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Speaker 1: In a world where information overload can cloud judgment, one

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Speaker 1: from identifying threats to predicting trends. Five cast empowers you

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Speaker 1: your data into decisive action? Visit fivecast dot com now

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Speaker 1: and step into a world of clarity and foresight.

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Speaker 2: Foot for us.

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Speaker 3: If it doesn't work, you're just not using enough. You're

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Speaker 3: listening to soft Web Radio Special Operations, Military Nails. I'm

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Speaker 3: straight talk with the guys in the community.

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Speaker 1: Hi, welcome to another wonderful episode of soft rep Radio.

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Speaker 1: I'm your host rad as always, and it's another beautiful year.

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Speaker 1: We're in the year twenty twenty four and we've made

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Speaker 1: it all the way this far. I just want to

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Speaker 1: say thank you, and I couldn't have done it without

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Speaker 1: you listening. You know, you guys go to the merch

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Speaker 1: store which is at soft reap dot com, forward Slash Merch,

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Speaker 1: and we've got new stuff coming in all the time,

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Speaker 1: and we just really appreciate you buying our branded items,

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Speaker 1: you know with softwarep software radio, and just keep doing that.

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Speaker 1: Totally stoked. The other thing is our book club. We're

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Speaker 1: totally pushing that full steam ahead. We have a lot

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Speaker 1: of quality books inside of our book club. So if

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Speaker 1: you'll please go to soft reap dot com, forward Slash

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Speaker 1: book hyphen Club will check out our book club. Get involved.

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Speaker 1: You know, Brandon Webb is just putting a great library

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Speaker 1: together for you to enjoy, So go and check it out. Now.

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Speaker 1: A lot of times I have people on who have

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Speaker 1: been there, you know, served in the military. I have

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Speaker 1: today a special guest who works in the private sector. Okay,

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Speaker 1: and Abby will for her as Abigail or we'll go

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Speaker 1: with Abby. Abby will say Abby, we'll keep the last

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Speaker 1: name to d She's a military brat, her father was military.

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Speaker 1: She's worked private sector, master's degree in public policy from

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Speaker 1: University of Virginia, so she's got credits on what we're

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Speaker 1: about to talk about. We're going to get into national

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Speaker 1: security talking about you know, company like five Cast, which

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Speaker 1: she has currently transitioned into to work with. And so

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Speaker 1: I just want to welcome you to the show. Abby, Welcome,

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Speaker 1: Thank you.

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Speaker 2: I appreciate it. And I really like hearing about the

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Speaker 2: book club. I'm gonna have to check that out.

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Speaker 1: Yeah, it's a good time. I mean, if you like

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Speaker 1: to read a book and you want to read, and

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Speaker 1: I encourage everyone to read all books, ban books, any book.

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Speaker 1: Just pick up a book and read a book. It's

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Speaker 1: good for the brain. It's a you know, it's like

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Speaker 1: a gym. You go to the gym for your body.

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Speaker 1: You need to work your brain. You know. My daughter

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Speaker 1: in math class was like, why do I have to

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Speaker 1: do this hard math? It's like, what in your business, dad,

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Speaker 1: do I use this math for? And I asked her

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Speaker 1: teacher at her parent teacher conference and she said, Rad,

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Speaker 1: it's not so much that the math is used every day,

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Speaker 1: it's that it's using your brain to figure out the math.

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Speaker 1: And I was like, oh, tooche touchee. So let's use

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Speaker 1: our brains, okay, Let's let's be that of this world.

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Speaker 1: Let's be good stewards and with that abby. You know,

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Speaker 1: I mentioned that you're a military brat, you know, and again,

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Speaker 1: welcome to the show. What branch of the service was

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Speaker 1: your family in?

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Speaker 2: Yeah, so dad was army, just recently got out, And yeah,

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Speaker 2: I call myself an army brat, which basically just means

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Speaker 2: I cling onto the coattails of my dad's service. But

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Speaker 2: I just got the benefits of getting to move around

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Speaker 2: and getting to see and experience a lot of different

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Speaker 2: things that I don't think the average person gets the

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Speaker 2: chance to do. So I got to live overseas, got

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Speaker 2: to move around the country a lot. It was a

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Speaker 2: really great experience. So yeah, I grateful for that.

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Speaker 1: Right. A lot of the gate guards say can I

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Speaker 1: see your ID, ma'am, and you're like right here, military

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Speaker 1: coming through, no problem, healthy, Like this is my base exactly,

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Speaker 1: it's my base. I'm a skateboard all over this place.

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Speaker 1: You can't stop me. I got an ID card, you

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Speaker 1: see this.

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Speaker 2: That was like a big moment in my young life

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Speaker 2: and like I'm finally old enough for the dependent ID.

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Speaker 1: Yeah, Like I remember going, we're gonna get your picture now,

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Speaker 1: ten eleven years old, to get your but my mom

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Speaker 1: and dad always held onto it they're like, uh yeah,

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Speaker 1: we're gonna hold onto this with it. But yeah, right, exactly. Well,

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Speaker 1: my dad was in the army. He was a former

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Speaker 1: Green Beret. That's his flag on the mantle above the fireplace.

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Speaker 1: So when you're a military brat, I'm a military brat,

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Speaker 1: you know, and so it bonds us. I'm sure someone

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Speaker 1: listening right now is a military brat. You know, what

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Speaker 1: do they call him? Over in England? They call him

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Speaker 1: h oh, they have a whole different name. I'll think

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Speaker 1: about it here in just a minute if I don't remember,

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Speaker 1: someone post up what the British called the military brats

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Speaker 1: over there. They're like bunker brats. It's something different. But anyways,

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Speaker 1: so here you are, and you know, going where dad goes, right,

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Speaker 1: dad serving the country recently got out probably like you said,

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Speaker 1: And now you're how old when you decide that you know,

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Speaker 1: college is for you and you're going to chase this

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Speaker 1: you know, public policy masters? How old were you did that?

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Speaker 1: Just go straight from high school to you know, to college?

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Speaker 2: Straight? Yeah, straight from high school into college. And I yeah,

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Speaker 2: probably the geekiest kid in high school. I was like,

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Speaker 2: I don't even want to stay here for the full

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Speaker 2: four years. I'm just gonna leave high school at the

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Speaker 2: end of my junior year, heads straight to UVA, and

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Speaker 2: then I really like college. I stayed there longer. I

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Speaker 2: stayed there for five years and I got my masters,

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Speaker 2: so I hung around there so I could geek out

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Speaker 2: a little bit. But I was interested in international affairs,

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Speaker 2: foreign affairs, and I studied that as well as German.

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Speaker 2: And then I got my master's in public policy through

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Speaker 2: the Baton School, which is a fantastic program, and it

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Speaker 2: really educates you on all the fundamental tools that you

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Speaker 2: need to make policy. So you're talking about math earlier

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Speaker 2: and you're like, when am I going to use this?

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Speaker 2: You feel that way sometimes when you're in like the

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Speaker 2: soft sciences, and the Baton School was like, no, no, no,

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Speaker 2: you still have to maintain all of these hard skills

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Speaker 2: because you're going to need them when you actually get

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Speaker 2: to the policy making. So I really like the program.

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Speaker 1: It was super cool, right, A lot of gym therapy

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Speaker 1: for your brain.

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Speaker 2: Yeah, yeah, a lot of.

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Speaker 1: Different kit I use my Texas instruments, calculator right now,

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Speaker 1: Do I really have to do this? Yes, yes, just

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Speaker 1: do it. It was a struggle to do it right Yeah,

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Speaker 1: just get it done. Because you're so proud of yourself

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Speaker 1: for finishing your schooling. I could see it already and

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Speaker 1: you smile. You're like, five years college was chill. It

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Speaker 1: is a little bit different than going through the high

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Speaker 1: school phases. But as someone who's in the middlelitary and

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Speaker 1: you're moving around, your schooling probably was broken up here

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Speaker 1: and there, friendships were broken up, and now you're this

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Speaker 1: place and now you're that place. So really it's commendable to,

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Speaker 1: you know, move through that process and get your degree.

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Speaker 1: So congratulations on that. Now, thank you. Did you go

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Speaker 1: straight to where you're at from college? How did you

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Speaker 1: wind up at five Cast? Right, we're going to talk

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Speaker 1: about five Cast because it's a pretty interesting company that

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Speaker 1: you're with and they're also huge, huge supporter of software.

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Speaker 1: But I'm not gonna lie about that, Kay. You guys

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Speaker 1: have been supporting us and on board and the ship

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Speaker 1: and we row it together, and so I was like,

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Speaker 1: why don't we just have you on abby tell us

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Speaker 1: all about it, right, it's the truth. Yeah, go ahead,

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Speaker 1: let's go.

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Speaker 2: Yeah. We were super excited to be involved with you

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Speaker 2: guys and get the chance to support you all. And

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Speaker 2: then of course if I get a chance to talk

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Speaker 2: in front of a bunch of people, that's always fun.

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Speaker 1: So I wanted to.

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Speaker 2: Jump off that. But I pretty much since I graduated

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Speaker 2: from UVA, I've been in and supporting the national security space.

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Speaker 2: I think the whole time I was studying, I was

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Speaker 2: interested in diplomacy and national security, and I left thinking

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Speaker 2: I was going to be some sort of policy maker

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Speaker 2: or involved in supporting policy making. And I actually left,

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Speaker 2: and I was waiting for a job, and I started

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Speaker 2: in an internship and I got to do some really

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Speaker 2: neat work with open source intelligence in that internship, and

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Speaker 2: it sort of ruined me for any other career. I

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Speaker 2: was like, this is the coolest thing since slice spread.

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Speaker 2: I get to actually work with intelligence. I get to

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Speaker 2: actually support people who are working, living, breathing the mission,

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Speaker 2: and I can make a difference. And I'm looking at

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Speaker 2: real life data here, I'm not just writing about the

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Speaker 2: theory of it, which is what policy felt like to me.

