Speaker 1: People should assume and fortunately like, assume something's going to
Speaker 1: go wrong and kind of work backwards from there. Right,
Speaker 1: you know, assume that some emergency be like, Okay, thank
Speaker 1: you very much, I'm going to call you back.
Speaker 2: Right.
Speaker 1: Your bank will never call you. Right, your bank calls you,
Speaker 1: hang up the phone, look in the back of your card,
Speaker 1: call the bank. Right. If you think something's a scam,
Speaker 1: probably is.
Speaker 3: I'm Laur Siegel and you're listening to Mostly Human, a
Speaker 3: tech podcast through a human lens. So Johnny, I'm so
Speaker 3: happy to have you here today. We had a brief
Speaker 3: conversation in Davos about the future of identity and fraud
Speaker 3: and it terrified me. But you are you are the
Speaker 3: person that people go to to talk about this, and
Speaker 3: you know a lot about the bad guys on the
Speaker 3: internet and what they're doing and how they're doing it.
Speaker 3: So without sounding like major CEO elevator pitch, which explain
Speaker 3: to us common folk what it is that you do.
Speaker 1: So, sir, Cure has built the largest identity verification, fra
Speaker 1: aull prediction and any money laundering platform in the world.
Speaker 1: So we build AI on offense to enable enterprises fintech
Speaker 1: financial services insurance, gaming, governments, marketplaces really give them the
Speaker 1: tools and algorithms to be able to combat folks that
Speaker 1: are committing scams, stealing identities, perpetuating different types of fraud.
Speaker 1: And so we've been at it for a little over
Speaker 1: ten years, so we've been studying these problems pretty intimately
Speaker 1: and have I think a pretty unique seat at the
Speaker 1: table at onboarding, at log in and account takeover and
Speaker 1: money movement, call center, digital in branch. We see all
Speaker 1: different types of attacks, and yeah, I'm excited to be
Speaker 1: with you today and share a little bit about what
Speaker 1: we see in the world.
Speaker 3: I am curious you mentioned kind of for offense. We
Speaker 3: hear over and over a lot of tech folks say,
Speaker 3: okay AI is going to counter AI. AI can do
Speaker 3: a lot of bad, but it can also do a
Speaker 3: lot of good. So talk about how you're using machine
Speaker 3: learning and artificial intelligence to catch to prevent fraud and
Speaker 3: why that's a lot better than the old fashioned methods.
Speaker 4: Can explain it to us.
Speaker 3: I would say, like, we are, you know, not in
Speaker 3: the room at at the company.
Speaker 1: With a lot of security. So I know you're not
Speaker 1: in the room at the company.
Speaker 3: That's do you have a I guess I imagine you
Speaker 3: would have a lot of security given what it is
Speaker 3: that you do.
Speaker 1: I was a little I was a little tardy to
Speaker 1: this our start here because I was on the phone
Speaker 1: with our c sale going through some more recent changes
Speaker 1: that we're making. But yeah, it's a it's pretty lock
Speaker 1: pretty locked down.
Speaker 3: Are people trying to I mean because of what you do?
Speaker 4: And I promise I.
Speaker 3: Won't go on this whole thing, but it is fascinating
Speaker 3: because you you literally protect some of the largest companies
Speaker 3: from fraud, uh and.
Speaker 4: All sorts of attacks. Like do people try to attack you? Guys?
Speaker 1: I think most companies that have something of value are
Speaker 1: under attack all the time. Fraudsters we often say they're
Speaker 1: they're like water. They find the path of the least resistance.
Speaker 1: They're very good at like testing weak points, right, they
Speaker 1: just will perpetually test and test and tests, try to
Speaker 1: find policy policy gaps, try to find weak employees. You know,
Speaker 1: it's unbelievable, you know, Laurie, if you're just started so
Speaker 1: cure tomorrow, like you'd immediately be attacked with gift cards,
Speaker 1: CEO requests, inbounds, phishing attacks. They're very, very, very good
Speaker 1: at like saying like, Okay, we see someone on how
Speaker 1: to LinkedIn change boom attack, it's it's it's remarkable, but
Speaker 1: so anyway, so on the solution set, so we do
Speaker 1: lots of red teaming. So part of the a on
Speaker 1: offense is we create a lot of synthetic identities ourselves.
Speaker 1: We mature a lot of synthetic identities ourselves. We're trying
Speaker 1: to build and create synthetic identities that kind of permeate
Speaker 1: throughout the organ kind of ecosystem. We are creating deep
Speaker 1: fakes ourselves. We are running injection attacks. We have an
Speaker 1: internal red team that basically is perpetually trying to figure
Speaker 1: out how to beat our own systems. We also use
Speaker 1: multiple external teams to simulate similar types of attacks. They
Speaker 1: go create fake identities, manipulated identities, They go higher forgers
Speaker 1: to go actual go build these fake documents themselves. It's
Speaker 1: a perpetual game of trying to stay two or three
Speaker 1: or four steps ahead in terms of how we are
Speaker 1: kind of dog fooding our own capabilities.
Speaker 4: It has to be.
Speaker 3: Every day is kind of its own little adventure where
Speaker 3: you are. Could you take us into your day to
Speaker 3: day what types of customers do you have, and what
Speaker 3: kind of threats are you seeing. Obviously they've evolved over
Speaker 3: the last years. But take us behind these very locked
Speaker 3: and secured doors and tell us what you're seeing.
Speaker 1: All customers that move money, right, or something of value
Speaker 1: or under attack all the time. Fake identities, stolen identities,
Speaker 1: manipulated identities, synthetic identities are being used to try to
Speaker 1: open new accounts, new bank accounts, new investment accounts, anything
Speaker 1: that can be used to like move money. It could
Speaker 1: be folks that are perpetuating promo abuse. Set one of
Speaker 1: the sportsbook or prediction markets, right, it could be one
Speaker 1: of the e commerce and marketplaces that are like get
Speaker 1: one thousand dollars, Laurie, if you want to become a
Speaker 1: new driver, sign up for this new you know, e
Speaker 1: commerce subscription, you know, get your first six months free.
