who we know, and in their case, it was their (6:41) kid’s school that had accidentally put the entire school directory online, right? So we (6:46) happened to find the hit because we’re looking for the individual and their family, but every (6:50) family’s name, all the kids’ names, the emails, the phone numbers, their addresses, all available (6:56) online, right? Wow.

And what happens with that, I’ll give you another story. You mentioned the (6:59) flip number, right? For the boat at the Marina. I’m thinking of a venture capitalist who says, (7:04) one Saturday he gets a knock on the door and okay, a little unusual.

And a guy shows up at his front (7:08) door to pitch him an idea, right? And so he thought it was a bit odd and weird and said, thanks, (7:13) but no thanks. I’ve got an office and a process. His wife was pissed, right? And so that’s the kind (7:19) of stuff where you don’t want that information out there and people don’t recognize it.

Look, (7:22) I get it. We don’t talk about this kind of stuff at cocktail parties, but it happens all the time. (7:26) Wow.

Wow. Well, I’m hoping we can get into some more of the stories because I think people will(7:31) really connect with those and they’ll resonate with them to say, okay, could that happen to me? (7:37) Yes. So let’s go into a little bit of backstory here because really right out of college,(7:45) you got into a venture backed business, kind of your first gig.

(7:50) That’s right. Even before actually. So it was in the dorm room.

So this was senior year, (7:55) second semester and had an idea, actually had a few ideas, not sure which one to take. And (8:00) yeah, we’re very fortunate to get early backing from SoftBank and another Boston VC (8:04) in those early days to get going. You know, the funny thing, it was about mobile technology.

(8:09) So if you can believe, you know, we have smartphones everywhere. This was Palm Pilots(8:13) and so forth. But yes, it was a very good journey.

Learned a ton of how not to run a business back (8:18) then. Eventually survived. We ended up selling that business and moving on, but nevertheless, (8:23) yeah, there’s a lot of lessons learned those early days.

(8:25) Can you tell us about each of these businesses? How many of you formed and successfully sold? (8:31) So three venture backed. So two exited and now we’re on number three venture backed. Other (8:35) companies, real estate and so forth.

It’s been about six in total. My first one was way back in (8:39) high school doing computer parts and repair. So, you know, I learned.

(8:43) You were kind of in our business. (8:44) I was, I was a little bit of, you know, the lower end of what you do, right. But you know, (8:49) and, you know, adding memory to computers and fixing modems and those things, but (8:53) someone had to do it.

Right. And I figured, Hey, if I can make some money that way, all the better. (8:57) Great.

Where did you, well, first of all, should we ask you these questions? Like, (9:01) you know, what area, what area of the country did you grow up in? (9:05) Oh, sure. Ask me anything. I can’t always answer, but ask me anything.

I say that because (9:09) it’s interesting. You know, we all know, I mean, I don’t know about you. I can imagine if I were (9:14) growing up right now, everything’s recorded, right? Every video is there.

It’s all out there. (9:19) And so as we think about our content videos, podcasts, everything else, it used to be, (9:23) you had to find, you know, that one entry, like you said, you had to find that one PDF from the (9:27) race that had everyone’s details. Now you can ask chat GPT and say, show me everything you know (9:32) about this person and the work is done for you.

Right. So, so yes, you’re right. I am a little (9:36) more cautious or politely paranoid about the things that I share, but yeah, I grew up here, (9:41) born and raised in Michigan.

Right. So I’m very excited to have gone East to university to MIT. (9:47) And then, like you said, started that first company.

So tell us about that first company. (9:50) So that was, you know, I still remember the, the agreement behind the company is myself and four (9:56) classmates. The idea was to build a mobile application consulting company, right? So no (10:02) one really knew where mobile was going, kind of like AI these days where it’s a whole, you know, (10:06) sort of evolution and we didn’t know what to build as a product.

So we said, well, (10:11) we’ll be a consulting company. Right. And so we start off as a mobile app development consulting (10:16) company and we work for groups like MasterCard and some of the carriers like Verizon to go build (10:21) early mobile applications.

We even built an early navigation system that had voice and visual, (10:26) you know, it’s funny you think of that as okay, everyone does that. But back then it was really (10:29) hard to do. And so we built apps, right.

For different companies. We eventually turned that (10:34) into a product, basically a software development kit that companies could use to build their own (10:38) apps. And that’s what the company was ultimately sold on.

Great. How do you feel like that? Go (10:44) back to, you kind of made a parallel to that, that app dev boom time and also time of uncertainty to (10:53) the AI boom time that we’re in right now and complete uncertainty as well. And can you make (10:59) some parallels there of, of what business leaders are going through and what they’re challenged with, (11:05) with, with, you know, I need an app.

I mean, every, every business owner that I talked to today (11:10) basically says I need to be an AI. Yes. I don’t even know that, that, that everyone knows what (11:15) they mean by that, but they certainly don’t want to be left behind.

So can you, can you talk about (11:19) that a little bit? I mean, it, it feels like, you know, 2000 where people are saying I need to be (11:23) on the worldwide web. Right. Exactly.

I think you’re spot on. I think we saw a reaction to this (11:28) where people were hiring chief AI, you know, officers in their company sort of like chief (11:33) innovation, I guess you could say, but a lot of it is a lot is foundational, right? If your business (11:37) wasn’t on the internet in the early two thousands, you made that, that transition, that transformation. (11:43) And now everything happens online and everything happens on mobile, right.

And so forth with AI. (11:48) I think it’s, you know, it’s interesting. You saw the the jobs data come out recently, right.

(11:52) Where there’ve been a lot of companies that are not hiring. I think, you know, probably not set as (11:56) much. There are a lot of organizations, a lot of CEOs who are thinking, okay, how do we maneuver (12:00) through and use productivity from AI, let alone the innovations that come through it to be able (12:06) to hold right fast and think about our company growing without adding more people.

And so from (12:12) a CEO perspective, I think the first thing that a lot of folks are looking at is, okay, where can I (12:15) get the employee efficiencies and how’s that going to look? It will completely disrupt entry-level (12:20) employment, right? You think about law firms, you think about finance organizations, all of that (12:25) analysis and so forth is done really well, really quickly with AI. So we’re at the beginning of a (12:31) big disruption here. Let’s, let’s dig in on the law firm piece just a little bit here, because (12:36) that’s one that, that, that gets my brain really tied up.

I can see where artificial intelligence (12:44) today can make a, let’s say you’re, let’s just pretend there’s a scale of level one, two, three, (12:50) and four of, four is a top-end senior lawyer, knows kind of everything they need to know about (12:56) their craft. And then level one is that person fresh out of, fresh out of college. The level four (13:02) attorney had all of the benefit of those late nights, digging through documentation, et cetera, (13:08) et cetera.

And in one, they, they were learning how to find the information. Two, then they were (13:14) learning the information and three, learning how to apply it and make it part of a relevant argument (13:19) of some sort. If you take away steps one and two, how does the lawyer, how does that lawyer that’s (13:27) in college today ever get to level three and four? That’s a great question.

And I don’t know how, (13:32) I don’t know how either. I don’t know that they do. You’re absolutely right in the research.

(13:36) You know, in theory, the research is done for them. It’s aggregated. They can sift through (13:41) the results.

They can know how and affect the machine that has thought and gone about it. (13:45) So I’d like to think that it’s explaining, right, how they’ve done their work. So there would have (13:50) been wasted hours in finding articles that weren’t relevant, why they weren’t relevant, (13:54) right? That is substantially more codified, made efficient, but it’s a real challenge, right? I(13:59) think how do people get from there here to there? I think there’ll be a lot less associates at law (14:03) firms going forward, right? Because that will be, you’ll still have associates, but there’ll be (14:07) technology enabled, right? And so they’ll be the ones who will eventually become the partners, (14:11) but it changes the game entirely.