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Speaker 2: And so ever, since I stuck out of academia and

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Speaker 2: started working, as soon as I got a taste of

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Speaker 2: working with intelligence, solving those real world problems and actually

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Speaker 2: feeding people information that could help them make the right

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Speaker 2: decision I didn't want to do anything else. So I've

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Speaker 2: been doing open source intelligence in the private sector since

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Speaker 2: I left school. I've been doing that for quite a

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Speaker 2: while now, and I've primarily been working in the technology

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Speaker 2: portion of open source intelligence. So I've had the unique

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Speaker 2: opportunity of not only collecting intelligence, but actually supporting the

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Speaker 2: technology that helps analysts access data, interpret data, and ultimately

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Speaker 2: get to the assessments that they're making. So I've always

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Speaker 2: been kind of on the tech side of things, which

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Speaker 2: has been really interesting, kind of a different, different side

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Speaker 2: of osent than I think most people see.

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Speaker 1: Right, And so with ost you know, okay, so open

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Speaker 1: source intelligence, right, I hear that to a lot of

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Speaker 1: guests that are into human you know, human intelligence, Right,

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Speaker 1: They're going after all these different acronyms, right and OCENT.

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Speaker 1: So what is it exactly and how is it used?

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Speaker 1: And why is it important in the job that you're

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Speaker 1: doing for you know, over for this tell.

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Speaker 2: Me, Yeah, so open source intelligence. You actually just asked

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Speaker 2: a question that's kind of a hot debate among the

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Speaker 2: community of OCENT because applying it has become pretty challenging

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Speaker 2: in the past ten years or so. But open source

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Speaker 2: intelligence is all about the intelligence assessment of publicly available information.

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Speaker 2: And publicly available information is anything that is readily publicly

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Speaker 2: accessible data to the average person, and publicly available information

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Speaker 2: nowadays is largely consumed through the Internet. So you see

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Speaker 2: a lot of people conflict open source intelligence with just

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Speaker 2: the Internet. So you see a lot of OCENT analysts

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Speaker 2: are really just passed with getting their arms around the

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Speaker 2: entirety of the Internet, which is really challenging and basically impossible.

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Speaker 2: And then not to mention that it includes things like

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Speaker 2: publications like this, like a podcast or a broadcast, news media,

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Speaker 2: hard copy paper, publications of data or information. So yeah,

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Speaker 2: OCENT is anything that someone without specialized access could get

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Speaker 2: their arms around or could get access to. And nowadays

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Speaker 2: it's just conflating with digital intelligence really.

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Speaker 1: So's what's hot right now where everybody has captured themselves,

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Speaker 1: you know, going after like the Capitol on January sixth,

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Speaker 1: and they filmed themselves and then they uploaded that, yeah, themselves,

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Speaker 1: So that would be OCENT where it's like public domain

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Speaker 1: info of what you want. They're like think, you're like, hey,

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Speaker 1: there is thanks for filming you going through the Capitol

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Speaker 1: and putting it up on the internet. Yeah, And now

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Speaker 1: it's public domain and you can just filter it and

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Speaker 1: just and that's how they're I mean, really, I can

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Speaker 1: imagine that's just one example.

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Speaker 2: Yeah, that's one example of publicly available information. And I

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Speaker 2: think one of the biggest difficulties in open source intelligence

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Speaker 2: is trying to communicate how valuable it can be. So

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Speaker 2: you kind of hit the nail on the head, like, oh,

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Speaker 2: that's an example of how I as insert agency or

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Speaker 2: insert official could use open source intelligence. And we're seeing

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Speaker 2: it nowadays with January six, but also with the conflict

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Speaker 2: in Ukraine that was actually able to support a lot

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Speaker 2: of force protection and even just basic awareness of like

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Speaker 2: order of battle going on in Ukraine, so understanding what

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Speaker 2: units are operating in Ukraine in Russia, what people are

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Speaker 2: these saying sending there, what resources do they have access

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Speaker 2: to that? And now we're seeing it again in Israel

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Speaker 2: and Gaza as well. There's a lot of information that's

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Speaker 2: spreading throughout typically the Internet, that's spreading rapidly through the

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Speaker 2: Internet and open source data and is providing critical insights

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Speaker 2: to really anyone that's responsible for security safety or if

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Speaker 2: you know they're part of the military, if there are

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Speaker 2: military efforts in those spaces they could benefit from those real,

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Speaker 2: live pieces of data that might affect the decision making

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Speaker 2: that they're making on the ground.

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Speaker 1: So right, because I mean like you mean, they showed

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Speaker 1: the attack that happened in Israel or October seventh was

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Speaker 1: like on social media right right there was like this

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Speaker 1: is what they came in on. People had this the

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Speaker 1: cameras out at the festivals and they were able to

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Speaker 1: capture all of this intelligence right there, real time data,

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Speaker 1: and we want to believe it's the real time data. Okay.

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Speaker 1: I'm not saying that that situation is anything that didn't happen.

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Speaker 1: I'm just saying that when you're taking these photos or

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Speaker 1: you're seeing this intelligence, that's where you have to come

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Speaker 1: in and you have to start to like really play,

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Speaker 1: like you get to decipher it, right mean, like it

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Speaker 1: could be put out there to be found mm hmm

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Speaker 1: yeah right, oh now you have Now there's the narrative,

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Speaker 1: or it could be out there because it's organically real, Okay,

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Speaker 1: because somebody on a wave runner had their cell phone

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Speaker 1: or GoPro when an explosion happened, and then that's you know, yeah, yeah,

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Speaker 1: holy yeah, you know, I never really you know, you know,

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Speaker 1: we don't really think of it like we're putting ourselves

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Speaker 1: out there so much to be investigated.

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Speaker 2: Yeah, and I think, and getting into you, you mentioned

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Speaker 2: you know why did I end up at five Casts,

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Speaker 2: And it's because I've I've worked in the space of

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Speaker 2: ultimately protecting people for a really long time and helping

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Speaker 2: people make good decisions in order to protect average citizens

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Speaker 2: or global citizens really, and open source data just plays

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Speaker 2: such a unique role because essentially there's this plethora of

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Speaker 2: data that everyone is capturing sharing online and to actually

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Speaker 2: make use of it, you need to have either some

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Speaker 2: sort of tradecraft or technical capability to help interpret that.

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Speaker 2: And the reason I ended up at five cast is

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Speaker 2: because five cast develops open source intelligence solutions and one

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Speaker 2: of my former jobs, I was responsible for evaluating open

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Speaker 2: source tooling and I actually five Casts came across my

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Speaker 2: desk and I was like, this is the coolest thing

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Speaker 2: I've ever seen. How does everyone not have this because

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Speaker 2: all of the rapid decision making and all of this

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Speaker 2: really important work that's being done to protect people and

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Speaker 2: protect our country. I was like, what do you mean

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Speaker 2: this isn't in people's hands yet. That's actually how I

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Speaker 2: ended up podcast because I was supposed to be evaluating

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Speaker 2: this tool, which I did, and I reached out to

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Speaker 2: one of the co founders. Feels like, if you ever

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Speaker 2: make it to the US, you tell me the second

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Speaker 2: you make it there, because I would love to work

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Speaker 2: with this company, and I've been there ever since.

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Speaker 1: Where's Pivecast? Where'd they come from? Australia?

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Speaker 2: Yeah? Fivecast is headquartered in Adelaide, Australia.

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Speaker 1: But yet they have a worldwide commitment to security. Correct

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Speaker 1: is that one? I understand? There?

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Speaker 2: Worldwide commitment and we pretty uniquely service primarily the Five

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Speaker 2: Eyes community. The Five Eyes community is just kind of

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Speaker 2: an intelligence partnership between a lot of English speaking nations,

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Speaker 2: So New Zealand, Australia, Canada, the UK, the US. You know,

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Speaker 2: all of these countries they fit into this space and

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Speaker 2: they're sharing intelligence, they're sharing methods, techniques in order to

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Speaker 2: protect one another, and we uniquely service that. So we

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Speaker 2: have offices in Australia, in London and in the US.

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Speaker 2: So I'm based out of our Arlington office in Virginia.

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Speaker 1: Do you ever get to go to London?

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Speaker 2: I did. Yeah, they're kind enough to send you there.

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Speaker 1: We love that. Yeah, yeah, kind five casts, like they

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Speaker 1: used to go back so kind, so kind.

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Speaker 2: Yeah I got to Yeah, I got to be involved

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Speaker 2: in helping the UK team sort of get their feet

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Speaker 2: under them as they were setting up the office, which

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Speaker 2: was really neat. And now they're you know, fully functioning office.

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Speaker 2: They're off and running, which is super cool. And just

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Speaker 2: this past year I got the chance to head out

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Speaker 2: to Australia as well and meet all these people that

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Speaker 2: I've been working with for like four years that are

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Speaker 2: developing all these really neat solutions. So yeah, I would

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Speaker 2: say we we have a commitment to protecting global communities

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Speaker 2: and we pretty meekly are situated within the five Eyes community,

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Speaker 2: which is super important just from a partnership perspective. So yeah,

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Speaker 2: that's it's a pretty exciting place to be.

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Speaker 1: Let me ask this a question, who would use five cast?

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Speaker 1: Like who would you put five Who would who would

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Speaker 1: need five cast? Right? We're talking national security here, but like,

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Speaker 1: really explain that to me.

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Speaker 2: Yeah, five gasts. So we have a couple of different solutions,

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Speaker 2: but our primary users would be intelligence analysts and or investigators.

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Speaker 2: So if you have some need, and this is how

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Speaker 2: I pitch it to people who are just learning about

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Speaker 2: fodcasts for the first time, I tell them, do you

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Speaker 2: care about people talking online? Do you care about what's

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Speaker 2: being said online? Then you need five casts because we

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Speaker 2: give you a unique tool to be able to access

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Speaker 2: online communications and begin deciphering them in the context of

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Speaker 2: either your next intelligence product or your next case that

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Speaker 2: you're working on. So investigators intelligence analysts typically, but we

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Speaker 2: find ourselves in unique spots where someone just needs they

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Speaker 2: care about what is being said online from a security

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Speaker 2: in front perspective, and they need five casts.

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Speaker 1: Go find five casts at the website fivecast dot.

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Speaker 2: They go to fivecasts dot com, they can check our

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Speaker 2: products there.

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Speaker 1: Let's talk a little bit more about this five cast.

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Speaker 1: So would I be able to use five casts to

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Speaker 1: see who's looking at my Instagram profile?