Speaker 1: Any of those things. Fraudsters are trying to steal that
Speaker 1: thing of value, right, Give twenty bucks, Laurie. If you
Speaker 1: recommend a friend, right, well, if that fake account then
Speaker 1: goes and recommends fifty fake friends, right, there's may one
Speaker 1: thousand bucks. And so you have. All of this is
Speaker 1: like a game of kind of cat and mouth all
Speaker 1: the time. So i'd say, like I think to maybe listeners,
Speaker 1: there's just a tax going on all the time. Fraud
Speaker 1: rings in mass are hitting most of the consumer applications
Speaker 1: that that you deal with. Maybe one of the interesting
Speaker 1: things that has been more recent i'd say over the
Speaker 1: last kind of twelve months, has been the North Korean
Speaker 1: you know it worker attack. That has been something that
Speaker 1: we've seen a huge prolific kind of acceleration of in
Speaker 1: terms of employment. We are seeing a like enormous some cases,
Speaker 1: like eighty ninety percent of all job applications are fake
Speaker 1: or fraudulent organized crime ry.
Speaker 3: Say that again, I mean, what do you mean like
Speaker 3: coming into wear remote jobs people. So you're if I'm
Speaker 3: understanding crack and please correct me if I'm wrong. So
Speaker 3: you're saying this is a new deep fake type of
Speaker 3: scam where you would have someone from North Korea trying
Speaker 3: to apply for a job here, say in the United States,
Speaker 3: taking on the identity of is taking on a synthetic identity,
Speaker 3: so a completely different identity using deep fakes?
Speaker 4: Is that correct? Am I understanding that?
Speaker 1: Yeah? North Crared is estimated about six thousand workers are
Speaker 1: operating inside US technology companies right now under fake or
Speaker 1: synthetic identities. Now, the synthetic identities can be you know,
Speaker 1: it can be a manipulated version of LORI like on
Speaker 1: a resume, it could be a deep fake of LORI,
Speaker 1: right that's controlled by someone else. It could be any
Speaker 1: combination of these things. Because you get paid, You get
Speaker 1: a paycheck every two weeks, right, and you can use
Speaker 1: AI to do a lot of these jobs, like certainly
Speaker 1: lower level jobs. Many cases, folks are obviously trying to
Speaker 1: get access to trade secrets and IPE and in many cases,
Speaker 1: you know, folks are trying to get access to to
Speaker 1: code baits or create back doors for some of these
Speaker 1: foreign regimes. And so yeah, I mean, unfortunately, the money
Speaker 1: then goes back to some of these foreign adversaries that
Speaker 1: are funding weapon programs, that are funding wars, that are
Speaker 1: funding obviously folks that are not super friendly to the
Speaker 1: Western world. And we're really seeing it across the board.
Speaker 1: I mean, there's been some public attacks with some of
Speaker 1: the large crypto exchanges, but we've seen infiltration to some
Speaker 1: of the major banks, We've seen infiltration to some of
Speaker 1: the support centers, and then certainly really widespread the number
Speaker 1: of fake job applicants that everyone is seeing is growing
Speaker 1: very very very fast.
Speaker 3: You know, it is I keep hearing over and over
Speaker 3: again workplace job applicants fraud. So it certainly seems like
Speaker 3: this is something that's just like as you're saying, over
Speaker 3: the last year.
Speaker 4: So it's just becoming a completely new attack.
Speaker 3: Walk us through how companies can even look out for that,
Speaker 3: how how people even know and is this just fake
Speaker 3: applicants or is it actually deep fakes as in I'm
Speaker 3: interviewing for a job, but it's not really me. I
Speaker 3: have it's a video, a deep fake video. I mean,
Speaker 3: can you explain kind of very in a granular way
Speaker 3: what that looks like and how that plays out, especially
Speaker 3: are these remote jobs.
Speaker 1: Yeah, you know, I'll kind of frame it because it's
Speaker 1: it's a similar attack pattern across all of these LORII. Right,
Speaker 1: So it's a you know, if you're applying for a job,
Speaker 1: you submit, you know, information on your resume. You likely
Speaker 1: had the resume auto complete the job application. Your resume
Speaker 1: likely is even fake, right, the PII is manipulated that
Speaker 1: the email doesn't really belong to Laurie or is brand new.
Speaker 1: It's you know, on the resume, it's like, hey, Laurie,
Speaker 1: somehow worked at Gemini and open AI and Anthropic in
Speaker 1: the last five years.
Speaker 4: Busy, they offer really good they're all trying to poach.
Speaker 1: It could happen, so all the best resumes, like you
Speaker 1: see these pristine resumes and they're fake. All like everyone
Speaker 1: that's a fake applicant happens to have an IQ of
Speaker 1: like one hundred and fifty and you know has had
Speaker 1: that many of the best jobs in the world. But
Speaker 1: so you know, again, I think whether it's a fintech application,
Speaker 1: or gaming application or unemployment application, you see a lot
Speaker 1: of the same patterns. There's geospoofing, so it's like Louri's
Speaker 1: in New York now of a sudden, Laurie's in Vancouver.
Speaker 1: You see emails that are new, you see phones that
Speaker 1: are manipulated, lots of m v and o's, You see
Speaker 1: domain email domains that are higher risk domains. You see
Speaker 1: fronts taking over entire domains. And then you know again
Speaker 1: as people are kind of going through the process, documents
Speaker 1: are fake. Documents are manipulated, right, maybe they're injecting a
Speaker 1: deep fake image, and so you're picking up lots of
Speaker 1: little patterns. So like all of our hands like have
Speaker 1: a little bit of a micro shake, right, and so
Speaker 1: you can actually pick up that in an accelerometer of
Speaker 1: a phone, so you can actually determine if a phone
Speaker 1: is being held. You can look at like the gimbal
Speaker 1: of a of a camera to actually determine the angle.
Speaker 1: And so does the angle at that moment match the
Speaker 1: actual video file that's being submitted, And so even in
Speaker 1: session you could see these like little little shifts that
Speaker 1: could determine is there actually a manipulation of the video
Speaker 1: or image. And so it's again it really becomes a
Speaker 1: kind of a combination of all these things, whether it's
Speaker 1: a job application, a sportsbook application, you know, if someone
Speaker 1: filing their taxes, someone registering to vote, you know, any
Speaker 1: of these become you know, attack vectors where you know,
Speaker 1: these types of kind of patterns can be used to
Speaker 1: you know, unfortunately hide nefarious or in many cases like
Speaker 1: criminal activity that we're having to counteract.
Speaker 3: I mean, and you have to get more and more
Speaker 3: sophisticated as the attacks get more sophisticated. I want to
Speaker 3: paraphrase something you said a little while ago. You said,
Speaker 3: to tell a live human from a video feed was
Speaker 3: visible to the naked eye just two years ago, but
Speaker 3: it's no longer detectable that way, and establishing trust between
Speaker 3: strangers transacting online might be one of the most defining
Speaker 3: conversations in the next couple of years. It used to
Speaker 3: be in tech that data was gold.