Same for the big consultant series, right? Accenture, (14:16) you know, PwC and so forth. I think you have a lot less junior people doing the work because (14:22) it’s just not needed anymore. Yeah.

I was, I bumped into a former McKinsey consultant (14:26) who she’s now on her own working in the Detroit area here. And I said, I said, oh, that’s (14:31) interesting. I speak to you all the time through my, through my chat GPT, because I’ll tell it (14:35) as a McKinsey consultant, answer this, answer this question.

And it’ll give me a McKinsey level (14:40) report, you know, out of there. And so the disruption is, is really interesting. But I (14:47) think that, you know, you have a point there where you said the associate will be technology (14:52) enabled, right? They’ll have to know how to go after that information and data and then know (14:58) how to sift through it themselves and then tell the bots what’s relevant to them or not.

That’s (15:03) right. So you know, just gaining that experience I think it’s going to be a lot of trial and error, (15:08) but it just will be the new way. It will.

You know, I think added to that, (15:13) the technology is, is today, right? So go to where not only can it do the homework and do (15:18) the research, everything else and explain, but also take on a organization’s persona or a partner’s (15:23) persona to say, okay, well, if you were being highly aggressive, let’s say about a given topic, (15:27) or if you wanted to be very risk averse as a simple example, right? Even from a branding (15:32) perspective, we say, look, I’d like XYZ tool to look at all of the things out there about our (15:37) company, Hush, everything we’ve said, all of our messaging and create new messaging, a white paper, (15:42) let’s say that takes into account our, our particular way of storytelling and our language, (15:48) our word, everything else it can do that. Right. And so I think you’ll have a use in law and other (15:52) places where the technology not only is doing the homework, but it’s also emulating how that(15:57) organization and its culture interface with the rest of the world.

Okay. What about, I’ll give (16:03) an example. A year ago, year and a half ago, I was at a seminar for AI, a three-day seminar, (16:11) and everything that this person taught us was about prompt engineering.

Okay. Creating the (16:16) perfect prompt within six months, everything that they told us was obsolete. What are you seeing (16:24) now as the next level of obsolescence in what we’re teaching ourselves about AI and how to use it? (16:30) Oh, gosh, it’s a great question because it is moving so fast, right? I’m in the business (16:36) broadly and it’s hard to keep up with all the changes that are going on.

(16:42) Gosh, probably right now it’s around content creation, right? So yes, they’re the LLMs. Yes, (16:47) there’s a generative AI that we’ve seen in the last year and a half, two years. It’s getting (16:51) really easy, right? So Google recently published a new model, which allows for the creation of (16:57) video content far more easily than ever happened.

This is in the last month, right? (17:01) And so I think, yes, it’s on prompts to a degree, but it’s the evolution of those. (17:07) I also think, as I mentioned earlier, the models are getting better at mimicking either individuals (17:12) or organizations, right? Which is actually one of the scary parts from a cybersecurity perspective, (17:17) because you can say, okay, we’ll mimic this person, right? And it can go and find all of (17:20) the content, all of the video, all of the audio, and then emulate that individual. (17:24) We talked about impersonation a little bit.

I know we will at some point, but (17:28) it’s getting really scary good. But from a learning perspective, it’s just staying current (17:33) on all the tools that are there. Yeah.

I mean, I feel like if I stay out of it for a week, (17:39) I’m lagging. Yes. It’s unbelievable.

What do you know about agentic AI? (17:48) So a fair amount. Maybe we should define that, define the agentic AI in your world. (17:56) Yeah, the non-academic version.

Basically, an interface where an AI, and again, I’ll use bot (18:01) for simplification, right, is engaging with an individual, taking the feedback or the information (18:06) that’s provided by that human, let’s say, that interface, taking it back, doing something with (18:11) it, changing or adjusting behavior or outcomes as a result, and continuing to have that back and (18:16) forth, right? The example I’d give is you could say a financial advisor, right, where someone is, (18:21) you know, robo-advising is kind of a concept where you first sign up for an account, (18:25) you answer some preferences and the rest, and maybe those things stay fixed pretty much, (18:29) right? And an agentic approach would be, again, hopefully someone doesn’t change with the weather (18:33) of the markets, but if they did, that back and forth continuous feedback would be very helpful. (18:38) That would be more of an agentic approach. Okay.

And then even a simple example that I’ll (18:42) give is just acting as an online agent. Yes. So, as an agentic, you know, agent.

That’s right. (18:48) Right there, seeking help. So, help desk, right? We know that this is coming in our world of (18:54) managed IT services, that agents, bots, will be part of the, you know, be part of the solutions.

(19:01) Absolutely, yes. And, you know, already are in so many places, right? Yes, very much.(19:05) Now, some of those just are not refined.

I mean, I had an interaction with the car dealership (19:10) the other day, and boy, did I get an apology call from them on that. I went in, tried to make (19:18) an appointment to bring my car in for an oil change. I tried to just simply put the information (19:24) in on a form, but it kept popping up a window to chat with me.

Finally, I just assumed that I (19:29) couldn’t put it in, that I needed to use the agent, and it represented that the agent was a real person. (19:35) And boy, was that, that was frustrating, because it wasn’t a real person. It was three fake names, (19:42) you know, that these are the, these are the current agents on call.

Sure. And when it, when it caused (19:48) difficulties for me, I finally sent an email to the company and said, hey, this is a terrible way (19:52) to get me to buy another car from you, because if you can’t get this piece right about me getting (19:57) a simple oil change, I don’t want to buy another vehicle from you. Yes.

So, they called me and (20:02) they talked me through it and they said, yeah, you know, we’re actually, we’re, we’re sunsetting (20:06) that system as soon as possible. One of our major complaints is that it represents that Jennifer (20:10) is a real person and Jennifer is not. So, one of the commitments that I’ve given here is that if we (20:15) ever engage with agentic AI or any sort of bot behavior, that the person has to know that they’re (20:22) talking to a bot.

Yes. I just don’t, I don’t, I don’t like the, the ethics of that otherwise. (20:28) I can agree with you entirely.

We don’t use them at our organization, right? The, the interface on (20:32) the, in the app, if you will, is, is to an actual human being. Although I can see a place where (20:38) humans like interacting with other individuals, right? They like that perception. That’s why (20:42) so many companies in agentic AI have put forth these names, right? Avatars and so forth.

It’s (20:46) a little weird. I would agree still entirely. But I could see a place where it is effectively(20:51) handed off where if the bot can’t do what it’s supposed to do, that it hands off to a person (20:55) in a seamless way, right? Right now, a lot of times it’s, I can’t help you.

I can’t figure (20:59) this out. Let me refer you to my colleague. I think we’ll start happening is it’ll simply be (21:03) passed off to a human.

They’ll continue the conversation and you’ll never know the difference. (21:07) Oh, interesting. Okay.

Yeah. So we’ll see where that goes. We haven’t gotten there yet.

Our, (21:12) our clients expect humans. I would, I would agree. I would agree.

So we’ve talked a little (21:17) bit about the, about kind of the stalker aspect of what hush deals with, right. And kind of the (21:23) genesis of why hush exists because of those, those stalker like behaviors. But what about (21:29) the AI scam side of things? How is hush kind of preventing, preventing that for the, for the (21:36) users? This episode of the BLTNT podcast is sponsored by Oxium, business IT and cybersecurity (21:48) designed to outsmart chaos.

Empowered by Juniper Networks, automate your network with Juniper (21:53) Networks and the Mist AI platform, the world’s first AI driven wired and wireless network. (22:06) Sure. So you know, unfortunate truth, right.