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Speaker 2: So five casts is built for and we actually have

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Speaker 2: improved use cases. It's built for certain agencies, typically public

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Speaker 2: agencies that have the authorities, legal authorities to look at

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Speaker 2: data for security. So like an average person me just

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Speaker 2: curious about what's on social media, I wouldn't be able

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Speaker 2: to use five casts. But if I have the mission

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Speaker 2: to look at online data in order to understand a threat,

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Speaker 2: in order to pick up certain risks, then I would

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Speaker 2: be able to use five casts. So five casts is

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Speaker 2: something that we set up for teams to use, typically

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Speaker 2: intelligence or collection teams or investigative teams. So typically our

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Speaker 2: users fall within defense, national security, law enforcement, private sector

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Speaker 2: security entities. So there's a lot of there's a lot

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Speaker 2: of pressure place on the private sector too to protect

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Speaker 2: their supply chains, to protect their day to day operations,

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Speaker 2: their employees, so we help with that as well. And

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Speaker 2: of course there are national security threats to private sector

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Speaker 2: entities right from an insider threat perspective, from IP protection.

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Speaker 1: That's intellectual property perty talking about exactly right.

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Speaker 2: So anybody that falls kind of within those types of

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Speaker 2: agencies would have the ability to use five casts.

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Speaker 1: I see, I see. Okay, So well that's a cool company,

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Speaker 1: cool place to work at, and they send you to London.

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Speaker 1: Software sends me to London. I can't complain about that, okay,

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Speaker 1: very kindly of you, Brandon and the team. We go

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Speaker 1: over there to do boxing each year, and then you know,

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Speaker 1: we had the great COVID Yeah, and it kind of

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Speaker 1: like nipped it in twenty nineteen. We were supposed to

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Speaker 1: go over in twenty twenty oh and then you know,

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Speaker 1: we've done like three or four years of it up

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Speaker 1: until from twenty sixteen till then and then COVID hit

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Speaker 1: and kind of like just stopped the world and everything's

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Speaker 1: just starting to you know, grease back to movement, you

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Speaker 1: know everything. It's just like I mean here in Utah,

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Speaker 1: that's where I'm out of It's it's everybody's just trying

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Speaker 1: to still, I think, shake it off. You know, we

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Speaker 1: have no not to quote Taylor Swift, but it's true.

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Speaker 1: You know, it's like, can we just shake it off already,

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Speaker 1: get back to functioning like we were, you know, going

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Speaker 1: to concerts where we were just like right next to

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Speaker 1: each other. And you know, that's starting to come back

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Speaker 1: to the world. So I can only imagine that intelligence

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Speaker 1: is just getting out of control as well, with everybody

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Speaker 1: just back to society, and you know, all sorts of

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Speaker 1: pings are going off on the internet with like you said,

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Speaker 1: involving maybe Gaza and Israel and you know, Iran and

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Speaker 1: all these different places. They got cell phones, they're posting

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Speaker 1: stuff up and that's being read and watched. You know,

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Speaker 1: it's in Ukraine, right, how many of these young kids

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Speaker 1: in their twenties over there are fighting the war, are

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Speaker 1: tech savvy and they're just like using their drones and

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Speaker 1: they're filming. Yeah, it's getting uploaded exactly right.

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Speaker 2: And I think you bring up an interesting point about

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Speaker 2: specifically open source intelligence during COVID, COVID lockdown and again

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Speaker 2: M five casts and OSENT, Like, if you care about

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Speaker 2: people talking online, that's when you start caring about this.

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Speaker 2: How did people connect and communicate and operate when they

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Speaker 2: couldn't do it in person? They moved to digital spaces,

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Speaker 2: they moved online. So OSIN actually became one of the

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Speaker 2: most critical sources in my opinion, because that's where people

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Speaker 2: could commune cap that's where they could collaborate, especially like

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Speaker 2: listed activities. That's where a lot of their efforts were shifting.

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Speaker 2: And if you didn't have some sort of ability to

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Speaker 2: look into that, then you were missing and you had

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Speaker 2: huge gaps in your intelligence products. So yeah, COVID affected

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Speaker 2: the intelligence space too, and it's affecting you know, what's

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Speaker 2: going on in the world obviously, but even just from

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Speaker 2: like an analyst perspective, knowing where to look and what's

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Speaker 2: going to affect your products or investigations that you're conducting. Yeah,

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Speaker 2: OCINTH became kind of came into the center I think

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Speaker 2: of where people realized they had to look, is, oh,

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Speaker 2: everyone's communicating online now, not that they weren't doing so before,

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Speaker 2: but it's especially important because they don't always have the

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Speaker 2: means to do that in person. So, yeah, ocean like

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Speaker 2: went through then too, all.

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Speaker 1: The zooms, right, everybody started to do all these you know,

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Speaker 1: like how we're doing these interviews. I used to be

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Speaker 1: in a studio and there was a mic situation and

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Speaker 1: had a guy in there that was producing and doing

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Speaker 1: it all right there, and you'd come in and I'd

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Speaker 1: have like three or four guests in a day, and

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Speaker 1: you would come in and we'd do hour long episodes

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Speaker 1: and then cut them, cut them, cut them. I changed

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Speaker 1: my shirt a new day, change my shirt new day.

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Speaker 1: You know. That's that's that's changed to this type of

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Speaker 1: dynamic where we're relying on the internet and I'm glad

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Speaker 1: I get to reach out and talk to somebody who

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Speaker 1: is on the other side of the country or the

431
00:24:27,600 --> 00:24:30,359
Speaker 1: world and and connect and I'm glad for this technology

432
00:24:30,400 --> 00:24:34,040
Speaker 1: to be there. But I remember people hacking the meetings

433
00:24:34,240 --> 00:24:40,240
Speaker 1: that these companies had with their yeah, exactly, and all

434
00:24:40,240 --> 00:24:43,520
Speaker 1: of a sudden you got someone listening in who has

435
00:24:43,560 --> 00:24:45,040
Speaker 1: not signed a non disclosure agreement.

436
00:24:46,480 --> 00:24:50,640
Speaker 2: Yeah, exactly, you know, and it's like wow.

437
00:24:50,359 --> 00:24:52,240
Speaker 1: You know, I mean, there's so much to think about

438
00:24:52,280 --> 00:24:54,760
Speaker 1: that as going on. And I know, I put myself

439
00:24:54,800 --> 00:24:57,240
Speaker 1: out there right and you're you're standing up in front

440
00:24:57,240 --> 00:24:59,240
Speaker 1: of the microphone. Put yourself out there right now. And

441
00:24:59,560 --> 00:25:01,439
Speaker 1: you know, we discussed before the show, Hey, if you

442
00:25:01,440 --> 00:25:03,359
Speaker 1: don't want it out there on the internet, don't say it,

443
00:25:03,359 --> 00:25:05,400
Speaker 1: because it goes out there on the internet. I mean,

444
00:25:05,440 --> 00:25:08,080
Speaker 1: this is real, right, And so my listener out there,

445
00:25:08,080 --> 00:25:09,640
Speaker 1: if you don't want to set on the internet, don't

446
00:25:09,640 --> 00:25:11,359
Speaker 1: say it on the internet because it'll stick.

447
00:25:11,600 --> 00:25:15,919
Speaker 2: Yeah, exactly right. And I think, yeah, open source intelligence,

448
00:25:16,040 --> 00:25:19,399
Speaker 2: especially digital intelligence rights, we're talking about things that appear

449
00:25:19,400 --> 00:25:23,520
Speaker 2: on the Internet. Social media becomes pretty important in that

450
00:25:23,720 --> 00:25:27,879
Speaker 2: space because that's how people are connecting, communicating, interacting, meeting

451
00:25:27,920 --> 00:25:33,639
Speaker 2: new people, learning about their interests, and ultimately that's where

452
00:25:33,920 --> 00:25:37,800
Speaker 2: a lot of really rich intelligence comes from. And I

453
00:25:37,840 --> 00:25:42,320
Speaker 2: think when you start talking about social media and where

454
00:25:42,359 --> 00:25:45,680
Speaker 2: that came about, which was what early twenty teens a

455
00:25:45,720 --> 00:25:49,440
Speaker 2: little bit before, there's been so many changes to how

456
00:25:49,520 --> 00:25:53,199
Speaker 2: information is following and I think around then. Nothing was

457
00:25:53,280 --> 00:25:56,639
Speaker 2: moderated or understood, and there was really interesting stuff that

458
00:25:56,680 --> 00:25:59,640
Speaker 2: people were like, Oh, it's on social media, nobody's ever

459
00:25:59,680 --> 00:26:03,320
Speaker 2: going to see this, And it turns out that yeah,

460
00:26:03,320 --> 00:26:07,000
Speaker 2: people will see that, and sometimes it would be like

461
00:26:07,080 --> 00:26:10,840
Speaker 2: a key piece of information for someone's security or safety

462
00:26:11,040 --> 00:26:14,119
Speaker 2: or an operational decision that you're making on the ground

463
00:26:14,480 --> 00:26:16,919
Speaker 2: around then. So, for example, you could find out where

464
00:26:17,119 --> 00:26:21,560
Speaker 2: different terror groups were moving operating, who they were getting

465
00:26:21,600 --> 00:26:25,360
Speaker 2: resources from pretty early on. Groups are getting more savvy

466
00:26:25,400 --> 00:26:27,560
Speaker 2: now in how they communicate, but that doesn't mean that

467
00:26:27,600 --> 00:26:31,080
Speaker 2: it's not out there and kind of five casts. Whole

468
00:26:31,080 --> 00:26:33,520
Speaker 2: mission and whole capability is about being able to get

469
00:26:33,560 --> 00:26:36,880
Speaker 2: and access and interpret those spaces and those critical pieces

470
00:26:36,880 --> 00:26:40,080
Speaker 2: of information where they're out there in the public domain.

471
00:26:40,200 --> 00:26:43,280
Speaker 2: So yes, if it's to the general public, if you

472
00:26:43,320 --> 00:26:45,560
Speaker 2: don't have something out there, don't say it. But if

473
00:26:45,560 --> 00:26:48,720
Speaker 2: you're a criminal, please post because it's really great when

474
00:26:48,880 --> 00:26:50,520
Speaker 2: I can, I can help.

475
00:26:50,560 --> 00:26:55,320
Speaker 1: Don't they say criminals are dumb, Yeah, because they're criminals. No,

476
00:26:55,640 --> 00:26:57,840
Speaker 1: Like they usually leave some type of like you know,

477
00:26:58,760 --> 00:27:02,000
Speaker 1: Shaggy and Scooby Doo. Yeah, it was mister so and

478
00:27:02,040 --> 00:27:05,560
Speaker 1: so at the amusement park look, you left the sandwich.