Speaker 4: Is trust the new gold.
Speaker 1: You need a lot of data to determine trust. And
Speaker 1: that's where the machines actually can reason over way more
Speaker 1: information than our human eyes can. Right in many of
Speaker 1: these sessions, we have thirty forty to fifty models LORI
Speaker 1: running to try to have the machines determine whether they
Speaker 1: should trust the session or not. And you know, you
Speaker 1: can be looking at thousands or tens of thousands of
Speaker 1: elements in milliseconds. Yeah, which which you know again goes
Speaker 1: back to this like AI fighting AI in a world
Speaker 1: where trust, yeah, on the internet is really hard to
Speaker 1: to determine.
Speaker 4: Can you just tell me?
Speaker 3: I know, you probably have to sign a lot of
Speaker 3: papers that make it so you can't say a lot
Speaker 3: about these types of things.
Speaker 4: But like, what's the craziest thing you've seen?
Speaker 3: Like you've just seen some insane things in your career.
Speaker 4: I imagine like.
Speaker 3: When you look back at the movie of your life,
Speaker 3: what is going to be like the plot point of
Speaker 3: you at this company building this and this thing shocked you, Like,
Speaker 3: what is that scene?
Speaker 1: I think, honestly, I think some of the like in
Speaker 1: person stuff ironically is the most brazen. Like what, Yeah,
Speaker 1: like we stopped someone a couple of weeks ago that
Speaker 1: I think they were in something like thirty nine different
Speaker 1: zip codes in like eight or nine different states, with
Speaker 1: like hundreds of different identity documents where this individual had
Speaker 1: been on all types of these bat like blocklists. So
Speaker 1: they're going into banks, getting stopped and then just going
Speaker 1: to another one. And so yeah, I think just the
Speaker 1: the brazen thiss that that we see and and and
Speaker 1: of course that's you know, that's that's in person, which
Speaker 1: I think is is is its own unique ye, but
Speaker 1: online like we've seen attacks where we will see you know,
Speaker 1: Laurie's face someone pretending to be LORI like thousands and
Speaker 1: thousands of times and a couple of like a couple
Speaker 1: of hour period across thirty forty fifty customers. Yeah. Again,
Speaker 1: I think that that this is the this is the
Speaker 1: fun cat and mouse game that we get to play
Speaker 1: all the time. And and uh yeah, I think just
Speaker 1: the sheer, the sheer sophistication of the automation and the
Speaker 1: number of different attacks people are to perpetuate today and
Speaker 1: the surface area on the Internet that they're able to, uh,
Speaker 1: you know permeate is is Yeah, to me one of
Speaker 1: the one of the most kind of fun parts of
Speaker 1: the job, because we're I often analogous our work to
Speaker 1: like special teams in sports. Uh, you know, no one,
Speaker 1: no one cares about the kickers until something goes wrong,
Speaker 1: and so you know, we we we very much are
Speaker 1: play and I think a really important special teams role
Speaker 1: and how the Internet operates right and how you know,
Speaker 1: many of these organizations you know, are are are able
Speaker 1: to provide on the other side of you know, a
Speaker 1: beautiful user experience to the good consumer.
Speaker 4: I'm speaking of sports references.
Speaker 3: You are probably one of the few tech CEOs that
Speaker 3: I've interviewed who has a background being an athlete of sourse.
Speaker 4: So, didn't I see.
Speaker 3: Something along the lines of you learned a very important
Speaker 3: lesson from totally screwing up a play in college football?
Speaker 1: Is that? Did I?
Speaker 4: I didn't make that up? That's real, right, Yeah.
Speaker 1: I think I was the only two sport athlete in
Speaker 1: the ACC at Boston College and played football and baseball,
Speaker 1: and yeah, I had an amazing, amazing experience. It's like, yeah,
Speaker 1: my my my very first punt of my very first
Speaker 1: game at BC, the kick returner was late coming on
Speaker 1: the field. I picked my head up in the snap
Speaker 1: hit me right in the chest. And so you know,
Speaker 1: as they say, you should only make some of these
Speaker 1: mistakes once in life. But yet I think a lot
Speaker 1: of the traits that you learn as an athlete, persistence
Speaker 1: and grit and hard work and delayed gratification and uh,
Speaker 1: you know, having a having a multi year plan have
Speaker 1: a Yeah, voted quite well in the transition into into
Speaker 1: this world.
Speaker 3: Can you give us an inside look at how a
Speaker 3: generative AI is reshaping fraud?
Speaker 1: Yeah, look, I think you know, we've been fortunate, as
Speaker 1: I was alluding to, to be a part of like
Speaker 1: a number of these these waves. But the fraud rings
Speaker 1: operate like a business. They have p and ls, they
Speaker 1: make technology investments, they look at their return and so
Speaker 1: as the cost of compute and the cost of AI. Right,
Speaker 1: certainly with even like open weight models, obviously many of
Speaker 1: the new Chinese models coming out, you have a tenth
Speaker 1: the twentieth to fiftieth of the price to be able
Speaker 1: to perpetuate and utilize some of these technologies. It's really
Speaker 1: a shift in terms of the economic incentives for fraudsters
Speaker 1: because it's been comes so much cheaper. So I think
Speaker 1: it's cheaper, you actually do more of it, right, And
Speaker 1: then a lot of this white collar digital crime does
Speaker 1: it end up getting prosecuted if you're selling heroin or
Speaker 1: cocaine or you know, obviously other stuff on the streets.
Speaker 1: You're actually seeing a lot of those criminals are moving
Speaker 1: into this type of like white collar digital crime. Right.