That the identity theft market in the U S financial (22:13) fraud, right. Is bigger than the illicit drug trade, right. It is a, is a tremendous industry (22:18) that continues to grow, made it to Hollywood, right.

You saw in a beekeeper, the movie on Netflix, (22:23) right. That’s just the, you know, a small window again, dramatized about kind of what goes on, (22:28) but it happens all the time. Right.

So I think it’s one out of 17 Americans has their identity (22:32) stolen each year. There are people who are being trafficked, right. To do this.

So people often (22:38) ask me and us, well, gosh, who’s doing this and where do they find these people to do it? (22:41) Yeah. They must be such bad, bad humans, right? Exactly. Now, a lot of times they are literally (22:46) being held against their will, right.

And, and effectively, you know, sweatshops are worse, (22:50) right. To do this work. And then if that weren’t bad enough, technology is making it easier, (22:55) right.

For folks. So you back to AI there’s something called a fraud GPT, right. So we’re (23:00) all familiar with chat GPT, how flexible that is.

Imagine, you know, fraud GPT, which we probably (23:05) shouldn’t show, right. Cause it’s on the dark room, the tool set, but that tool is out there. (23:09) And so this goes back to, well, how do you find information about someone that’d be useful? How (23:13) do you find the answers to their bank challenge questions to unlock, you know, XYZ that they may (23:18) use their credit file, right.

Their two-factor authentication and so forth. But all of those (23:23) tools are there. And then there’s the impersonation that goes along with it.

Right. So most criminals (23:29) don’t really even have to bother with the dark web anymore. Or if they do, they get one piece (23:33) of information because most people share a ton on their social media accounts, right.

(23:38) Or from data brokers. And so all they need is several of those pieces of data to do what they (23:43) do. And again, that used to be a manual process.

Now AI can go grab those things and automate it (23:49) increasingly. So, so, so the AI and the, between the data mining and the AI, the secret questions, (23:57) like what was your first car? Were you a great name of your grade school teacher or (24:01) things like that? You’re saying those are being aggregated in there. (24:04) Correct.

That’s right. And it’s shockingly easy to do. Right.

So not hard to figure out (24:08) your dog’s name. It’s not. Right.

And people may say, well, I’m not on social media. I don’t, (24:12) I don’t post that. Well, yes.

What about your spouse or partner or your teenage kids? Right. (24:16) Or like even the friend who visited your house last week, one of your teenagers, friends who put it (24:21) on social media, right. You didn’t know about.

So there’s always that information leakage. (24:25) Yeah. And when I’m thinking back to, so our oldest son is getting married this weekend, (24:29) as I, as I mentioned to you.

And one of the ways he got in trouble as a kid was (24:33) his friend posted up a picture of him in front of the emergency room sign. (24:40) And my wife, my wife bumped into that on the web and that wasn’t, wasn’t too good. So (24:46) yeah, you can never, you can never, you can’t even personally (24:52) police everything about yourself that’s out there.

(24:54) It’s very hard to do. Yes, that’s right. Because it’s, yes, you can, you can police yourself,(24:59) but policing your family and your extended group is very difficult to do.

There’s that, you know, (25:04) even so Michael Dell, there’s this, this famous story about his daughter. So Michael Dell spends (25:09) a ton of money on private security has for a very long time. And so there’s a story about his (25:13) daughter getting onto a private jet, right.

And basically posting on social media about her (25:19) upcoming activities. And for people who are very substantial means private, well, that’s the last (25:23) thing you would do is say where you’re going to be, right. Made her security team detail crazy.

(25:30) Yeah. So, so your daughter is in the room today while we’re filming. So is there a bit of advice (25:38) right there for her if she gets on the private jet? No, no, no posting.

Yes. No posting, right. (25:43) No posting.

Yeah. No social media, right. By design.

Yeah, exactly. And what are some of (25:47) the rules that you have for your own kids? Sure. So, so one of those is a delay devices, (25:52) right? So, you know, we don’t think we advise other people and look, every family is different.

(25:56) Everyone’s gonna make their own decisions. We’re very aware of that. We’re also aware that every (26:00) family is different from a risk perspective, right? What they’re worried about and what (26:03) they’ve experienced in the past, but generally speaking, delay, delay, delay devices and (26:07) smartphones in particular, right? There are other ways to stay in touch, whether it’s an Apple watch (26:11) or a flip loan and so forth.

And we had that conversation earlier, right? So that that’s one (26:16) social media. You know, there’s a reason why you see so many people, (26:20) tech bros and otherwise who their families are not right on social media. So I think that’s (26:25) indicative of well, certainly.

And then there’s just awareness, right? You know, we often joke (26:29) at our business. We wish there was a law and order cyber crime, you know, addition, because people (26:34) don’t see the scams coming their way. They don’t see how the dominoes fall.

And so if you’re not (26:39) in this, like we are every day, you’re not looking for it, right? So having that awareness of, well, (26:44) how does someone’s two factor authentication get hacked when they think it’s very secure with (26:48) their bank or how did malware get on their phone? These are important conversations that we have at (26:53) least once a week in our house. Great. Great.

So you’re just making it a part of the, the norm, (26:57) just like somebody would say, don’t talk to strangers. That’s right. Yes.

Yes. Lock the door. (27:02) I mean, you know, we, we say all that stuff.

Don’t talk to strangers. You know, lock the door, (27:07) all these things that we drill into our kids over time. But these, these technical pieces are, (27:12) are added to that conversation.

They are in your house. They are indeed. Even something like (27:15) connecting to wifi when you go on a trip, you know, how many teenagers will find any wifi (27:19) they can get ahold of, right? Just connect.

And there are plenty of honeypots that are out there (27:24) that will pull and get access to that information. So we should explain. So a lot of our audience (27:29) are not it executives, their business executives.

So explain what a honeypot is. Sure. So let’s say (27:36) you go to some hotel resort or, you know, any place where you think is reasonably popular, (27:41) heck even an airport, for example.

Right. And you’re looking for the wifi. People assume (27:45) that the name of let’s say that property and wifi is actually the wifi that might actually be a (27:52) hacker’s wifi node that they’re basically trying to encourage folks to connect to and has a similar (27:57) name.

Right. So, and once they do that, if your device doesn’t have all the recent protections, (28:01) they can attempt to penetrate your device, get access, right. And from there commit whatever(28:06) crime they’re looking.

Sure. And once they’re in that device, then when that device is reconnected (28:10) to your private network, back at your home, back at your business now, then they get in (28:15) even further. That’s right.

Yes. And you know, people often ask, well, it’s just a little old (28:18) me. Like what’s, what’s the, you know, why me? The answer is think of it just a massive net.

(28:23) This is about fishing and not the social engineering term fishing, but just casting a(28:28) very wide net. So if that happens for 10,000 people, hackers will say, well, who do we have? (28:33) Right. Okay.

Oh, there’s 50 people. This looks interesting for let’s go target them. Right.

So (28:37) it’s opportunity. Well, and sometimes the, the, I mean, just like in fishing, you’re catching a (28:42) small fish to then catch a big fish. That’s right.

Yes. So I think that’s the piece that people (28:46) always miss out on is what’s little old me. Well, little old, you may work for bank of America.

(28:51) That’s right. Or little old, you might go, you know, into the take-home building, you know,(28:56) once, once a month, that’s right. Whatever that might be.

That’s right. Yes. You know something (29:01) we’ve talked about.

And so little fish, big fish, let’s talk about that a little bit is (29:06) right now, hush. The main focus for you is, and this is, this is on a life cycle (29:12) or a product life cycle that, that I think this is where you’re starting. And then (29:15) eventually it’ll become ubiquitous for, for others.