479
00:27:05,880 --> 00:27:06,119
Speaker 2: You know.

480
00:27:06,200 --> 00:27:09,000
Speaker 1: It's like, you know, they think they get away with it.

481
00:27:09,440 --> 00:27:11,919
Speaker 1: Or even this guy recently who they just attributed to

482
00:27:12,000 --> 00:27:15,679
Speaker 1: like three missing women even though he's dead. Technology today

483
00:27:16,359 --> 00:27:18,879
Speaker 1: can go now and figure things out that it couldn't

484
00:27:18,920 --> 00:27:23,520
Speaker 1: then and give these women closure for this guy, you know,

485
00:27:23,840 --> 00:27:26,359
Speaker 1: never having closure for the families, et cetera. It's like

486
00:27:26,760 --> 00:27:30,879
Speaker 1: technology is just crazily expanding and with the world of

487
00:27:31,040 --> 00:27:34,280
Speaker 1: AI out there right and and that you guys have

488
00:27:34,359 --> 00:27:36,919
Speaker 1: to be completely you know, all these chat bots and

489
00:27:36,960 --> 00:27:39,640
Speaker 1: all these different bots and all these different like, hey,

490
00:27:40,040 --> 00:27:43,000
Speaker 1: tell me how to write a contract about rent and boats,

491
00:27:43,240 --> 00:27:45,080
Speaker 1: and it brings up like what you should have for

492
00:27:45,119 --> 00:27:48,040
Speaker 1: a boat rental. But you got to think the words

493
00:27:48,040 --> 00:27:50,800
Speaker 1: that they're taking from all of that information it's gathering,

494
00:27:50,960 --> 00:27:53,320
Speaker 1: the chat gp, these chat bots and all this AI's

495
00:27:53,359 --> 00:27:56,199
Speaker 1: gathering all this information just like a human would be

496
00:27:56,240 --> 00:27:58,880
Speaker 1: doing it, right, But I think a human has If

497
00:27:58,880 --> 00:28:01,800
Speaker 1: you have only AI talking to AI, you know it's

498
00:28:01,800 --> 00:28:04,600
Speaker 1: not going to work. You have to have human interaction

499
00:28:05,240 --> 00:28:08,960
Speaker 1: to control the AI's flow of information. Otherwise AI is

500
00:28:08,960 --> 00:28:12,600
Speaker 1: just going to repeat itself to itself at some level.

501
00:28:13,080 --> 00:28:16,640
Speaker 2: I think not so much with chat GPT. I think

502
00:28:17,000 --> 00:28:18,600
Speaker 2: that's oh man, that's a whole can of worms. We

503
00:28:18,600 --> 00:28:21,040
Speaker 2: could probably talk about that for the whole session.

504
00:28:21,080 --> 00:28:23,080
Speaker 1: We'll let New York Times handle that because they have

505
00:28:23,200 --> 00:28:25,880
Speaker 1: something to say about that right there.

506
00:28:25,960 --> 00:28:28,600
Speaker 2: But you bring up AI and I think it's so important,

507
00:28:28,640 --> 00:28:31,159
Speaker 2: and it's something that at five casts we have a

508
00:28:31,200 --> 00:28:34,560
Speaker 2: whole team dedicated to just figure out new and interesting

509
00:28:34,560 --> 00:28:40,000
Speaker 2: ways to analyze data to raise to the four the

510
00:28:40,000 --> 00:28:45,200
Speaker 2: most relevant content, right, the most important things that investigators

511
00:28:45,200 --> 00:28:49,040
Speaker 2: and analysts would want to find in data. And the

512
00:28:49,120 --> 00:28:53,480
Speaker 2: reason that AI is so critical is because the amount

513
00:28:53,760 --> 00:28:58,240
Speaker 2: of data and the variety of ways it can appear

514
00:28:58,480 --> 00:29:02,120
Speaker 2: online that we have nowadays, and just the rate at

515
00:29:02,120 --> 00:29:05,640
Speaker 2: which it's being generated. You'll hear people talk about the

516
00:29:05,720 --> 00:29:10,120
Speaker 2: three v's a lot in O sense, like velocity, variety, volume. Yeah,

517
00:29:10,200 --> 00:29:14,840
Speaker 2: like that amount of data that's appearing. If you sat

518
00:29:14,880 --> 00:29:17,640
Speaker 2: down a bunch of intelligence analysts and just said you

519
00:29:17,760 --> 00:29:20,120
Speaker 2: have to consume all the data, interpret all the data,

520
00:29:20,400 --> 00:29:23,520
Speaker 2: and put out all these reports, they'd be stuck at

521
00:29:23,560 --> 00:29:25,960
Speaker 2: stage one a lot of the times because they're just

522
00:29:26,000 --> 00:29:28,120
Speaker 2: sitting there trying to find, you know, where is that

523
00:29:28,160 --> 00:29:30,800
Speaker 2: one piece of data where criminal left that bread crumb

524
00:29:30,840 --> 00:29:33,320
Speaker 2: that you're talking about. They have to consume so much

525
00:29:33,320 --> 00:29:37,560
Speaker 2: of that information to get to that point. And ultimately,

526
00:29:38,160 --> 00:29:42,200
Speaker 2: analysts are hired because they have these giant brains and

527
00:29:42,240 --> 00:29:45,880
Speaker 2: they're able to make these connections and able to make

528
00:29:45,960 --> 00:29:49,760
Speaker 2: really critical assessments about why this information is important and

529
00:29:49,880 --> 00:29:52,560
Speaker 2: why decision makers should know about it, and you want

530
00:29:52,560 --> 00:29:56,520
Speaker 2: their big brains working on that problem ninety percent of

531
00:29:56,520 --> 00:29:59,520
Speaker 2: the time, right, And unfortunately, I think a lot of

532
00:29:59,560 --> 00:30:02,520
Speaker 2: the time investigators analysts are just sitting there, like, how

533
00:30:02,560 --> 00:30:06,480
Speaker 2: do I access all this billions and billions of bits

534
00:30:06,480 --> 00:30:09,200
Speaker 2: of data that exists out there? And they're stuck spending

535
00:30:09,280 --> 00:30:11,840
Speaker 2: ninety percent of their time just trying to access the content.

536
00:30:12,520 --> 00:30:13,360
Speaker 2: And unfortunately, you.

537
00:30:13,440 --> 00:30:15,240
Speaker 1: Try to art to make a macro to just push

538
00:30:15,240 --> 00:30:18,040
Speaker 1: a button. Yeah, I have a macro on my five cast.

539
00:30:18,360 --> 00:30:20,840
Speaker 1: Have a macro. I just hit it and it pulls

540
00:30:20,880 --> 00:30:22,680
Speaker 1: it all up. I had to put it together, but

541
00:30:22,760 --> 00:30:24,160
Speaker 1: it's just a one button macro.

542
00:30:24,360 --> 00:30:27,240
Speaker 2: Right, And that's the way you see tools like five

543
00:30:27,320 --> 00:30:32,160
Speaker 2: casts becoming so critical in this space because not only

544
00:30:32,200 --> 00:30:35,680
Speaker 2: are threats developing at this crazy rate, data is developing

545
00:30:35,680 --> 00:30:38,680
Speaker 2: at this crazy rate. Criminals are getting smarter every single day,

546
00:30:39,160 --> 00:30:43,520
Speaker 2: and if you can't consume data and make assessments at

547
00:30:43,560 --> 00:30:46,920
Speaker 2: that same rate, you're going to fall behind at some level.

548
00:30:47,080 --> 00:30:50,600
Speaker 2: So when we start introducing tools to automate things that

549
00:30:50,680 --> 00:30:54,400
Speaker 2: the analyst is doing, and we start introducing AI to

550
00:30:54,480 --> 00:30:58,800
Speaker 2: help the analysts arrive at the most relevant data for

551
00:30:58,960 --> 00:31:02,880
Speaker 2: their assessments, I think you end up really getting to

552
00:31:02,920 --> 00:31:06,000
Speaker 2: the point of a company like Fivecasts, which is we

553
00:31:06,080 --> 00:31:10,120
Speaker 2: build solutions to hyper enable an analyst. We don't want

554
00:31:10,160 --> 00:31:14,400
Speaker 2: to replace analysts. We don't want to replace investigators intelligence officers.

555
00:31:14,880 --> 00:31:17,719
Speaker 2: We want to give them the tooling so that that

556
00:31:17,800 --> 00:31:20,040
Speaker 2: ninety percent of their time can be spent all the

557
00:31:20,080 --> 00:31:22,600
Speaker 2: big brain stuff you know AI, talking to AI, you

558
00:31:22,600 --> 00:31:24,640
Speaker 2: talked about chat, GBT, you talked about all these things.

559
00:31:25,080 --> 00:31:29,120
Speaker 2: If you had, you know, computers doing everything, you miss

560
00:31:29,200 --> 00:31:33,320
Speaker 2: this critical component of judgment and decision making and being

561
00:31:33,320 --> 00:31:36,840
Speaker 2: able to draw these really disparate relationships and conclusions that

562
00:31:36,880 --> 00:31:40,880
Speaker 2: analysts are able to do because of their expertise. All

563
00:31:40,920 --> 00:31:43,479
Speaker 2: we want to do is we don't want computers doing everything.

564
00:31:43,560 --> 00:31:46,680
Speaker 2: We want to create solutions that makes their job as

565
00:31:46,720 --> 00:31:50,040
Speaker 2: easy as possible and then lets them spend the majority

566
00:31:50,080 --> 00:31:53,720
Speaker 2: of their time on the most important stuff, which is Okay,

567
00:31:54,240 --> 00:31:58,000
Speaker 2: here's billions of pieces of data, here's the ten most

568
00:31:58,040 --> 00:32:01,760
Speaker 2: important pieces of data. What I care about? And now

569
00:32:01,800 --> 00:32:04,840
Speaker 2: what does this mean and how do I communicate this

570
00:32:04,920 --> 00:32:08,360
Speaker 2: to my leadership so that we can make important tactical, operational,

571
00:32:08,440 --> 00:32:12,160
Speaker 2: strategic decisions so or solve our case or make an arrest,

572
00:32:12,200 --> 00:32:15,120
Speaker 2: whatever it might be. So that's yeah, you hit the

573
00:32:15,200 --> 00:32:18,120
Speaker 2: nail on the head. AI is just that and automation,

574
00:32:18,320 --> 00:32:20,560
Speaker 2: I think are all about the analysts. At the end

575
00:32:20,600 --> 00:32:22,080
Speaker 2: of the day. We just want them to be able

576
00:32:22,120 --> 00:32:23,560
Speaker 2: to do their job best as they can.