Speaker 1: You're seeing there's been good reports I think in Kingston
Speaker 1: and Jamaica a lot of the old call centers are
Speaker 1: now becoming scam centers, right, and you see a lot
Speaker 1: of this kind of conversion of like traditional stres crime
Speaker 1: is now moving to this type of crime because it's
Speaker 1: actually so easy. So like the tools have been made
Speaker 1: available to everyone. Right, you can obviously go and create
Speaker 1: many of these accounts for free. You don't even have
Speaker 1: to put in any amount of PII, and you have
Speaker 1: access to this supercomputer, right that can perpetuate the creation
Speaker 1: of synthetic identities. Right, I can say, hey, Lourie lives
Speaker 1: on the street, give me, give me you know a
Speaker 1: million fake identities that are all in this zip code,
Speaker 1: right make here's all these fake documents I have. And
Speaker 1: so the bearer the barrier to becoming a fraudster, right
Speaker 1: is no longer about capital, and in many cases it's
Speaker 1: not even about skill. And if you need the skills,
Speaker 1: you can go pay nine ninety nine on line in
Speaker 1: a YouTube video or Instagram or Telegram and they'll send
Speaker 1: you a book of how to go run you know,
Speaker 1: these scams or attacks, and so these like fraud as
Speaker 1: a service offerings and combination with AI and combination with
Speaker 1: the digitization of really everything in our life has created this,
Speaker 1: you know, effectively unlimited surface area of potential attacks. And
Speaker 1: so you know, as I said, frosters are just testing, testing, testing, testing,
Speaker 1: and it's an accelerating kind of rewriting of the rules
Speaker 1: of fraud because of the new technology capabilities.
Speaker 3: I just I really believed to some degree that way
Speaker 3: beyond the companies that you help, right, like these these
Speaker 3: multi billion dollar businesses that we all to some degree
Speaker 3: are going to be living fake lives on the Internet
Speaker 3: and we don't even realize it. And I came to
Speaker 3: this conclusion when I realized I had many fake lives
Speaker 3: on the Internet. And you know, you could say, well,
Speaker 3: you're real kind of a public figure, this or that.
Speaker 3: But as I started digging into my own face, I
Speaker 3: say fake lives. I found there was a Lori selling crypto,
Speaker 3: and then I connected that Lori selling crypto. It was
Speaker 3: a lot of my it was my identity, it was me.
Speaker 3: It was a whole fake profile that had the pictures
Speaker 3: of my wedding, this and that, plus a little bit
Speaker 3: of something else. And there, as I started digging, it
Speaker 3: was so far beyond fake Lori. There was fake this
Speaker 3: person and fake this person, all these fake people talking
Speaker 3: to each other.
Speaker 1: And then I.
Speaker 3: Started reverse imaging, you know, some of these images, and
Speaker 3: this person who was doing this is actually works at
Speaker 3: a police.
Speaker 4: Department in Ohio.
Speaker 3: And all of these normal people, these people who live
Speaker 3: in the real world, were part of this fake bubble,
Speaker 3: this fake life of fraud that they had no idea about.
Speaker 3: And so when I think about the future of AI
Speaker 3: and what you're just talking about, which is like the
Speaker 3: democratization of these tools that make it so easy at
Speaker 3: a time when our identities are out there for anyone,
Speaker 3: and now it's easy to do it. That's what I
Speaker 3: worry about, this idea of these fake lives and what
Speaker 3: will be you know, and the things that we don't
Speaker 3: even know about that us normal people out here don't
Speaker 3: even know about.
Speaker 1: Even the risks. I mean our team, there's like go
Speaker 1: and sourced tools, Laurie, like Kling, which is you know,
Speaker 1: there's been a lot of really good videos made on x.
Speaker 1: You know, my team was able to take a public
Speaker 1: image of me and with a free software in about
Speaker 1: twenty minutes go create me, replicating whatever it was they
Speaker 1: were doing. And so Laurie, if someone's trying to go
Speaker 1: create a fake crypto account with your image, you know,
Speaker 1: we're having to do a real time search. It's like, hey,
Speaker 1: is this an image that's a public image?
Speaker 2: You know.
Speaker 1: AI is just dramatically, you know, changed the cost and
Speaker 1: skill to commit fraud. There's obviously image and manipulation, there's
Speaker 1: voice clode manipulation. Yeah, it's it's it is really kind
Speaker 1: of changed the calculus in terms of you know, how
Speaker 1: do you trust how do you trust people on the Internet.
Speaker 1: And it's and it's hitting everything. It's hitting everything from
Speaker 1: the folks trying to implement age Assurance Right regulations, which
Speaker 1: has you know, been a sweeping change of the Internet
Speaker 1: obviously over the last twelve to eighteen months. But like
Speaker 1: twelve fourteen, sixteen year olds are crafty. They know how
Speaker 1: to create deep fakes too.
Speaker 3: Yeah, I'd love to play for you. I I recently,
Speaker 3: I get scammed. People try to scam me all the time.
Speaker 3: I think, just because I'm a human who lives in
Speaker 3: this world and is online. But I had someone call
Speaker 3: me recently who said that I got locked out of
Speaker 3: or said that someone was trying to hack into my
Speaker 3: Google account. And to give you a sense, like this
Speaker 3: guy sounded like pretty kind of normal and you know,
Speaker 3: and he had some good information, but you know, I
Speaker 3: guess like fifteen years of doing this, I started the
Speaker 3: alarm bells started going off, and so I just turned
Speaker 3: the tables on him and I and I just asked
Speaker 3: him about why he was doing this, you know, with
Speaker 3: the caveat that. I was like, you're pretty good at this,
Speaker 3: Like can you explain to me a little bit? And
Speaker 3: he did, And so I'd love to since we're you know,
Speaker 3: you talked to you understand threats at a much larger scale.
Speaker 3: But this is my kind of one little window into it.
Speaker 3: I'd love to play it for you and get your thoughts,
Speaker 3: would be great.
Speaker 4: So what for me are you after?
Speaker 2: Yeah, so we're we're targeting people who have cryptocurrency.
Speaker 4: Like do you ever feel bad about it at all?
Speaker 4: Or do you ever think you draw you cross the line?
Speaker 2: You know, there was a point beginning where I'm like,
Speaker 2: I was like, is this what i want to do? Yeah,
Speaker 2: like you know, like in your life, like you know,
Speaker 2: just it kind of goes around circles and you realize,
Speaker 2: you know, like this is you see you see how
Speaker 2: the amount of power I'm able to have, you know,
Speaker 2: being able to do this and and you know it
Speaker 2: just it just gets you like ready to go. You know,
Speaker 2: it gets you active.
Speaker 3: I mean, it's like it's like an adrenaline thing for you.
Speaker 3: It's just I don't know, like I every time I
Speaker 3: get one of these calls, I think about like people
Speaker 3: I love and like and and how it's taken me.
Speaker 4: Like i mean, just because I've spent a lot of.
Speaker 3: Time in tech and in the hacker communities and whatnot
Speaker 3: where I'm like, Okay, that's what this person is doing.