But right now you’re really protecting (29:20) and focused on high value, high net worth and high visibility clientele. Is that, is that correct? (29:27) Generally speaking? Yes. So there’s a few buckets.

One of course is yes. Private equity, (29:33) family office, right. And so forth.

We work with a lot of businesses and those businesses (29:38) are named brands, right? Probably can’t name them here, but you know, fortune 500, (29:42) fortune 1000 companies and their employees. And it could be any level, right? So it might be (29:46) the assistant treasurer, for example, or the accounts payable clerk, because they are targeted (29:52) by criminals as well. So yes, it may be with a big company, but those individuals, we give our(29:56) solution away to current and former members of the military.

We know that they face enhanced risks (30:03) and there are frankly, just not effective tools out there for them. So automatic to them and their (30:08) families. And then we have we have some other programs for current and former public servants (30:13) as well.

As sadly you’ve seen, you know, the news there is far too much toxicity and, and danger, (30:20) right. For those who are serving today. So.

Sure. So and on that, on that life cycle of just kind (30:26) of where Hush is going, you start there and then you’ve told me that in five years, googling a (30:32) person won’t even really be a thing necessarily. That’s right.

That’s our vision. Certainly. Right.

(30:36) So taking identity theft, going back to that, right. So identity theft in the U.S. is twice(30:41) as bad as it is anywhere else in the world. Right.

There’s a tremendous thing. Billions and billions (30:46) of dollars are lost to this every year simply by working for a bank, a credit union, et cetera. (30:52) We expect that, yes, in five years time, your information won’t be findable.

Right. It’s (30:55) ludicrous that I can go look up anyone who works at Lawrence Livermore National Laboratories, (30:59) the ones who look after and engineer our nuclear program amongst others and find a ton of (31:04) information about all employees and their families at large. And by the way, so can other nation (31:09) states at the same time.

So that’s got to go away. We’re so worried about in the press these days, (31:16) retribution, retaliation for what’s happening militarily. That will probably be directed at (31:21) private sector companies as well.

Right. And so same thing. The best way to shut it down is to (31:26) start pulling back that information about employees and just make them quasi invisible.

(31:31) That’s right. Yes. And it’s quasi right in this technology age.

We all have to have some (31:36) digital footprint. Right. But we don’t necessarily need, you know, our kids information fully (31:42) available or dogs or otherwise.

I’ll give you another example as to why I think we’re getting (31:46) to this place where in five years time the information won’t be there. So ransomware, (31:52) we’ve all heard the term and seen the press releases from a breach perspective. We can (31:56) talk about, you know, any number of these big these big breaches.

It used to be that, hey, (32:01) we have your information. And unless you pay us ten million dollars, whatever the number is to (32:06) scattered spider, whoever, that will release the information on the Internet. A lot of them have (32:10) been targeted at health care right the last year.

And now it’s if you don’t, we’re going to reach (32:13) out to your patients, let them know that we have their information to apply pressure to the (32:17) organization. Sure. Now, what law enforcement is sharing and we’ve seen is the criminals are (32:23) finding information about the employees of the organization, the things that scare them like (32:27) here’s where your kids go to school and saying, unless you pay up, we’re going to reach out and (32:31) find your family and otherwise.

Right. So we’re in a place where, yeah, two years from now, three (32:37) years from now, we’re already seeing this will happen in Minnesota. There will be laws increasingly (32:40) probably at a state level.

We’ll see if we get there federally where information is going to be (32:45) pulled back. I think data brokers don’t have a very long runway anymore. When you talk about the (32:53) about those state laws just a second ago, you mentioned that the United States is really (32:59) the worst at at data privacy.

Now, we don’t have we have things like HIPAA and some others, but we (33:06) but we don’t have GDPR. Like, can you talk about that a little bit? I know you’ve worked internationally (33:11) and you know that you’re you’re versed on the subject. Yeah, absolutely.

So so Europe, like (33:16) you said, has this GDPR privacy law, which I think is probably more reflective of European (33:22) culture. Right. We’re generally speaking, people don’t share a lot of information.

Right. They (33:27) don’t talk about their families as visibly. I mean, yes, of course, people are on social media (33:31) and so forth.

But there’s a culture of holding back information. You know, when you think about (33:35) Switzerland, right. It’s what’s, you know, banking, privacy and so forth and hushed conversations (33:40) to think about rights, sort of how privacy is looked at, generally speaking in Europe.

(33:45) And so I think that bill reflected right. What was there? I’ll give you an example in Germany, (33:50) you know, Google Street Maps. Right.

You can see a house and type an address and see what’s there. (33:55) So many people removed it in Germany and took down their own property where they actually shut (33:59) down the service. Right.

OK. So so Europe gets privacy, maybe to the extreme, maybe more than (34:05) needed. But certainly I think it’s a step in the right direction.

The U.S., by comparison, (34:10) has state regulation so far. There’s talk about it at a federal level. California has led the way (34:16) something called CCPA.

Right. That’s the California privacy statute. And it basically allows a (34:21) consumer to get their information taken down.

Right. In effect, there are now 20 U.S. states (34:26) that have some kind of privacy rule and reg in place. Michigan’s not one of those just yet.

(34:31) Hopefully it will be, but done in the right way. I’m the first to say there should not be an undue (34:35) burden on business. And so that’s we’ve got to watch out for.

But it’s coming for every state (34:40) at this point. And then there are, thankfully, particular laws around anti-doxing. Right.

For (34:47) those who aren’t familiar, where an individual finds someone’s home address or their phone number, (34:51) elected official or otherwise, and puts it out on the Internet for all to see.(34:54) And then swatting, as you well know, has been a problem for years. This is when someone who has (35:00) a beef, a problem with a number individual, basically calls law enforcement to that person’s (35:06) house, fakes or falsifies a report.

And then law enforcement comes charging in, (35:12) usually with very negative outcomes. Right. So most states have laws, I should say most, (35:16) a lot of states have laws for anti-doxing, anti-swatting.

And we’re getting to the (35:20) place where they have the privacy laws. Yeah. So I don’t want to geek out there too much.

(35:24) Trevor Burrus No, no, no. No, this is great. I mean, (35:26) but nobody’s going to want to leave their house after they watch this episode.

Or maybe they (35:33) will because they’re afraid they’re going to be swatted. Go back to in your career, (35:40) right, a little bit more of that WealthX and how that really informed what you’re doing today (35:48) and where the holes were, you know, that you were seeing in the market. And maybe feel free (35:53) to extrapolate on the stories that you told about yourself and kind of being victimized by that.

(35:58) Sure. Happy to. So at WealthX, again, there’s this Bloomberg for private bankers.

That was the (36:03) idea. And this was coming out of 2008, the financial crisis. All of the talk in financial (36:08) services was not about trading, not about investment banking.

It’s all about wealth (36:12) management. Right. We’re at the point where we’re in the largest wealth transfer in the (36:16) history of the world, right, from baby boomers to the next generation.

It’s a good time to be a (36:20) wealth manager. Right. And so the idea was, let’s give information to these people because this (36:25) will help them grow their wealth management business.

Right. And I don’t know about you, (36:28) I get calls all the time. So there’s a lot of activity in wealth management.

(36:32) As we started to do this and build what we call dossiers on individuals, all publicly available (36:38) information. So no dumpster diving, no talking to ex-wives or anything else, all publicly available (36:43) and putting that information together. It was shocking what you could find.

And we actually (36:47) left things out. Right. So we left out addresses, we left out phone numbers.

Meaning you almost felt (36:52) bad. There was too much detail. Entirely.