577
00:32:24,160 --> 00:32:26,280
Speaker 1: Right, how many of us have ever had like an

578
00:32:26,320 --> 00:32:29,800
Speaker 1: auto email that left your like you know your name,

579
00:32:29,880 --> 00:32:32,000
Speaker 1: your number, your hours, you're in the office, you know

580
00:32:32,080 --> 00:32:35,200
Speaker 1: at the bottom of you know your you know you

581
00:32:35,280 --> 00:32:37,360
Speaker 1: sign your signature. Yeah, I mean it's just like we

582
00:32:37,640 --> 00:32:39,920
Speaker 1: that's just like helps us to have to not type that, right,

583
00:32:40,000 --> 00:32:41,880
Speaker 1: it's like one less thing for me to type. I

584
00:32:41,880 --> 00:32:43,680
Speaker 1: can just fill out the email, send it and it's

585
00:32:43,720 --> 00:32:46,520
Speaker 1: already pre populated at the bottom. When I say macros,

586
00:32:46,520 --> 00:32:48,800
Speaker 1: if my listener doesn't know what a macro is, it's

587
00:32:48,800 --> 00:32:51,440
Speaker 1: like it's a shortcut. It's like a keystroke shortcut that

588
00:32:51,880 --> 00:32:53,560
Speaker 1: somebody learned how to make a macro out of a

589
00:32:53,600 --> 00:32:55,880
Speaker 1: program on the computer, and they just hit the letter

590
00:32:56,040 --> 00:32:59,240
Speaker 1: control F and it like does this whole spreadsheet and

591
00:32:59,280 --> 00:33:01,160
Speaker 1: pulls in all of this data that they spend some

592
00:33:01,240 --> 00:33:03,719
Speaker 1: time to build. But now it's because they don't want

593
00:33:03,760 --> 00:33:06,160
Speaker 1: to have to keep doing that same thing over and

594
00:33:06,240 --> 00:33:10,680
Speaker 1: over and over and over again. It becomes redundant and monotonous.

595
00:33:10,680 --> 00:33:12,280
Speaker 1: And then you start to like, I don't want to

596
00:33:12,280 --> 00:33:14,120
Speaker 1: do this no more. I wish there was a faster way.

597
00:33:14,160 --> 00:33:17,520
Speaker 1: And then that macro, which would be five cast, is

598
00:33:17,560 --> 00:33:20,200
Speaker 1: your macro in those agencies. I would think that you

599
00:33:20,200 --> 00:33:22,520
Speaker 1: guys would be the macro button that an agency would

600
00:33:22,520 --> 00:33:24,440
Speaker 1: want to reach out to you and say, hey, can

601
00:33:24,480 --> 00:33:26,080
Speaker 1: you come help us? And then you guys have all

602
00:33:26,080 --> 00:33:29,680
Speaker 1: the people who make the macros if you will, you know,

603
00:33:29,760 --> 00:33:33,560
Speaker 1: these shortcuts and these spreadsheets and these algorithms and aggregate

604
00:33:33,640 --> 00:33:36,480
Speaker 1: things in meetings, and you know, have all these whiteboard

605
00:33:36,560 --> 00:33:42,360
Speaker 1: conversations about hopefully I'm just assuming, right, And so yeah,

606
00:33:42,440 --> 00:33:45,400
Speaker 1: sounds like you guys are the kind of I don't.

607
00:33:45,200 --> 00:33:48,800
Speaker 2: Know, I like that the macro makers of ocent.

608
00:33:48,720 --> 00:33:52,520
Speaker 1: That's five cast, that's five cast. Yeah, you know, you

609
00:33:52,560 --> 00:33:55,440
Speaker 1: want a simple way. Yeah, you want to have a

610
00:33:55,440 --> 00:33:57,480
Speaker 1: auto reply on your email that you're on vacation, or

611
00:33:57,520 --> 00:33:58,880
Speaker 1: you want to reply every single time.

612
00:34:00,240 --> 00:34:02,800
Speaker 2: And I think something that really drew me to five

613
00:34:02,920 --> 00:34:06,640
Speaker 2: casts is that you talked about, like, you know, these

614
00:34:06,680 --> 00:34:10,319
Speaker 2: whiteboard conversations and trying to solve the problem of I

615
00:34:10,320 --> 00:34:13,320
Speaker 2: don't want to do this no more like the signature.

616
00:34:13,800 --> 00:34:17,000
Speaker 2: The reason I really enjoyed five casts is it's a

617
00:34:17,040 --> 00:34:20,040
Speaker 2: company of people who are doing that every single day.

618
00:34:20,280 --> 00:34:23,320
Speaker 2: They're like, what what are our analysts struggling with today?

619
00:34:23,600 --> 00:34:25,279
Speaker 2: What do they have to do over and over and

620
00:34:25,320 --> 00:34:29,520
Speaker 2: over again that we can save them time from doing?

621
00:34:30,160 --> 00:34:32,799
Speaker 2: Or what is this really challenging piece of data that

622
00:34:32,800 --> 00:34:36,040
Speaker 2: they can't even access unless they have some sort of solution,

623
00:34:36,320 --> 00:34:39,399
Speaker 2: Or what's this really challenging bit of analysis that they

624
00:34:39,600 --> 00:34:42,520
Speaker 2: want to do and it's not possible unless there's a

625
00:34:42,560 --> 00:34:44,759
Speaker 2: tool to help them do it. We have a whole team,

626
00:34:44,800 --> 00:34:47,640
Speaker 2: like the majority of five Casts is made up of engineers,

627
00:34:47,960 --> 00:34:52,239
Speaker 2: data scientists just doing that the macro making that you describe.

628
00:34:52,400 --> 00:34:56,080
Speaker 2: We're just sitting there like, you know, my role, I'm

629
00:34:56,120 --> 00:34:59,800
Speaker 2: a tradecraft advisor. I lead our tradecraft team in the US,

630
00:35:00,040 --> 00:35:02,839
Speaker 2: and I like to think of us as just communicating

631
00:35:02,960 --> 00:35:05,799
Speaker 2: the really cool things that our data scientists and our

632
00:35:05,840 --> 00:35:09,920
Speaker 2: developers are doing day and day out, and that is

633
00:35:09,960 --> 00:35:13,799
Speaker 2: they're just sitting there scratching their head about what is

634
00:35:13,880 --> 00:35:17,680
Speaker 2: making the analyst sic Today and let's try and remove

635
00:35:17,719 --> 00:35:20,839
Speaker 2: that from their day to day tasks. So, yeah, that's

636
00:35:21,200 --> 00:35:22,560
Speaker 2: a lot of what five cast does.

637
00:35:23,320 --> 00:35:26,000
Speaker 1: How to make it better and better and better because

638
00:35:26,040 --> 00:35:29,879
Speaker 1: they get it to a simplistic way to figure things out,

639
00:35:29,880 --> 00:35:32,680
Speaker 1: and then how do they make that even more simplistic

640
00:35:33,040 --> 00:35:35,920
Speaker 1: and how do they make that even more integrated with

641
00:35:36,280 --> 00:35:39,880
Speaker 1: another feature that can just pull in that data to

642
00:35:40,200 --> 00:35:42,200
Speaker 1: maybe give you three of the things you need in

643
00:35:42,239 --> 00:35:45,840
Speaker 1: one macro button versus the one macro. I love that

644
00:35:46,000 --> 00:35:48,880
Speaker 1: concept of just the growth. And you guys have an

645
00:35:48,880 --> 00:35:53,360
Speaker 1: ever changing evolving mindset, right, you have to everybody's like

646
00:35:53,440 --> 00:35:55,680
Speaker 1: working and just getting it done and trying to figure

647
00:35:55,680 --> 00:35:58,799
Speaker 1: out what analysts need. So you guys have to have

648
00:35:58,880 --> 00:36:03,120
Speaker 1: like probably four former detectives and like analysts working for

649
00:36:03,160 --> 00:36:05,839
Speaker 1: you to like give you this insight. Right, you guys

650
00:36:05,840 --> 00:36:10,640
Speaker 1: are like, it can't just be me x skateboarder snowboarder, Like,

651
00:36:10,760 --> 00:36:13,640
Speaker 1: let's do analysts stuff, right, I mean I could I'm.

652
00:36:13,520 --> 00:36:14,440
Speaker 2: Sure you bring you pressure.

653
00:36:15,440 --> 00:36:20,759
Speaker 1: Yeah, just think about the gum, the bubble gum. It

654
00:36:20,800 --> 00:36:22,520
Speaker 1: was the bubble gum, right, that's off Colombo.

655
00:36:23,040 --> 00:36:25,879
Speaker 2: That's actually my team. So the tradecraft team at five

656
00:36:26,000 --> 00:36:32,319
Speaker 2: Casts is entirely comprised of former open source intelligence specialists,

657
00:36:32,920 --> 00:36:40,160
Speaker 2: former intelligence analysts, former investigators. So our team sits in

658
00:36:40,200 --> 00:36:44,200
Speaker 2: the function of trying to help our end users get

659
00:36:44,200 --> 00:36:47,000
Speaker 2: the most value out of these products, and then helping

660
00:36:47,120 --> 00:36:50,880
Speaker 2: our engineers and data scientists understand what's making the analysts

661
00:36:50,880 --> 00:36:54,760
Speaker 2: and the investigators sick today. So again work chief communicators

662
00:36:54,840 --> 00:36:58,640
Speaker 2: like our role is just helping meeke sense of the

663
00:36:58,680 --> 00:37:02,640
Speaker 2: traditional challenges and the new novel challenges that are being

664
00:37:02,719 --> 00:37:05,680
Speaker 2: faced in the open source intelligence space, but also in

665
00:37:05,719 --> 00:37:10,000
Speaker 2: the analytical space, so that our users can better leverage

666
00:37:10,040 --> 00:37:13,040
Speaker 2: the five CAST solutions, which are just helping them access

667
00:37:13,040 --> 00:37:16,200
Speaker 2: that ocent in a better and more seamless way. And

668
00:37:16,239 --> 00:37:19,880
Speaker 2: then also the developers, we sit and work with our developers.