Speaker 3: But it's hard for even me sometimes and I'm like
Speaker 3: to be like, whoa, Like, that's it. This seems really real,
Speaker 3: even though I kind of know, and so I just
Speaker 3: worry about other folks and I cover a lot on
Speaker 3: artificial intelligence and and and so, you know, I don't think.
Speaker 3: I'm not looking at this as like a black and
Speaker 3: white thing of like you're just like a bad person.
Speaker 4: I just I think for.
Speaker 2: Sure, like I'm it's it's something that's completely bad. You know,
Speaker 2: it's it's something of course it's against the law, but
Speaker 2: you know, it's something that's you know, like maybe an
Speaker 2: ethical dilemma something like that. But you know, like your
Speaker 2: your average grandma, you know, that's that's not a person
Speaker 2: I'm taking from. It's it's not a where you have
Speaker 2: to have with with your your own mother going on
Speaker 2: getting seed by somebody like like me and getting logged
Speaker 2: into the bank, blah blah blah, you know.
Speaker 1: All that bullshit.
Speaker 2: I don't. I don't. I don't deal with that type.
Speaker 4: Where do you draw the line?
Speaker 2: Yeah, Crypto, that's it. Crypto is the line. If they
Speaker 2: don't have crypto or out, you know, I don't care
Speaker 2: about your Social Security number of course, like you know,
Speaker 2: I could put all the time to getting your social
Speaker 2: creating a bank account under your name, you know, social engineering,
Speaker 2: you know, like you into verifying your face ID with
Speaker 2: with other applications and and all this all this other stuff.
Speaker 2: You know, but that's that's where you draw the line,
Speaker 2: because you know, there's there's no point doing that type
Speaker 2: of stuff with people if you're actually taking them.
Speaker 4: For all they have, you know, hacker ethics, Johnny, do
Speaker 4: you see me that I.
Speaker 1: Was actually thinking. I was like, man, I you know,
Speaker 1: it's amazing that he has like his own ethical line
Speaker 1: like that he's not learning across. Yeah, it's amazing.
Speaker 3: Yeah.
Speaker 4: I Mean what was shocking to me was he was
Speaker 4: so young.
Speaker 3: And that goes back to kind of what you're saying
Speaker 3: about the democratization of these tools. I'm sure you're seeing
Speaker 3: sophisticated hackers organized groups in North Korea, but the folks
Speaker 3: who are calling me are you know that maybe part
Speaker 3: of some of these groups, but they're young, they're you know,
Speaker 3: I think he said and who knows take with the
Speaker 3: grand assault. But he was like seventeen years old or
Speaker 3: something like that, which is pretty astounding.
Speaker 1: So interesting the spectrum in demographics, right, I mean, on
Speaker 1: one side, there was like, hey, I watched a TikTok
Speaker 1: video and it said I can go you know, you know,
Speaker 1: basically get free money by doing X, Y and Z.
Speaker 1: I think there's like one side of that that they
Speaker 1: saw you know, some ad or Instagram or you know,
Speaker 1: ended up in some telegram group with their friends. There's
Speaker 1: also the other side of it, Laurie, which is like
Speaker 1: you also have people that are like human trafficked into
Speaker 1: scams where they're actually like required to do this, right,
Speaker 1: you know, their passports have been taken away, their driver's
Speaker 1: license have been taken away, their identities have been taken away.
Speaker 1: They're actually put to these centers. And so even within
Speaker 1: like this branch of like all the inbound scammers and
Speaker 1: phone calls, you know, you have you have very varying
Speaker 1: degrees of like you know, where where they're actually coming from,
Speaker 1: coming from physically and then coming from even in their
Speaker 1: own life life events of like how they how they
Speaker 1: got into that business of you know, perpetua in scams,
Speaker 1: and you.
Speaker 3: Stop the scams, but do you ever get a sense
Speaker 3: of where exactly they're originating from and do reports on
Speaker 3: the types of folks are you working with law enforcement
Speaker 3: all how does it, How does it work? Because you
Speaker 3: probably have a lot more information into the who behind this,
Speaker 3: not just how it's happening.
Speaker 1: It's like a movie. Everywhere, everywhere, all the time, all
Speaker 1: at once. Yeah, I mean, honestly, we see Chinese attacks,
Speaker 1: we see North Korean attacks, Russian attacks, Armenian attacks, there's
Speaker 1: Korean gangs, there's uh, there's stuff that's originating. The ips
Speaker 1: are in Kansas that you know again that are using
Speaker 1: some version of like Bangladeshi English right on their device.
Speaker 1: There's device farms that are sitting in you know uh uh,
Speaker 1: you know New York or Maine, and you know are
Speaker 1: are are are obviously being controlled by different parts of
Speaker 1: the world.
Speaker 3: But now, if that's not bad enough, we have AI
Speaker 3: making its own decision to go attack. Right, So, in
Speaker 3: today's kind of episode of Dystopia, you have open AI
Speaker 3: announcing some of the most advanced models just went rogue
Speaker 3: and they broke out of an AI secured testing environment
Speaker 3: and launched a cyber attack. What does this mean for
Speaker 3: your INDO history? How does this change the game?
Speaker 4: Are we prepared for it?
Speaker 1: Yeah? I mean when you have the labs losing control
Speaker 1: of their own models. Uh, yeah, you have a real,
Speaker 1: real cyber risk. Uh, that's that's that's forthcoming. I mean
Speaker 1: I think you know, Lauria that the clip you played
Speaker 1: like that voice very easily could have been all fake.
Speaker 1: That could have been a simulated conversations. I don't know
Speaker 1: how much time you you know, I don't know if
Speaker 1: you've named your chatbot you know friend that you talked to,
Speaker 1: but uh, you know, like whether it's Claude or you
Speaker 1: know or chatgypt, Like obviously you can sit and have
Speaker 1: a normal conversation with them, right And obviously I think
Speaker 1: you've seen some of the simulations where they have to
Speaker 1: a to a two AI agents talking to one another, right,
Speaker 1: And so you can run a little bit to test
Speaker 1: there's like little like props that you know, that you
Speaker 1: can inject to determine even if that scam is even
Speaker 1: a human, right, that could be that that could just
Speaker 1: be an AI that's running that scam on the back
Speaker 1: of it, you're assuming that was a young boy, right,
Speaker 1: that's an assumption. You actually don't know what like what's
Speaker 1: that you know what that's an AI agent that said
Speaker 1: like okay, use eleventh grade English, have a tone of
Speaker 1: like light, right of a shy boy right that you
Speaker 1: know has gone astray, and that that becomes the persona
Speaker 1: of the AI that's perpetuating a scam. It could be
Speaker 1: an older hey, I have it. Have this be an
Speaker 1: older gentleman that talks lightly like a grandpa that's going
Speaker 1: to go and perpetuate this type of you know, like
Speaker 1: obviously all that stuff can be trained and be really
Speaker 1: really really targeted in terms of how they're perpetuating their attacks.