Right. The fact that I can go find a billionaire’s (36:57) kids where they go to school, who the nanny is and where they are on a Friday afternoon is in, (37:02) to me, it’s insanity. Right.

And certainly of concern. Right. And we always said, gosh, (37:07) we’re doing this at arm’s length.

This is publicly available. But someone needs to let (37:11) them know. Right.

That this is out there. I remember, you know, once we found the phone (37:16) number for someone’s, you had a client of ours, you know, very high regarded wealth manager. (37:22) And so they asked us to find a way to get ahold of someone.

We rarely did that. In this case, (37:26) we did. And we found the number to this billionaire’s ranch that rang inside the horse (37:32) stable.

Right. So and they picked up, by the way, that’s that’s the funny story. Right.

So (37:37) this information is out there if you know where to look and it’s publicly available. But too many (37:41) times it was, again, things that would put naturally almost any family at risk. I’ll give (37:47) you another story.

It’s been a heated political season, shall we say, for the last several cycles. (37:53) Sure. Donation information is publicly available.

Right. Who gave to what campaign? (37:58) And there are a lot of people on the other side of the aisle who don’t like certain (38:01) individuals. Right.

And so there have been plenty of examples where someone (38:05) who doesn’t like a given candidate calls and harasses someone who made a gift (38:09) right to that candidate. And I don’t mean Elon Musk. I mean, two thousand dollars to a candidate.

(38:13) Sure. And they will say all kinds of horrible things. I’m thinking about one of our (38:17) relationships where they looked up information about that person’s adult daughter, (38:21) knew that she had gone through some pretty rough times on social media because it was there,(38:25) right, and unlocked.

And they basically called and harassed this this adult daughter, saying (38:30) your your father is this horrible person, done all these things, everything else. This is all (38:33) over a few thousand dollar political donation. Wow.

So this stuff happens and people just don’t (38:39) recognize it. So that’s the WealthX times where we know this stuff is out there. We’ve been (38:44) personally affected.

These people who are in our database don’t seem to sort of see and get it, (38:48) even though most very affluent people fancy themselves as very private. (38:53) The reality is they’re not. Right.

And so there’s got to be a better, smarter way. Right. We even (38:58) thought about having a business extension to help pull back some of that information that we left (39:03) out of the dossier just to get rid of it.

And so here we are. Right. Several years later.

(39:07) Great. So that company, though, to you. So let’s let’s talk just about kind of the entrepreneurial (39:12) spirit there, too, that that company also was three guys on a couch up to two hundred and fifty (39:18) employees, 13 countries, I think you were in.

That’s right. Is that correct? Yeah, spot on. (39:23) So we started, yeah, three guys on a couch in the Upper West Side of Manhattan.

I remember our first (39:27) office was, if you can call it that, was an interesting place. We were next door to bill (39:32) collectors, debt collectors. Right.

I remember hearing those phone calls in the background. (39:35) You know, we’re glad to move right after that. And then, yes, we grew pretty fast.

We moved the (39:41) business from New York to Singapore. So we headquartered out of Singapore, (39:45) believed that we authentically needed to be in the market where the biggest brands, global UBS (39:50) and Citibank and so forth, were focusing a lot of their efforts. So so we did the Asia realignment (39:56) at our small company and then we grew, like you said.

So we ended up getting to about 500 (40:01) clients in about four and a half years and sold that to private equity after having expanded like (40:06) we did. Amazing. And then and then you went to was that when you went to Equifax? I did.

(40:11) Seemed like the next logical place to go after a wealth data business, (40:15) WealthX. And so, yes, ran the consumer data division there. Was there before the breach (40:20) and then during it, of course, and then the aftermath thereof.

Thankfully, I wasn’t an (40:24) IT leadership during that time. I was a business owner. But look, it affected us all.

As rightly (40:30) it should have. You know, what is often said, and I’ll say here, is it’s unfortunate that it (40:35) happened. Shouldn’t have happened.

At the same time, I would love for people who are listening (40:39) in to know that all of our information pretty much is already on the dark web. Correct. So we (40:44) talked about this iceberg and there’s the above the water below the water in the bottom of the (40:48) iceberg.

The bottom of the iceberg is a dark web. Our phone numbers were already there, right? Our(40:52) socials were already there. And so it’s unfortunate, again, shouldn’t happen.

Of course. But (40:56) everything is there. It’s been stolen.

It’s sitting there, unfortunately, for threat actors to take (41:00) advantage. That’s funny. I was just asked on an interview the other day about the the more recent (41:08) Google and Facebook.

You know, there was a big, big breach there of I think it was 16 billion (41:13) records. Yes. Or net new records.

And they said, you know, why is this one any different? It’s like, (41:19) well, this one’s a little different because, you know, it’s new passwords. It’s new information. (41:24) It’s not the data that’s already been aggregated and sold a hundred different times.

Right. So (41:29) when we hear of, you know, health care company getting getting hacked, most of that information (41:34) is out there. Even the Equifax one that was already out there.

But this Google breach and (41:39) I was I believe is Google meta and and somebody else. Yes. But nonetheless, can you explain kind (41:47) of your view on that? Why why that one maybe has got a little more impact for sure.

So so the few (41:53) things there. One, by the way, I should note to my knowledge, at least the Equifax data has not (41:57) shown up in the dark web. So as much as I say, it’s out there.

Oh, great. Right. There is a (42:01) data points are out there, but not necessarily from the from that origination.

That’s right. (42:06) The Equifax breach. That’s right.

So a lot of these breaches, the bigger ones we hear about (42:09) end up where, yes, some of those end up in dark web. A lot of those end up just (42:12) sort of like, you know, high end art. It disappears.

Right. And no one knows where it (42:17) is. The supposition is those are nation state actors that would use that information.

Right. (42:21) For whatever purposes or whatever time. Right.

They’re out there. But with the most recent (42:26) large data breach and there’s there’s ones all the time, sadly. Right.

I think the issue (42:31) that continues is it adds to the file, so to speak, for targeting. So, you know, we all assume (42:37) that whatever we Google about ourselves is all that there is. The challenge is there’s always a (42:42) dark web file on all of us.

Right. That’s there. And it just gets more and more robust as time (42:47) goes on.

There are some people who don’t have one. It’s rare. But that that large breach fills (42:52) in a lot of blanks.

Right. For those files. And that’s the issue is, again, it’s sort of like (42:57) we talk about China.

Right. And their system of having a file on every citizen. And, you know, (43:02) maybe you can look at points in the U.S. history where, yeah, there’s a version of a file, (43:06) but means we all have a we all have a dark web file, too.

We’ve got to worry about. For sure. (43:10) Yeah, that’s interesting.

That’s making me think about my relationship with my (43:13) chat GPT right now. Yes. Right.

Which is my son got into it the other day. He’s he’s a early teen (43:22) and he said, roast me as me. He says, yeah, the things that it had from its memory (43:30) in its recollection and ability to use against me, you know, in a roast.

Yes. Was ridiculous. (43:37) It really made me think to myself, this thing knows this is remembered way more than I would (43:42) have even thought that I had told it.

Yes. Yes, very much. So this in many ways goes back to us (43:48) being more aware of our privacy settings, but also, of course, working with organizations and (43:53) brands that are trying to protect us as well.

Right now, it’s in chat GPT open eyes, interest (43:57) to get everything they can learn as much as they can improve their systems as much as they can. (44:01) But there are settings, right? You can adjust the settings at GPT and other tools. And most people (44:06) don’t know that.

Why? Because they’re not front and center. They don’t have time for it. They’re (44:09) just trying it out.

But that’s the thing we always say is whether it’s chat GPT or any other (44:14) right, LLM, even PayPal and Venmo, right? Just look for the privacy settings and turn off as (44:19) much as you can. Yeah. Yeah.