669
00:37:19,920 --> 00:37:22,000
Speaker 2: We had a meeting earlier this week where we just

670
00:37:22,040 --> 00:37:25,440
Speaker 2: sit and brainstorm how to better map out a feature

671
00:37:25,520 --> 00:37:29,640
Speaker 2: so that it is seamless and an analyst is not

672
00:37:29,680 --> 00:37:32,480
Speaker 2: going to have to spend more of their time learning

673
00:37:32,480 --> 00:37:34,960
Speaker 2: a new solution and a new way of doing things.

674
00:37:35,000 --> 00:37:37,759
Speaker 2: It's the easy button, it's the macro. So we do

675
00:37:37,800 --> 00:37:40,400
Speaker 2: a lot of that. And the reason we're employed, to

676
00:37:40,480 --> 00:37:44,360
Speaker 2: your point, is we get it. We get what creates

677
00:37:44,360 --> 00:37:47,160
Speaker 2: those headaches. We get what the analysts probably don't want

678
00:37:47,160 --> 00:37:49,160
Speaker 2: to be doing, and if we don't understand, we get

679
00:37:49,160 --> 00:37:51,600
Speaker 2: to sit and listen to them tell us their headaches,

680
00:37:51,800 --> 00:37:55,480
Speaker 2: and we have a special empathy for what they're going through,

681
00:37:55,840 --> 00:37:58,520
Speaker 2: and then we can go champion those changes back to

682
00:37:58,560 --> 00:38:02,440
Speaker 2: the developers and the data science. And sometimes it's really specific,

683
00:38:02,680 --> 00:38:05,320
Speaker 2: like they need this data and they need it faster,

684
00:38:05,600 --> 00:38:08,440
Speaker 2: and they need it to look exactly like this. Sometimes

685
00:38:08,440 --> 00:38:11,560
Speaker 2: it's very specific and I outline the challenge really clearly,

686
00:38:12,000 --> 00:38:15,040
Speaker 2: and then other times I'm going back to the data scientists,

687
00:38:15,040 --> 00:38:19,080
Speaker 2: for example, with a really novel question, and they get

688
00:38:19,080 --> 00:38:23,080
Speaker 2: to sit and just play with how to solve that problem,

689
00:38:23,080 --> 00:38:28,319
Speaker 2: which I admire because it'll be something really huge, like

690
00:38:28,800 --> 00:38:33,560
Speaker 2: how do we better locate information campaigns spreading through social media?

691
00:38:34,200 --> 00:38:36,719
Speaker 2: And our data scientists will say, let's just sit here

692
00:38:36,840 --> 00:38:39,520
Speaker 2: noodle on this as long as we want, and so

693
00:38:39,600 --> 00:38:42,120
Speaker 2: we come to the good solution and we're actually rolling

694
00:38:42,160 --> 00:38:44,640
Speaker 2: out a really cool feature shortly that's going to help

695
00:38:45,320 --> 00:38:50,319
Speaker 2: identifying narratives spreading throughout certain online communities better so that

696
00:38:51,000 --> 00:38:55,279
Speaker 2: when we see dangerous things like miss disinformation, so the

697
00:38:55,360 --> 00:38:59,840
Speaker 2: purposeful manipulation of online data or placement of false information,

698
00:39:00,160 --> 00:39:03,400
Speaker 2: we're able to locate that. Because sometimes just as important

699
00:39:03,400 --> 00:39:06,560
Speaker 2: as finding the critical piece of real information is finding

700
00:39:07,080 --> 00:39:10,439
Speaker 2: the really important fake information out there, so we don't

701
00:39:10,480 --> 00:39:13,239
Speaker 2: make decisions on that right. So we're coming up with

702
00:39:13,320 --> 00:39:15,319
Speaker 2: tools in that area too, and that's all thanks to

703
00:39:15,840 --> 00:39:19,360
Speaker 2: asking some really big, open ended questions to our data scientists.

704
00:39:21,200 --> 00:39:24,479
Speaker 1: Yeah, it's just wild because I'm just thinking of all

705
00:39:24,480 --> 00:39:26,760
Speaker 1: these different things in my head as you're talking about,

706
00:39:26,800 --> 00:39:30,040
Speaker 1: like you know how everybody just leaves a trail everywhere

707
00:39:30,040 --> 00:39:33,680
Speaker 1: they go of just intelligence that you know, you may

708
00:39:33,680 --> 00:39:36,920
Speaker 1: not think you're giving it, but somebody can gather it

709
00:39:36,960 --> 00:39:40,320
Speaker 1: from your photo the background that they wanted to see,

710
00:39:40,680 --> 00:39:42,360
Speaker 1: and they're just like, hey, show me pictures of the

711
00:39:42,360 --> 00:39:45,440
Speaker 1: mountains in Utah. There's like some algorithm that they just

712
00:39:45,480 --> 00:39:49,120
Speaker 1: go and find photos of Utah and next thing you know,

713
00:39:49,120 --> 00:39:51,160
Speaker 1: you see these mountains and then it's just it's just

714
00:39:51,200 --> 00:39:53,760
Speaker 1: like drilling it down onto what you want to find

715
00:39:54,160 --> 00:39:56,440
Speaker 1: and it's already out there, right. You can click on

716
00:39:56,480 --> 00:39:59,040
Speaker 1: a picture, you can write click a picture and you

717
00:39:59,040 --> 00:40:01,120
Speaker 1: can find that proper he's of a picture. You find

718
00:40:01,160 --> 00:40:04,120
Speaker 1: that the picture's geo location tag. And people are just

719
00:40:04,160 --> 00:40:06,399
Speaker 1: not even thinking. It's just right there.

720
00:40:06,760 --> 00:40:12,120
Speaker 2: It's just right now, It's just right there.

721
00:40:12,239 --> 00:40:16,000
Speaker 1: It's just right there. Like like, somebody sent me a

722
00:40:16,000 --> 00:40:18,440
Speaker 1: picture they are sick, and they sent me a COVID

723
00:40:18,480 --> 00:40:21,280
Speaker 1: picture of their like test and it had a thumb

724
00:40:21,320 --> 00:40:24,000
Speaker 1: holding the thing. And I was like, I wonder if

725
00:40:24,040 --> 00:40:27,600
Speaker 1: that's their thumb holding the test strip, right, And I'm cool,

726
00:40:27,600 --> 00:40:29,640
Speaker 1: we're funny about it. And this is a while ago.

727
00:40:29,680 --> 00:40:32,120
Speaker 1: And so my buddy's like, well, Rad, check this out, man,

728
00:40:32,120 --> 00:40:35,120
Speaker 1: and he hit the picture, put it in it said

729
00:40:35,160 --> 00:40:38,120
Speaker 1: show me this picture. He pulled up all these other same,

730
00:40:38,400 --> 00:40:42,319
Speaker 1: similar pictures of the same test with a thumb in

731
00:40:42,360 --> 00:40:45,040
Speaker 1: it holding it. And what we did was we just

732
00:40:45,080 --> 00:40:47,680
Speaker 1: went with the same photo against all of the other

733
00:40:47,719 --> 00:40:50,040
Speaker 1: photos for the thumb. And I was like, dude, if

734
00:40:50,040 --> 00:40:52,400
Speaker 1: that thumb is in these photos, I'm gonna be pissed

735
00:40:53,239 --> 00:41:00,120
Speaker 1: because he's sending me a stock image. Yeah, bro, wait oh,

736
00:41:00,120 --> 00:41:02,560
Speaker 1: Because we're pretty easy going, you know, I'm like Grey's

737
00:41:02,560 --> 00:41:04,520
Speaker 1: six stay home. You know, come on, I love you

738
00:41:04,640 --> 00:41:07,320
Speaker 1: ninety percent love you, ten percent have to fire you.

739
00:41:07,320 --> 00:41:11,239
Speaker 1: You guys know that. Okay, all right? But yeah, yeah,

740
00:41:11,239 --> 00:41:13,319
Speaker 1: they ninety percent love me, but they ten percent might

741
00:41:13,360 --> 00:41:15,879
Speaker 1: have to quit. So we have this relationship dynamic at

742
00:41:15,880 --> 00:41:17,600
Speaker 1: the shops where I work at Kay, It's like we

743
00:41:17,640 --> 00:41:19,880
Speaker 1: love each other, but there's a thing we might have

744
00:41:19,960 --> 00:41:23,160
Speaker 1: to do something to each other so to keep it

745
00:41:23,200 --> 00:41:26,319
Speaker 1: real and like you know, professional, but again, man, wow,

746
00:41:26,480 --> 00:41:28,279
Speaker 1: imagine like I just thought, oh, hey, check out all

747
00:41:28,320 --> 00:41:30,239
Speaker 1: my social media here. Check it out here, We're playing

748
00:41:30,239 --> 00:41:32,080
Speaker 1: war games out in the desert. Here, check out my tank.

749
00:41:32,200 --> 00:41:35,239
Speaker 1: Check out all this stuff. It's like twenty years later

750
00:41:35,280 --> 00:41:37,040
Speaker 1: there's a diary of rat.

751
00:41:37,600 --> 00:41:41,879
Speaker 2: Yeah, all available online. And I think, I mean, you're

752
00:41:41,920 --> 00:41:45,680
Speaker 2: hitting on you know, the part where sometimes what we're

753
00:41:45,760 --> 00:41:49,200
Speaker 2: educating people on is look at this really scary stuff

754
00:41:49,239 --> 00:41:51,799
Speaker 2: that we can do. So not only almost as like

755
00:41:51,800 --> 00:41:54,439
Speaker 2: a red teeming exercise, We're like, here's what you don't

756
00:41:54,480 --> 00:41:57,320
Speaker 2: want to do, because here's what we were able to find.

757
00:41:57,680 --> 00:41:57,880
Speaker 1: You know.

758
00:41:57,920 --> 00:42:02,239
Speaker 2: Sometimes we're educating on that angle of open source intelligence,

759
00:42:02,760 --> 00:42:06,280
Speaker 2: and then other times where yeah, we're showing the shocking

760
00:42:06,320 --> 00:42:09,000
Speaker 2: reality of this is right, you know, here's what it's

761
00:42:09,040 --> 00:42:12,280
Speaker 2: out here, and I actually you just brought up something

762
00:42:12,320 --> 00:42:15,320
Speaker 2: really cool that I think is unique about five casts

763
00:42:16,080 --> 00:42:20,080
Speaker 2: is the ability to do media and analytics at a

764
00:42:20,120 --> 00:42:25,800
Speaker 2: really advanced rate, and specifically doing image and logo detection.