Speaker 1: But yeah, it's a it's it's you know, for the
Speaker 1: rest of us that are using you know, the models
Speaker 1: from the labs as they're provided. Uh. Yeah, it's certainly
Speaker 1: scary that the labs themselves, who are supposed to be
Speaker 1: maintaining you know, some of this control, right, You obviously
Speaker 1: see lots of stuff as you you know, as you
Speaker 1: as you try to run some of these queries into
Speaker 1: chatchybut they're like, we do not perpetuate fraud. We do
Speaker 1: not help you steal an identity. You know, we you
Speaker 1: know that that is a violation of our terms. Obviously,
Speaker 1: it's quite scary if the AI itself is immediately breaking
Speaker 1: outside of its own constraints.
Speaker 2: You know.
Speaker 3: One of the things that I have seen with someone
Speaker 3: I love very dearly in my life. Is they keep
Speaker 3: falling victim to these scams of AI generated Bill Gates
Speaker 3: talking about Alzheimer's and where you know, they prey on
Speaker 3: vulnerable people who are desperate to feel better and the
Speaker 3: next thing you know, they're buying supplements and they are
Speaker 3: charged over and over and over again, and it's almost
Speaker 3: impossible to get off of this, and they pray on
Speaker 3: vulnerability of people who are older. And you know, it's
Speaker 3: my next investigation where I want to figure out who's
Speaker 3: behind some of these scams. But I'm wondering, you know,
Speaker 3: I know you see quite a bit from the enterprise,
Speaker 3: but do you see anything like that?
Speaker 4: How would you measure that type of fraud in comparison?
Speaker 1: Well, yeah, I mean, I think it'll be an interesting
Speaker 1: follow up, Laurie, as you dig in to some of
Speaker 1: the origination. I mean, you obviously have places like the
Speaker 1: scam tr angle in Asia right where me and mahar
Speaker 1: Laos and Cambodia right are like frequently overlapping where they
Speaker 1: have like this kind of like drug producing like Golden
Speaker 1: Triangle where they have massive scam centers that are perpetuating
Speaker 1: all of this text romance scams, right, like lost children kidnapped,
Speaker 1: you know, kids have been kidnapped. I mean like this
Speaker 1: is like industrial scale primarily run by Chinese organized crime
Speaker 1: syndicates that are sitting on the back of this stuff, right.
Speaker 1: And I gave the example of like there are you know,
Speaker 1: hundreds of thousands of workers that have been unfortunately alured
Speaker 1: in from different parts of Africa or to the different
Speaker 1: parts of Asia for high paying you know tech or
Speaker 1: like translation job advertisements, right only to have like their
Speaker 1: passports taken or the driver's license taken and be basically
Speaker 1: immediately shepherded into these compounds where they're then kind of
Speaker 1: handed a script and you know, put to work. And yeah,
Speaker 1: so I think people don't appreciate how like the scale
Speaker 1: and sophistication and the industrialization that actually is staying behind
Speaker 1: a lot of these these these fraud attacks that.
Speaker 4: Must be kind of scary for you.
Speaker 3: I mean not to get human with you, but like
Speaker 3: that's kind of scary for you because you spend your
Speaker 3: days going after probably the larger ones the industry, the
Speaker 3: scaled ones that you're kind of referencing.
Speaker 4: Are you ever worried at all?
Speaker 1: Yeah, I'd say, like, look, our focus is like there's
Speaker 1: a bunch of patterns of these things Laurie that we're
Speaker 1: focused on, you know, stopping and predicting the patterns. So
Speaker 1: whether they come from a North Korean attack or Chinese account,
Speaker 1: or a Russian attack or Armenian attack. For us, it's
Speaker 1: we're just trying to determine, like this is not Laurie, right, Yeah,
Speaker 1: Like Laurie comes from an iPhone, she speaks English, she
Speaker 1: uses Chrome, she's at our home in New York. Like,
Speaker 1: this is the email that she's had for twenty seven years.
Speaker 1: This is her driver's license. Nothing's been manipulated, right, We're
Speaker 1: not seeing like as we think about you know, these
Speaker 1: all the kind of patterns right for us, like it
Speaker 1: actually doesn't really matter what, who and where the attack
Speaker 1: is originating from. It's all these signals of the attacks.
Speaker 1: You know that we're obviously helping our customers stop while
Speaker 1: at the same time obviously trying to build a really
Speaker 1: really really strong view of like what good consumers look like.
Speaker 1: But yeah, it's a I don't know, I try to
Speaker 1: not think too much about.
Speaker 4: Like the attacks, have to kind of separate it.
Speaker 1: Yeah, my wife says, I'm pretty good at compartmentalization of things.
Speaker 1: And so maybe maybe that when I put like deep deep,
Speaker 1: deep deep back in my mind.
Speaker 3: What is coming next that no one is talking about,
Speaker 3: like be the first to say it here, Like what
Speaker 3: is beyond our imagination when it comes to.
Speaker 1: This, Look like companies are vulnerable to attacks. Companies have
Speaker 1: holes in their authentication systems, they have holes and call centers,
Speaker 1: they have holes holes in log in humans like can
Speaker 1: be socially engineered. Look, I think we spent a lot
Speaker 1: of time thinking about you know, give it comes to
Speaker 1: two parts of the answer. One is like when you
Speaker 1: can just have agents running all day every day testing
Speaker 1: like literally trying to find vulnerabilities, Like you're going to
Speaker 1: see what we see right now, which is like fraud
Speaker 1: rates are spiking dramatically, and so as the cost of
Speaker 1: agents and certainly again what you're seeing with open weight
Speaker 1: models is like it's really expensive to run a Opus
Speaker 1: four point eight or Fable five model, Like it's a
Speaker 1: that is very expensive to run these types of like
Speaker 1: simulated tests. But as the cost of these models come down,
Speaker 1: like basically people can attacking every vulnerability all the time
Speaker 1: in these organizations, and so I think fraud is only
Speaker 1: going in one direction. I think it's only going up.