Great advice. You had told me that there was quite a boost (44:27) during the beginning, but twelve, fourteen, whatever, how many days ago it was with the (44:34) Israeli strikes on to Iran. That’s right.

Yes. You got an influx of of calls or business or (44:41) however it’s transacted. And then, of course, right after that, with the (44:45) Minnesota lawmaker who was who was attacked in their home.

That’s right. The Minnesota lawmaker (44:51) makes a lot of sense to me, right? Because it would be a high profile person. You know, (44:56) Hush would be one of those perfect platforms to read the read the web of data about that person.

(45:02) But why? Why with the strikes? What happened there? I’ve been hearing a lot lately about the (45:07) knock when an employee knocks on your office door and says, got a minute, and you immediately (45:11) know it’s some sort of incident. But Oxium IT can help whether you’re having a problem, (45:16) need consulting, an upgrade or a managed IT approach. They focus on preventing cyber attacks (45:22) and proactive solutions that deliver results.

My friend Matt Lauria and everyone at Oxium are (45:28) ready to help before or after you get the knock. Visit Oxium.com and let Oxium IT help you outsmart (45:36) chaos. Sure.

So so with the strikes, you know, the news broke and also the same time the (45:45) Israeli government closed many of the embassies and consulates around the world. (45:47) Right. Anticipating, you know, blowback or attacks at those locations.

Sadly, there was (45:53) the shooting and the murder of two embassy employees in D.C. Right. That happened as well. (45:59) So putting this and stringing it all together.

Well, and if, you know, someone who’s intent on (46:04) doing harm to Jews or Israeli can’t get at someone at an embassy, well, then maybe it’s other (46:08) locations, maybe it’s other sites, maybe it’s someone’s home. Right. And so, you know, even if (46:14) we go back to some of the protests about what’s happening in Gaza and Israel and so forth right (46:20) now, I’m always careful to use the right language there.

There have been people whose homes have (46:26) been ransacked even here in Michigan, actually. I shouldn’t say ransacked, who have been vandalized (46:30) right as well. There are a number of stories about protesters showing up at home, you know, (46:35) putting bloody dowels.

So dolls actually on the lawn. I mean, all kinds of things that have (46:40) happened there. And so for a lot of people in the Jewish community, there’s a concern that as a (46:45) result of that turning up the even more right that attacks may come to their house, to their place of (46:50) work and so forth.

So that was one of the reasons for the influx. At the same time, we’ve received (46:55) just those same calls from folks who are Muslim from the Middle East, because, again, they are(46:59) concerned about the same types of issues. And again, this is where it’s not talked about at (47:03) cocktail parties, but, you know, I could probably spend a whole hour giving you stories of people (47:09) being threatened, harassed online, having packages delivered to their home and newer sent to their (47:14) house because of an issue that they supported or didn’t support from one post, right? One video (47:20) online and so forth.

And, you know, as we say in our business, everyone reacts to online harassment (47:26) differently. So if we were to sort of, you know, look at what’s most likely to least likely, (47:31) right? Least likely someone graduated, right? Yes. Yeah.

Least likely someone showing up to (47:35) someone’s house, right? Catastrophic outcome, as was seen, sadly, with the Minnesota lawmakers, (47:40) but pretty unlikely to occur. But you’re seeing the reaction to that. And now state legislators (47:46) amping up privacy protections.

Most likely it’s threats, right? It’s death threats, (47:51) violent threats sent to someone by email or posted on a social media site. And so after (47:57) the UHC murder, which was the CEO, right? Of course it was very public. People are broadly (48:03) taking those threats a lot more seriously, right? And so knowing that’s the case, people react very (48:09) differently to threats.

And here’s what I mean by that. You might have a, you know, person who (48:13) works at a company, their company is taking a stand on an issue, whether it’s Gaza, sending (48:17) relief, whatever it may be. And they get blowback and they get threats.

And that leader, (48:22) that CEO business owner might get a threat. And that may be against their house. Maybe someone (48:26) throws in there, Hey, here’s your address.

Speaking of another example, you know, the other (48:29) day, you know, we know a story where someone had someone who was angry with this executive and (48:35) sent a photograph of their house to the executive, right? Just say, Hey, I know where you are. Right. (48:41) I mean, just like out of the movies, isn’t that? I mean, (48:43) but it happens.

It’s a very toxic environment these days. Right. And so again, you know, (48:50) the person who receives it might be okay.

Their, their spouse may say, I’m sorry, we got to move. (48:55) Right. I’m not okay with that level of risk.

And so this is where the family dynamics really (48:59) kick in. You remember this with Dan Campbell, right. And the lions here where, Oh yeah.

Right. (49:03) It was made public where he lived and they were gone. Right.

So, and again, different profile, (49:08) different reasons and so forth. Hopefully he wasn’t facing any, I don’t know any direct threats, (49:13) but people react very differently to concerns about violence than they might think. (49:17) Yeah.

Yeah, that’s true. There’s a, there’s a statement. So I’m going to kind of pull a couple (49:23) of things together here for you.

There’s a statement that, that always resonates with (49:27) me when I’m sitting down with someone, which is you are uniquely qualified to be exactly where (49:32) you are right now. And I think that that really stands true for you. And when I think about this (49:38) for you, I think there’s a, there’s a couple of different places of, of how that how that fits.

(49:45) And one is the, the timing, right? The timing in your career because of the people that you’re (49:51) working with, you’ve had to establish yourself as someone with a track record of keeping his (49:57) mouth shut. Yes. You know, who’s, who’s, who’s keeping things private all the while while still (50:05) maintaining normal relationships, right? I mean, I’ve never found our conversations to be (50:10) too odd because you had to withhold too much information or anything like that.

But, (50:14) you know, obviously to, to get the funding for a company like this, to start a company like this, (50:19) and to also have the, um, the people trusting you to, to be the guy who’s keeping this stuff (50:26) quiet and squashing it and finding it, right. Cause you may find, you may dig up something else (50:30) that someone didn’t even know was out there. And you’re, you’re squashing that or hushing that, (50:35) as I would say.

Um, but then you also told me that you’re about one and a half years early (50:41) on kind of where you are in terms of people adopting what you’re doing. So can you, can you, (50:46) can you take what I just said, that big, that big pile and kind of digest it and, uh, and (50:53) tell me what you think about it. So I think you’re, you’re spot on.

Uh, it’s been an interesting (50:57) journey, right? For sure. I guess I’ve done a lot. That’s why I have, I’m part of the no, (51:00) no hair club, right? We do this for fashion reasons.

I forgot to tell everyone that it’s, (51:05) it’s not genetics or anything like that. Yeah. Efficiency, right? Efficiencies.

Yeah. Yes. Um, (51:12) it’s come a long way.

I think there is absolutely, you know, I remember back in the WealthX days (51:16) where early on we started and we felt kind of weird, right? We’re like the spies who are out (51:20) here looking at information about people. And yes, we’re holding things back, but eventually we (51:25) started to, I remember from our, for early days, we’d go into a private bank and we talk about the (51:30) solution. And then we’d figure out that the head of the private bank, who’s usually very well (51:34) connected.

And sometimes it’s from a very substantial family by name and background (51:37) is in our database. And so they’d say, well, show me my profile. Right.

And then we’d have to, (51:43) again, always ethically the right way, show them that profile, walk them through. (51:47) And then we started picking up, uh, business owners, uh, as clients as well. Right.

So, (51:52) you know, in the WealthX days it was, yeah, here’s a dossier, but actually I’m going to buy, (51:56) you know, WealthX, I want to see what you have about me and my family. And by the way, (52:00) I’ve got a son or a daughter getting married. I want to see what you have on the other family, (52:04) right.