765
00:42:26,120 --> 00:42:30,080
Speaker 2: So that scenario of what you described, something I can

766
00:42:30,160 --> 00:42:35,160
Speaker 2: do is say there was a check that has been stolen,

767
00:42:35,640 --> 00:42:37,560
Speaker 2: and I need to see if there are any photos

768
00:42:37,600 --> 00:42:43,319
Speaker 2: of this check appearing online. I can actually run collections

769
00:42:43,360 --> 00:42:47,799
Speaker 2: against potential suspects and see if they've ever shared that

770
00:42:47,920 --> 00:42:52,240
Speaker 2: photo or they've shared instance of that type of concept.

771
00:42:52,280 --> 00:42:53,960
Speaker 2: I don't even have to have the exact image. I

772
00:42:53,960 --> 00:42:56,800
Speaker 2: can say I'm looking for checks, show me anytime a

773
00:42:56,920 --> 00:43:00,040
Speaker 2: checks popped up in here, and our advanced meet the

774
00:43:00,160 --> 00:43:02,880
Speaker 2: analytics are going to help us get to that. And

775
00:43:03,000 --> 00:43:05,160
Speaker 2: if you're ever scared by the way, speaking of checks,

776
00:43:05,400 --> 00:43:08,000
Speaker 2: I think it was a recent New York Times article

777
00:43:08,040 --> 00:43:13,360
Speaker 2: which talked about checkwashing and don't ever use paper checks anymore.

778
00:43:13,360 --> 00:43:16,000
Speaker 2: I don't think most people do. But it's a whole

779
00:43:16,080 --> 00:43:20,440
Speaker 2: business online now to steal checks, and they can actually

780
00:43:20,440 --> 00:43:23,880
Speaker 2: extract the ink off of them using variety of chemicals,

781
00:43:24,280 --> 00:43:27,279
Speaker 2: and they can now write in new amounts and they

782
00:43:27,400 --> 00:43:29,839
Speaker 2: now have a blank check from whoever wrote it, which

783
00:43:29,880 --> 00:43:33,640
Speaker 2: is pretty terrifying. But this has become a whole illicit

784
00:43:33,760 --> 00:43:37,759
Speaker 2: business online. This has become something where people are coordinating, cooperating,

785
00:43:37,840 --> 00:43:42,239
Speaker 2: collaborating online to do this at massive scales. And I

786
00:43:42,400 --> 00:43:45,400
Speaker 2: just recently had to use onyx to uncover these rings

787
00:43:45,400 --> 00:43:48,920
Speaker 2: of folks that are doing this at a massive scale

788
00:43:49,520 --> 00:43:53,239
Speaker 2: and figure out how onyx can not only find these

789
00:43:53,320 --> 00:43:57,200
Speaker 2: illicit communities that are collaborating and doing these things, but

790
00:43:57,320 --> 00:44:01,320
Speaker 2: also how do we pick up on maybe specific pieces

791
00:44:01,320 --> 00:44:02,920
Speaker 2: of evidence that I'm looking for if I want to

792
00:44:02,960 --> 00:44:06,120
Speaker 2: extract that specific stolen check that I'm looking for protect

793
00:44:06,480 --> 00:44:10,000
Speaker 2: XYZ individual that reported it's stolen. We're trying to do

794
00:44:10,040 --> 00:44:11,960
Speaker 2: it at you know, kind of the macro scale of

795
00:44:12,000 --> 00:44:14,719
Speaker 2: kind of find this threat, but also the really tailored

796
00:44:14,880 --> 00:44:17,560
Speaker 2: scale of I need this specific piece of evidence and

797
00:44:17,880 --> 00:44:21,560
Speaker 2: on its unfortunately unveiled all of this stuff to me.

798
00:44:21,680 --> 00:44:26,600
Speaker 2: So I've learned a lot about checkow and all of

799
00:44:26,640 --> 00:44:30,880
Speaker 2: that online. But yeah, image analytics are so critical because

800
00:44:31,600 --> 00:44:34,840
Speaker 2: I think something we would see earlier in osent is

801
00:44:34,920 --> 00:44:38,440
Speaker 2: people are really reliant on keyword detection. I'm sure, if

802
00:44:38,480 --> 00:44:41,000
Speaker 2: you were to look for something, or if the average

803
00:44:41,000 --> 00:44:43,560
Speaker 2: person were to look for something, they'd go to Google

804
00:44:43,920 --> 00:44:47,040
Speaker 2: and they type in words. That has been obviously made

805
00:44:47,040 --> 00:44:49,399
Speaker 2: a little bit more advanced, but people still really rely

806
00:44:49,520 --> 00:44:52,960
Speaker 2: on keyword searching. I think something I love about Onyx

807
00:44:53,040 --> 00:44:57,920
Speaker 2: is that because evidence nowadays sits in pictures, TikTok videos,

808
00:44:58,280 --> 00:45:01,080
Speaker 2: you know, things like that, you need to move into

809
00:45:01,120 --> 00:45:03,320
Speaker 2: that space of being able to pick up on threats

810
00:45:03,320 --> 00:45:08,400
Speaker 2: and other types of media, if you will. So, yeah,

811
00:45:08,440 --> 00:45:12,880
Speaker 2: there's illicit groups and how they're using TikTok videos to

812
00:45:12,920 --> 00:45:16,080
Speaker 2: just share evidence of check washing. That's like just one

813
00:45:16,120 --> 00:45:18,760
Speaker 2: small example of how we've automated a lot of this stuff.

814
00:45:18,800 --> 00:45:20,480
Speaker 2: So not only can you pick up on the crime,

815
00:45:20,840 --> 00:45:22,960
Speaker 2: but you can start putting that into your cases and

816
00:45:23,080 --> 00:45:27,480
Speaker 2: using it to stop people from stealing checks and making

817
00:45:27,520 --> 00:45:28,480
Speaker 2: them in blank checks.

818
00:45:29,239 --> 00:45:32,440
Speaker 1: Some of these musicians are out there showing all of

819
00:45:32,480 --> 00:45:35,440
Speaker 1: this like I got cash, I got cars, I got this,

820
00:45:35,600 --> 00:45:38,120
Speaker 1: I got that, and then you know, they're putting it

821
00:45:38,120 --> 00:45:40,040
Speaker 1: out there, and they're putting it out there and they're

822
00:45:40,080 --> 00:45:43,640
Speaker 1: either like being attacked or robbed for showing off all

823
00:45:43,680 --> 00:45:46,760
Speaker 1: of their stuff. And now they're like, you know, walking

824
00:45:46,840 --> 00:45:50,879
Speaker 1: with a five million dollar necklace. Somebody's itching for that, yeah,

825
00:45:51,000 --> 00:45:55,960
Speaker 1: or they're getting in trouble and indicted for you know,

826
00:45:56,120 --> 00:46:00,000
Speaker 1: running gangs and they think that their rap career just

827
00:46:00,120 --> 00:46:02,840
Speaker 1: took off. But they're showing all this other stuff and

828
00:46:02,840 --> 00:46:04,520
Speaker 1: I'm just saying it's out there on the Internet, and

829
00:46:04,600 --> 00:46:08,200
Speaker 1: people are just you know, grabbing it and just saying,

830
00:46:08,239 --> 00:46:10,359
Speaker 1: this is you know, all the rap songs. You're saying

831
00:46:10,440 --> 00:46:12,640
Speaker 1: about all these crimes that happened at the same time

832
00:46:12,640 --> 00:46:14,960
Speaker 1: that you wrote these rap songs and you put them

833
00:46:14,960 --> 00:46:17,920
Speaker 1: all out there. Yeah, these are that's an actual story

834
00:46:17,960 --> 00:46:19,640
Speaker 1: going on. I don't know the full details of it,

835
00:46:19,680 --> 00:46:22,480
Speaker 1: but this rappers and dieted out of Atlanta. So it's

836
00:46:22,520 --> 00:46:25,600
Speaker 1: like ninety eight indictments. I don't want none of that. No,

837
00:46:25,719 --> 00:46:26,920
Speaker 1: thank you, you know.

838
00:46:27,080 --> 00:46:30,759
Speaker 2: But something Yeah, I walk off my lines. I'm like, yeah,

839
00:46:31,000 --> 00:46:34,240
Speaker 2: people don't be very careful about what you post online.

840
00:46:34,280 --> 00:46:37,320
Speaker 2: But if you happen to be doing something illicit, don't

841
00:46:37,360 --> 00:46:38,480
Speaker 2: just share everything.

842
00:46:38,760 --> 00:46:42,200
Speaker 1: Like yeah, right ahead, I'm not trying to say my

843
00:46:42,280 --> 00:46:43,799
Speaker 1: listen it's not my listener.

844
00:46:43,520 --> 00:46:45,360
Speaker 2: No, none of your listeners.

845
00:46:46,360 --> 00:46:48,440
Speaker 1: No. They don't post nothing up on the No, they

846
00:46:48,440 --> 00:46:49,680
Speaker 1: don't incriminate.

847
00:46:49,239 --> 00:46:53,240
Speaker 2: Themselves, right, But I feel like you break something interesting

848
00:46:53,280 --> 00:46:57,799
Speaker 2: because another new trending topic in ocent nowadays is and

849
00:46:57,840 --> 00:47:00,319
Speaker 2: you bring up an important example of it. There are

850
00:47:00,360 --> 00:47:03,040
Speaker 2: a couple of reasons why people use social media. One

851
00:47:03,040 --> 00:47:06,160
Speaker 2: of them is to connect communicate, So from a criminal perspective,

852
00:47:06,640 --> 00:47:09,600
Speaker 2: you know, you can leverage that reliance on social media

853
00:47:09,640 --> 00:47:13,359
Speaker 2: to find a criminal network. Another reason is they are

854
00:47:13,440 --> 00:47:16,799
Speaker 2: running an illicit business. And for our business, you need customers.

855
00:47:17,280 --> 00:47:20,320
Speaker 2: How are you going to connect with customers? Everybody's communicating

856
00:47:20,320 --> 00:47:22,600
Speaker 2: online nowadays, right, so you're going to need to do that.