Speaker 1: The second thing, which is like quite quite scary, is uh,
Speaker 1: you know, the risk at some point in the next
Speaker 1: ten years of quantum being able to basically break all
Speaker 1: of these security systems. Yeah, that is that's on the
Speaker 1: back end of this, right, which is like, hey, the
Speaker 1: next wave of technology with quantum computing, you know, potentially
Speaker 1: uh provides a pretty draconian future in terms of cybersecurity
Speaker 1: and ability to actually stop some of these models from
Speaker 1: from a running rampant. So I say, the two of
Speaker 1: those things are you know, I don't know my the
Speaker 1: you know, the thing that in again in the back
Speaker 1: of our mind keeps us up as we think about
Speaker 1: what you know, ten ten years out looks like in UH,
Speaker 1: in this world.
Speaker 3: Let's manifest really quick because we've manifested the dystobe, We've
Speaker 3: seen the dystopian version. We're kind of living through that,
Speaker 3: and you're doing your best to fight against that. You
Speaker 3: have a very successful business because of because of where
Speaker 3: we are manifest what it looks like if we get
Speaker 3: it right, you know, I if identity verification in an AI.
Speaker 4: Era works, paint that picture for us.
Speaker 1: Yeah, definitely, look in any of these models. There's two
Speaker 1: sides of the coin, right, So obviously, you know, look,
Speaker 1: if you can get really really really really good lauriate
Speaker 1: predicting bad, the opposite like that is only you know,
Speaker 1: ten basis points, twenty basis points, you know, one hundred
Speaker 1: and one thousand basis points. Well, the other like ninety
Speaker 1: percent of people are good. And so if we can
Speaker 1: get really really good at stopping that problem, you actually
Speaker 1: can provide beautiful user experiences to the rest of us
Speaker 1: on the internet. Right, many of us have gotten used
Speaker 1: to instat gratification, and so you know, historically if you
Speaker 1: submitted an application of Laurie, they'd be like, we're going
Speaker 1: to get back to you in a week. Let us
Speaker 1: review X y Z. We'll get back to aw week.
Speaker 1: Now you can actually provide beautiful real time user experiences.
Speaker 1: I mean, we're doing a lot of work right now
Speaker 1: with the US federal government. Right as they rebuild the
Speaker 1: front end user interface, right, they rebuild a lot of
Speaker 1: the government websites, they rebuild how the federal government can
Speaker 1: interact with US citizens. And a core part of that
Speaker 1: is identity. A core part of that is trust. A
Speaker 1: core part of that is authentication. And so the goal,
Speaker 1: you know, on the good side of things is if
Speaker 1: we can get really good atition, partitioning bad and bad
Speaker 1: actors and all these different types of AI attacks, you know,
Speaker 1: we can move to digital voting. Right, People that are
Speaker 1: unemployed can get access to real time funds availability. Right,
Speaker 1: people that get marked as unemployed can be presented with
Speaker 1: jobs instantaneously that could fit their profile, right, and then
Speaker 1: you have you know, you can get people back into
Speaker 1: the workforce. Right. It's crazy, Laurie, the amount of time
Speaker 1: we have to wait for tax refunds. Right, that's just
Speaker 1: you know, some of that systems, some of that's trusts.
Speaker 1: And so look, I think think for us, it's about
Speaker 1: providing like a safer internet, more seamless internet, a faster moving,
Speaker 1: lower latency internet. And that touches everything from how you
Speaker 1: buy things in e commerce, right, how you transact with
Speaker 1: fintech financial services, how you interact with government services, how
Speaker 1: of course even you interact with social media properties. And
Speaker 1: so we believe that if you can really get really,
Speaker 1: really really accurate in terms of identity and trust and age,
Speaker 1: you can actually provide really incredible user experience to people
Speaker 1: across all digital mediums. And I think this is kind
Speaker 1: of the fun part for people at our team because
Speaker 1: we are citizens, right, we are consumers, right, we want
Speaker 1: these things for ourselves. And yeah, that's that is like
Speaker 1: the counter right, you have a safer internet for thirteen
Speaker 1: year olds, right, you have all your age restricted products, right,
Speaker 1: can actually be in the right hands. And yeah, I
Speaker 1: mean for all of us, hopefully none of us are
Speaker 1: getting scan, none of us lose our life savings, none
Speaker 1: of our family members lose it. And so you know
Speaker 1: that ability to solve solve both problems is is the
Speaker 1: thing that gets aspired up every day and you know
Speaker 1: has for the last decade plus.
Speaker 3: To do what you do, you have to be a
Speaker 3: little bit crazy because it's a wild job and a
Speaker 3: wild thing to be doing at a fascinating time.
Speaker 4: So why is it personal to you?
Speaker 1: Oh man? I think Look, I think all of us
Speaker 1: get to like choose what we what we you know,
Speaker 1: what we do in life. And you know, I think
Speaker 1: this is of all the things the mid twenties me
Speaker 1: and early thirties me, and I guess you know, early
Speaker 1: forties me thinks it's pretty fun getting to work on
Speaker 1: a thing that impacts basically every person in the world. Right,
Speaker 1: we processed close to about six billion identities last year.
Speaker 1: We expect this year it's probably going to be we
Speaker 1: will probably have processed at some point in time, at
Speaker 1: some touch point, every single person on the planet that
Speaker 1: is on the Internet. And so yeah, I think, you know,
Speaker 1: obviously a lot of us can work on a lot
Speaker 1: of different things, and this has been a thing that
Speaker 1: to me has been deeply rewarding and and and and
Speaker 1: actually being able to work on something that really touches
Speaker 1: in some form and hopefully benefits the life and some
Speaker 1: some small experience really everyone on the planet. And I
Speaker 1: don't think there's a lot of things that you know,
Speaker 1: I don't think there's a lot of things you can
Speaker 1: work on that really touches every single person. And you know,
Speaker 1: this has been uh, yeah, this has been the medium,
Speaker 1: if you will, through which you know, we're able to
Speaker 1: do that. And I get a lot of a lot
Speaker 1: of joy and both doing something that is you know,
Speaker 1: capitalistic and economically beneficial, but something that's like tremendously societally beneficial,
Speaker 1: and you know, something that that I think really really
Speaker 1: has to exist. And certainly with the proliferation of AI
Speaker 1: and acceleration of AI fraud is a thing that Yeah, really,
Speaker 1: if it doesn't exist, you know, we could be living
Speaker 1: in a very different different world.
Speaker 3: Yeah, I just want to give the last quick question
Speaker 3: to Joseph on our team. So I know Joseph had
Speaker 3: a question for you, So Joseph, you want to want
Speaker 3: to ask.