As well. And again, who might be ultra high net worth in the case. And so we started (52:08) to build relationships with those families and we’re in the middle of this, which was, (52:12) uh, incredibly insightful, I will say, but also a lot of fun.

So I think that’s where the journey (52:16) started being the real sort of trusted advisor in the middle of all of this. At Hush, it’s only (52:21) been amplified, right? So the, the challenges are, I mean, we see everything, we see everything (52:25) from deep pig, pornography, death threats, stalking. I mean, you name the issue.

And usually (52:31) when families are at, uh, some of their worst times, right. Uh, where there’s an active situation (52:36) and an active issue, and we’ve got to engage to get whatever it is, right. Taking off the internet (52:40) as, as fast as possible.

So I, you know, I think that’s, that’s worked well, uh, for us. (52:47) The timing is such where, you know, I remember Apple putting on billboards two years ago, (52:52) you know, privacy by design. Right.

And I think they sort of started the consumer consciousness of (52:59) privacy and what does that mean? And do I really care about that now they’ve made it a effective (53:04) marketing ploy. And I say a ploy, I think the reality is they are more private on, on average (53:09) in their tool set and how they operate. And so, you know, I don’t know if you’ve ever gone Apple (53:13) pay and you can use a virtual email as opposed to your regular email.

So it’s getting there, (53:17) but then these other events have picked up, right. And this is the sad part, but it’s also (53:21) been good for our businesses, the UH CEO, right. Uh, you know, murder, um, all of these things that (53:28) have happened, including the Minnesota lawmakers, two of which were, were, uh, killed, uh, and (53:33) related as well as the two who were injured.

Um, it’s all in a place where we, we just see these (53:38) trend lines. Um, and even if we take that in the zoom in at a state level, like I said, (53:44) university of Michigan here, this has been going on for a year, right. Where the regions have been (53:48) having issues around their own safety and security.

You can go on a list of different orgs, different (53:52) people. And even when you go to a cocktail party, right. And this is what we do.

Oftentimes we’re (53:56) the catalyst around a conversation. So, you know, we try not to be doom and gloom, but you know, (54:01) we open the dialogue and then people start sharing, right. And they say, oh yeah, I, you know, (54:05) had a stalker, right.

So I’ll give you a crazy stat. One out of six Americans (54:09) is stalked in their lifetime, right? Wow. Half of that before the age of 25.

(54:13) So if you talk to a room of 15, 20 people, you’re going to have one person who’s gotten stalked, (54:18) right. And then one person who will know if someone who has been stalked and they’ll say, (54:21) oh yeah, this is absolutely crazy. Like, you know, here’s how we dealt with it.

And no one will know, (54:25) right. One last story and I’ll, you know, pause. The stories are what make the difference, (54:29) to be honest.

I mean, the, when, when you just talk in platitudes of, you know, (54:33) be more aware of your cybersecurity or, you know, things like that, or, or, you know, (54:36) everyone’s at risk or it’s not, if it’s when those are all been muted, nobody’s, nobody’s (54:44) listening to them. The only thing that works is the actual story and story of somebody that (54:48) maybe they know. Yes.

Agreed. And you look, I guess I consider myself and ourselves fortunate (54:54) to share these stories because they’re also the kind of stories that people want to, they don’t (54:58) want to share themselves, right. They don’t want to be on video recorded and, you know, (55:01) telling the story about some of their worst times, but it’s true.

So I’ll, I’ll mention this one. (55:06) There’s an individual, you know, relating to sort of election season and the rest, (55:12) you know, has an opportunity to publicly say his piece, right. For no more than two minutes worth, (55:18) right.

Seats his opinion, open mic, that moment goes viral, right. Because, you know, things do (55:24) sometimes, it blows up. Next thing you know, he’s getting shipments to his house of manure,(55:30) of glitter bombs.

He’s getting death threats at the house. His spouse is getting called at her (55:35) office with death threats as well. And people are trying to harass her out of her job.

(55:39) And again, this is two minutes of, you know, public comments, right. Yeah. And so street type (55:44) of thing it is.

And so people just, and so the reason I tell you the story is I was at an event (55:49) where this individual, you know, when I described what I do was happy to share, right. What had (55:53) happened to him and he’s surrounded by his close friends and none of them knew that had happened. (55:56) Right.

And so it’s, again, it’s not the kind of thing people talk about at cocktail parties, (56:01) but it happens all the time. Wow. Wow.

Let’s also talk about the, so, so when you said the one (56:08) point, the one and a half years early, that was something you told me, right? Yes. Talk about what (56:13) that, what that meant to you. So, you know, we like to think that we try to capture the zeitgeist (56:19) of the moment, right? Like what, where are we and what’s going on? There’s so much going on these (56:23) days, right.

But relative to privacy and personal safety and security, right. Where are we on that (56:29) journey? And yeah, we probably started a year and a half, even two years early. I’d like to think(56:33) we got the technology ready so that we’re, we’re, you know, now built for this moment and we are.

(56:39) But, you know, again, it’s timing’s hard, right. With startups and tech, (56:42) I was going to say, isn’t that just the technology life cycle, right. You see this, (56:46) these early adopters, right.

These are people who can afford this. These are people who it affects (56:50) most, most regularly. It’s really affecting everyone, but we’ve got to get it to where the (56:55) cost can be appropriate for everyone.

And then also just that the platform is scalable to where (57:00) it can serve everyone. And that’s what you guys are, that’s what you guys are building, right? (57:03) That’s right. And that’s, that’s where we are, which is fantastic.

But again, it’s sort of, (57:06) what, you know, when does it become a consumer trend or consumer fad where people are just, (57:11) Hey, I’ve got to figure something out about my privacy and security, right. You know, (57:15) I Google myself and I did it last week or last month because someone said I should, (57:18) I don’t like what I see. We’re getting to that place where more and more people are aware (57:23) because of these incidents that have gone national.

Well, I mean, I think it’s interesting (57:26) too. I mean, when you think about when I think about just myself, right. I mean, I’ve got a (57:30) pretty heavy online presence.

I’m pretty forward about this is what I’m up to these days. And I (57:38) have to say every word that I’m saying to you, I’m almost pausing and wondering, you know, (57:42) what, what should I be doing on that? And so, and I don’t think I’m the rarity, right. I think (57:46) there’s many business leaders who are doing a podcast, doing other video work, doing a lot (57:52) of social media, doing a lot of outreach, which is part of the scalability of their own organization.

(57:57) Right. So we can’t stop. That’s right.

So, so is this, is this for someone like me as well? (58:02) It is. You know, we often say again, platitudes, right. Don’t overshare, (58:07) right.

Holding information back. And that’s, that’s true because it’s easy to connect the (58:10) dots. Right.

So yeah, I’ve done this. I’m very practiced at what I share and what I don’t share (58:14) and the rest, but I think we’re getting to a place where generally speaking, people will share (58:19) a lot less. You know, for example, I’m thinking about another story recently.

This was in the news (58:25) just in the last week where someone showed up at an individual’s home trying to break in, (58:31) you know, two in the morning. And it seems as if it is a all indications, a relative of a (58:38) disgruntled employee. Oh, right.

Okay. And so, you know, we think about the person who owns, (58:43) you know, 25 humming trucks. Yeah.

You might want to be very well aware of your, (58:47) at least your home address, right. Because even in cases where, you know, you think, (58:51) for example, law enforcement and folks who are very well able of taking care of themselves, (58:55) they’re not always there, right. They’re not always there in the middle of the night or (58:58) they’re not always there in the, you know, afterschool pickup.

And so that’s where this (59:02) stuff matters. Yeah. Well, I mean, boy, that’s bringing to mind the whole, the Minnesota lawmaker.