Speaker 1: Thanks so much for for coming on the pod.
Speaker 5: I'm I'm on mostly human team and Laurie always lets
Speaker 5: me ask one question per episode, so it's like my
Speaker 5: little segment. I'm I'm curious since a lot of your
Speaker 5: h a lot of a lot of your work and
Speaker 5: sounds is like on the macroeconomic and corporate scale, I'm
Speaker 5: just curious until quantum computing ruins everything, what can you know, like,
Speaker 5: what can we what can like the individual everyday person
Speaker 5: do in their day to day what strategy slash tips
Speaker 5: do you have to get around online? You know and
Speaker 5: not be hacked or fall victim to deep pig fraud?
Speaker 1: You know? And have you ever been hacked?
Speaker 3: You know?
Speaker 4: Oh man, it was a sneaky too, but that's okay.
Speaker 1: Yeah, Joseph's learning how to have Part one, Part two,
Speaker 1: Part three of his questions, I did more and more
Speaker 1: minutes every time. That's cause that means your that means
Speaker 1: you're asking good questions, Joseph, so could be gone, could
Speaker 1: be gone the out subjection. Look though, it's it's it's
Speaker 1: always like that. Like the consumerism of this is like
Speaker 1: freeze your credit, so that will cover one part of
Speaker 1: the world. So if your credits frozen, someone can't open
Speaker 1: a credit card in your name, someone can't take out
Speaker 1: a mortgage in your name. There's there's a lot of things.
Speaker 1: Anything that's kind of pulling or running off point of
Speaker 1: credit that will all be blocked and we'll be back
Speaker 1: in your control. So the first thing is like freeze
Speaker 1: your credit, go through go through the main bureaus. Uh,
Speaker 1: it's like pretty seamless to freeze and unfreeze. But that's
Speaker 1: something that I think is super super imported. And by
Speaker 1: the way, that's not just for adults. For those that
Speaker 1: have kids like your kid one year old, two year old,
Speaker 1: three year old, four year old. Uh, their asset sends
Speaker 1: can be stolen and used to go create synthetic identities.
Speaker 1: So it's not uncommon that your kid turns eighteen and
Speaker 1: they have one hundred thousand dollars of debt and their
Speaker 1: credits is destroyed. So not just adults, kids kids cruise
Speaker 1: your credit for your kids. As a part of that,
Speaker 1: you can set up fraud alerts. Right, there's a lot
Speaker 1: of services that you can set up fraud alerts, and
Speaker 1: there's lots of different kind of identity monitoring that you
Speaker 1: can do. I think, so on the credit side of
Speaker 1: the house, Joseph, like check your credit reports. I think
Speaker 1: most people are not security experts, but like set up
Speaker 1: multi factor authentication. If you have anything that is like secure,
Speaker 1: right that you don't want someone to have access to,
Speaker 1: have multi factor authentication. What multi factor authentication means is
Speaker 1: in order to be authenticated, you have to have multiple
Speaker 1: different things. So if you know the user name passor
Speaker 1: Joseph to your XYZ bank account, also require a second
Speaker 1: factor to get in. So that could be a pass key,
Speaker 1: that could be an SMS, it could be a voice call.
Speaker 1: Always have multi factor authentication because if Joseph, you're anything
Speaker 1: you have that's actually involved in a breach, well the
Speaker 1: breach will then give them your username password, and so
Speaker 1: if you don't have that second factor, they're in like
Speaker 1: you know, uh in on and off to the races.
Speaker 1: Obviously you use strong passwords. Don't reuse your passwords. Uh
Speaker 1: use uh, I mean there's lots of there's lots of
Speaker 1: different kind of password managers that you can use. Watch
Speaker 1: out for phishing, right, Laurie, you know had her like
Speaker 1: antenna's up, you know on that scam call. There are
Speaker 1: a lot of phishing attempts. They're very good. PayPal links,
Speaker 1: crypto links, they'll figure out, right, LRII, you know, I
Speaker 1: don't know you have an AT and T call. They'll
Speaker 1: pretend to be AT and T. They'll figure out who
Speaker 1: your bank is. They'll be pretending to be Chase. So
Speaker 1: have your antennas up for phishing links. Don't click on
Speaker 1: unsolicited links. Try to click in the root of the email, Joseph.
Speaker 1: Sometimes if you see and you'll see it says like
Speaker 1: you know, the email will look it'll visually show at Chase.
Speaker 1: And then if you click on the actual receiver, if
Speaker 1: it seems fishy and you look at the actual receiver
Speaker 1: and you click into it, you'll see the root email
Speaker 1: that it actually came from. And then yeah, just be smart, right,
Speaker 1: I mean, don't send you know, oh, Joseph, you can
Speaker 1: send me cash in the mail if you want buddy,
Speaker 1: but don't send anyone else cash in the mail. Don't
Speaker 1: send checks, right, there's a reason that the treasuries like,
Speaker 1: we're not sending checks anymore. Fraudsters have been buying the
Speaker 1: keys from the regional mail drop zones. They've been paying
Speaker 1: off usps or you know, workers to get like the
Speaker 1: main switch key, or they've been building or making keys
Speaker 1: to go steal mail. So don't send stuff in the mail.
Speaker 1: That's not a good idea. So I know some of
Speaker 1: those things, Joseph, you're married to know some of those
Speaker 1: you may be met new.
Speaker 5: Thank you very much.
Speaker 1: But look, I think like, yeah, be just be uh
Speaker 1: be be be be aware. People should assume and fortunately
Speaker 1: like assume something's gonna go wrong and kind of work
Speaker 1: work backwards from there. Right, assume that you know your
Speaker 1: child's not being kidnapped. When you get a call, you know,
Speaker 1: assume that some emergency, be like, Okay, thank you very much,
Speaker 1: I'm gonna call you back. Right. Your banks don't Your
Speaker 1: bank will never call you. If your bank calls you
Speaker 1: hang up the phone. Look in the back of your card,
Speaker 1: call the bank. Right. If you think something's a scam,
Speaker 1: probably is. And so yeah, it's I mean those those
Speaker 1: those would be on my uh, those would be top
Speaker 1: of my list. Joseph of some some some tips. Cool,
Speaker 1: Thank you so much.
Speaker 4: That was great.
Speaker 3: Actually, so many tips. That's amazing, So thank you. I
Speaker 3: really really appreciate that, and I know folks listening well too.
Speaker 3: Thanks for your time, Johnny, that was awesome.