(59:06) I mean, they kind of walked through on one of the articles of the details of how that attacker (59:14) came at them. And it was essentially, I don’t care if she would have had a gun in her hand, (59:20) she had no chance. Yes.

Yes. So, you know, sometimes the biggest prevention could have been (59:26) just not having that address. That’s right.

Yes. Very much. Take yourself off the board.

(59:30) Yeah. That’s the, I mean, I hate to say it, this is this and this is the sad part, (59:33) but it is indicative in that example, there was a list. Of course.

Right. And if one target was (59:39) too hard to deal with, he would have gone to the next one. Yeah.

That’s right. The thing I also (59:43) thought was interesting on that was, you know, they showed a picture of the person’s home and (59:49) I’ll use the word modest, not in any derogatory way. It was just a modest home.

It was just a (59:54) normal American home. And it really made me think, wow, sometimes we think when we hear these names (59:59) on the TV or radio or internet or whatever, just through the media, you know, you’re hearing about (1:00:05) this lawmaker, you think that they’re in this ivory tower type of setting. No, they’re just in the (1:00:11) average American neighborhood sometimes.

Very much. Right. Especially at state elected official (1:00:15) level.

Yes. Very much. And then even the highest profile folks like the UHC CEO, he’s just walking(1:00:23) with a Starbucks in his hand outside of the hotel.

That’s right. I mean, he’s got to walk to the car. (1:00:27) Yes.

Yes. So there’s a softness to all of us as targets for sure. Very much.

(1:00:36) I noticed also when I was doing some prep work for our conversation was just going out and (1:00:45) Googling you. OK. And you have your own website.

Yes. OK. Is that on purpose to kind of control (1:00:51) messaging and to control that information flow? So there’s so it’s less likely that someone (1:00:58) takes liberties to to maybe add or delete from that.

So, yes, that’s part of it. One of it was (1:01:03) to own the domains. I could, of course, not put a website out there, but actually own the names.

(1:01:08) It’s funny how, you know, again, not even sort of big name CEO, but moderate, even small profile. (1:01:15) I consider myself small profile. People will take, you know, Nicholas Rambis sucks dot com.

(1:01:22) And and try to use that or post whatever they’d like to. If I make one, it’ll be (1:01:26) Nicholas Rambis is decent. Yeah.

OK. Thank you. So I’ll go with something a little more.

Yes. (1:01:30) More friendly. Yes.

Thank you. But but, you know, you got to own those domains. You got to see (1:01:35) where your digital footprint is.

And, you know, a lot of people talk about search engine suppression, (1:01:40) right, where thankfully, to my knowledge, at least there’s no negative articles out there, (1:01:43) but you don’t want the first things to be something that would index very high, (1:01:46) which is your own name with content that you didn’t control. Sure. So, yes, (1:01:50) that’s one of the reasons that’s part of the by design.

Absolutely like that. (1:01:54) Let’s get a little silly with that, though. Is there one embarrassing data artifact out there (1:01:59) that I hope not? Yeah.

You know, I had a Tumblr account once, which you with a Mohawk or something (1:02:04) like, oh, no, thankfully there’s a high top fade somewhere. Yeah, absolutely. But but no, (1:02:10) I can’t think of one.

Social media has been shut down for a long time. Right. So.

So, (1:02:15) no, not much. Not much. Yeah.

I mean, there probably is on some bulletin board system from (1:02:19) nineteen ninety nine. OK, good. But you’re willing to live with that.

It’s the sins of (1:02:23) the past that people will look past. That’s right. You also you live in the area here, (1:02:34) but not not not exceptionally close to where we are here.

But you do love (1:02:38) a certain bakery. This is near my office. What other what other kind of Michigan or just home (1:02:47) based sort of stuff do you really enjoy that you’re willing to share? Of course, I’m happy (1:02:52) to.

So there’s a I’m a big supporter of small business and local business. I’d be remiss if (1:02:56) I didn’t mention Buddy’s Pizza first. But of course, I imagine most listeners will know that(1:03:00) and have spent many a game night right at Buddy’s Pizza.

But that’s one. We’ve got some some great (1:03:07) down the street. There’s a cheese lady you’ve ever been.

Right. So fantastic business as well. (1:03:12) There’s so many right to point to.

Yeah. Yeah. Yeah.

But you’re you’re fond of the (1:03:16) the niche sort of very much, very much. Yeah. If you can find that right thing, (1:03:20) all the all the better.

Yes. Love it. Yeah.

Love it. What else? What do you think? (1:03:27) In just general advice doesn’t necessarily have to be security, but with our audience of being (1:03:32) business leaders, what would you give us as a as a takeaway? You know, if you could get right in (1:03:38) their face and tell them or do this one thing based on everything I know, what would you tell (1:03:43) them? So I would tell them to a few things. One, go back and run the playbook from 1999, 2000, (1:03:49) right, where at that point, people were scrounging for somebody who knows something about this thing (1:03:53) called the Internet.

Right. Go hire your teenage kid. Go hire a college student.

Do the same thing (1:03:58) for AI. Right. AI is transforming every business.

It’s hard to keep up. Right. For those who even (1:04:03) who are in the industry.

And so get that person and figure out what it’s going to mean for your (1:04:07) business. You’re not left behind. Right.

You know, we have such an opportunity with this technology. (1:04:14) This is a bit of my, you know, sort of, you know, U.S. cap on. Right.

As a nation, (1:04:18) we have a great opportunity to leverage this and make that much more productive gains in our (1:04:23) economy and to reinvent the economy in other ways. Right. I’m very excited about reindustrialize.

(1:04:28) That’s a big conference that’s coming to Detroit, coming up soon. So figure out AI,(1:04:34) figure out how to leverage it for your business and begin to pull it. Probably doesn’t have to (1:04:37) be able to pull someone who’s, you know, new and doesn’t have the baggage of a certain way of doing (1:04:42) something to look at your business in a different way and bring them to the fore.

Right. So that’s (1:04:47) one. Right.

Two is, look, we live in a complicated, messy time. Right. So, you know, know what’s out(1:04:54) there about yourself and your family.

You know, we say often, look, even if you don’t use Hush or (1:04:59) whatever, at least Google yourself, just know what’s there, because whether you’re a business (1:05:03) owner and you one day might sell your company, you know, you’re worried about the competitors, (1:05:08) they’re doing the same thing. Right. They’re doing all the research, all the homework.

So just (1:05:11) awareness is the first step in that regard. And if they want to go further than great. Right.

(1:05:15) Those are the two things I’d share with with listeners, mostly. That’s great. Yeah.

You know, (1:05:19) and to buttress that a little bit, I mean, I continue to go with anyone younger than me. (1:05:24) It’s like good, clean living pays off long term. Oh, yeah.

Because it’s the best thing is to not (1:05:30) have any of that negative, those negative remarks, that negative news very much about you in the, (1:05:34) you know, in the in the ongoing media. Yes, I look at very much agreed. And look, I would like to (1:05:40) think that, you know, covid, which in some ways seems so long ago, taught us all hopefully some (1:05:46) good lessons about what’s most important.

Right. Family, friends and life is short. So hopefully (1:05:51) people remember that going forward.

And you’ll notice I didn’t ask you a whole lot of questions (1:05:54) about your family, because I know you’d be pretty hushed about that. Deflect those. Right.

Yes, (1:05:58) exactly. Well said. All right.

Well, thanks for being here, Mick. This was fantastic. Very timely.

(1:06:04) And and you’re a gem. So I thank you for being here. Likewise.

I’m lucky to be here. Pleasure. (1:06:08) Thank you.

Thank you. Appreciate it.