857
00:47:23,000 --> 00:47:25,719
Speaker 2: Another reason people use social media is to brag. It's

858
00:47:25,760 --> 00:47:28,919
Speaker 2: for the validation. It's people just want to post things

859
00:47:28,960 --> 00:47:32,000
Speaker 2: and feel really good about themselves. And you can rely

860
00:47:32,080 --> 00:47:36,479
Speaker 2: on that habit when you're thinking of folks maybe leaving

861
00:47:36,520 --> 00:47:41,040
Speaker 2: behind evidence of you know, maybe those funds weren't realistic

862
00:47:41,160 --> 00:47:45,920
Speaker 2: for you to have as a public service employee, or

863
00:47:46,239 --> 00:47:49,480
Speaker 2: you're investigating somebody for fraud, people leave behind.

864
00:47:49,560 --> 00:47:52,720
Speaker 1: Everybody carry a birken bag on a on a government jet.

865
00:47:53,080 --> 00:47:53,759
Speaker 2: Yeah, what's going on?

866
00:47:53,840 --> 00:47:55,480
Speaker 1: You know, and take pictures of that, you know, and

867
00:47:55,520 --> 00:47:59,359
Speaker 1: put it online. Hey, doesn't everybody? Isn't that a normal thing?

868
00:47:59,680 --> 00:47:59,879
Speaker 2: Yeah?

869
00:48:00,120 --> 00:48:02,400
Speaker 1: You're totally right. It's like out there, you like just

870
00:48:02,480 --> 00:48:03,560
Speaker 1: incriminated yourself.

871
00:48:03,920 --> 00:48:06,279
Speaker 2: Yeah, and of course, like you're only getting looked at

872
00:48:06,280 --> 00:48:09,440
Speaker 2: if there's some reasonable suspicion in the first place. But

873
00:48:09,920 --> 00:48:12,920
Speaker 2: and so you'd think, you know, somebody who is doing

874
00:48:13,320 --> 00:48:17,400
Speaker 2: criminal activities, you think they would clean up that online footprint.

875
00:48:17,480 --> 00:48:20,400
Speaker 2: But no, like they even even if they know, like

876
00:48:20,440 --> 00:48:23,200
Speaker 2: they've been arrested or charged with or whatever, a lot

877
00:48:23,200 --> 00:48:26,080
Speaker 2: of times you can still go to their online footprint

878
00:48:26,239 --> 00:48:30,359
Speaker 2: and see just litters of evidence for whatever they've been

879
00:48:30,400 --> 00:48:33,719
Speaker 2: charged with. So in that sense, oh, say, it's great

880
00:48:34,080 --> 00:48:37,719
Speaker 2: because it's a cheap, spective way to find evidence, to

881
00:48:37,760 --> 00:48:40,560
Speaker 2: find that intelligence. It's going to help you make a decision.

882
00:48:41,080 --> 00:48:44,360
Speaker 2: And it doesn't take what like curating a human source.

883
00:48:44,480 --> 00:48:46,960
Speaker 2: You know, it doesn't take like putting boots on the

884
00:48:47,000 --> 00:48:49,600
Speaker 2: ground to figure that out. You know, it takes a

885
00:48:49,680 --> 00:48:51,080
Speaker 2: quick search online.

886
00:48:51,600 --> 00:48:56,600
Speaker 1: And I'm just thinking about Google. They yeah, well, yeah,

887
00:48:56,600 --> 00:48:58,719
Speaker 1: I got this Google thing. It's like, oh, uh, you

888
00:48:58,760 --> 00:49:02,440
Speaker 1: didn't update Google missed an update of your travels yesterday.

889
00:49:02,440 --> 00:49:04,719
Speaker 1: And I was like, well, and I know I'm a

890
00:49:04,719 --> 00:49:07,480
Speaker 1: Google guide okay, And this isn't like a promotion for them.

891
00:49:07,520 --> 00:49:10,000
Speaker 1: I'm just like, oh, damn, dude, I'm tracked everywhere I go.

892
00:49:10,040 --> 00:49:11,879
Speaker 1: If I go into this restaurant, it's like, hey, you're there,

893
00:49:12,360 --> 00:49:14,200
Speaker 1: will you post up a picture of what you're eating?

894
00:49:14,239 --> 00:49:15,680
Speaker 1: And I'm like, yeah, for sure, and I post it,

895
00:49:15,719 --> 00:49:19,799
Speaker 1: you know, and you know, eleven million views later, you know,

896
00:49:19,920 --> 00:49:23,160
Speaker 1: there's where rat eight At the time, I ate whose

897
00:49:23,160 --> 00:49:25,640
Speaker 1: hands were in the photo? Maybe my wife's are friends

898
00:49:25,640 --> 00:49:27,560
Speaker 1: and it can just start to be broken down right there.

899
00:49:27,600 --> 00:49:29,920
Speaker 1: But I'm just trying to I'm thinking of it innocently,

900
00:49:29,960 --> 00:49:33,200
Speaker 1: you know. But at the end of the day, it's, uh,

901
00:49:33,360 --> 00:49:35,560
Speaker 1: it's the Internet, man. We got to watch what we're doing.

902
00:49:35,560 --> 00:49:36,400
Speaker 1: You gotta be careful.

903
00:49:36,480 --> 00:49:38,759
Speaker 2: But in that sense, like I think one of the

904
00:49:38,800 --> 00:49:43,359
Speaker 2: best ways to train folks on open source intelligence, it's

905
00:49:43,440 --> 00:49:48,160
Speaker 2: just specifically social media and it actually has its own

906
00:49:48,280 --> 00:49:51,160
Speaker 2: name now, socment social media intelligence.

907
00:49:51,320 --> 00:49:57,480
Speaker 1: Oh that was the thing that's coming. Yeah, sock it

908
00:49:57,560 --> 00:49:57,840
Speaker 1: to me.

909
00:49:58,000 --> 00:50:01,120
Speaker 2: Yeah, So that's its own thing now. And one of

910
00:50:01,160 --> 00:50:05,520
Speaker 2: the best educators is just reflecting like you are now, Like,

911
00:50:05,800 --> 00:50:08,520
Speaker 2: how am I using all of these tools and online

912
00:50:08,520 --> 00:50:12,800
Speaker 2: platforms and ways to communicate and connect, which are all

913
00:50:12,840 --> 00:50:15,840
Speaker 2: built you know, to help people. They're all built to

914
00:50:15,920 --> 00:50:19,600
Speaker 2: help people communicate, collaborate, all these you know, innocent, perfectly

915
00:50:19,640 --> 00:50:22,600
Speaker 2: lovely things. But if you reflect on it and you think, Okay,

916
00:50:22,640 --> 00:50:25,719
Speaker 2: if I was a bad person, I'm either still doing

917
00:50:25,800 --> 00:50:28,040
Speaker 2: those things because I saw friends and I still want

918
00:50:28,040 --> 00:50:30,960
Speaker 2: to talk to people, and I'm just doing that and

919
00:50:31,000 --> 00:50:34,040
Speaker 2: that might give me critical evidence. But also if I'm

920
00:50:34,040 --> 00:50:37,800
Speaker 2: an a lisit actor, those tools can serve illicit purposes

921
00:50:37,840 --> 00:50:41,200
Speaker 2: if I manipulate them slightly. So, like going back to

922
00:50:41,239 --> 00:50:44,600
Speaker 2: that chech example, you know when I was investigating checkwashing,

923
00:50:44,960 --> 00:50:47,399
Speaker 2: you know, they wanted to sell these checks that they've

924
00:50:47,440 --> 00:50:50,600
Speaker 2: been stealing from the mail. They ultimately need to sell them.

925
00:50:50,760 --> 00:50:52,759
Speaker 2: How are you going to sell them? Like? What are

926
00:50:52,760 --> 00:50:55,959
Speaker 2: you going to go house house like offering listed checks?

927
00:50:56,000 --> 00:50:58,880
Speaker 2: Like what are you gonna do? No, you're gonna go online.

928
00:50:58,960 --> 00:51:02,000
Speaker 2: And people suspect that this is going to be in

929
00:51:02,040 --> 00:51:05,800
Speaker 2: the dark web, which sometimes it is, but it doesn't

930
00:51:05,840 --> 00:51:08,239
Speaker 2: know that's not where the majority of your business is

931
00:51:08,280 --> 00:51:10,520
Speaker 2: going to sit. The majority of folks don't use the

932
00:51:10,560 --> 00:51:12,680
Speaker 2: dark web. They're going to go to social media, where

933
00:51:12,719 --> 00:51:14,600
Speaker 2: a lot of folks are. They're going to use really

934
00:51:14,680 --> 00:51:18,320
Speaker 2: clear hashtags, they're going to use really clear photos and

935
00:51:18,360 --> 00:51:20,279
Speaker 2: they're going to try and hide themselves a little bit,

936
00:51:20,320 --> 00:51:22,080
Speaker 2: but they still want to make their business. They still

937
00:51:22,080 --> 00:51:25,640
Speaker 2: want to build their business. And you know, I'm using

938
00:51:25,719 --> 00:51:29,120
Speaker 2: a law enforcement type example, but the same ghosts for

939
00:51:30,280 --> 00:51:33,080
Speaker 2: are you know our listeners are likely from the defense space.

940
00:51:33,160 --> 00:51:36,400
Speaker 2: You know, same goes for if you know you are

941
00:51:36,440 --> 00:51:39,440
Speaker 2: looking for evidence of TRUP movement, if you are looking

942
00:51:39,440 --> 00:51:43,720
Speaker 2: for evidence of certain you know, weapons capabilities in a region,

943
00:51:44,520 --> 00:51:47,719
Speaker 2: those same photos are either being captured by just an

944
00:51:47,760 --> 00:51:50,120
Speaker 2: average person who happened to see it and it looked

945
00:51:50,160 --> 00:51:52,040
Speaker 2: really cool and they wanted to share that photo with

946
00:51:52,080 --> 00:51:56,640
Speaker 2: their friends, or unintentionally that is being captured by maybe

947
00:51:56,680 --> 00:51:59,600
Speaker 2: people assisting with the Truth movement or assisting with the

948
00:51:59,640 --> 00:52:02,560
Speaker 2: resource movement. We saw that a lot with a Russian

949
00:52:02,560 --> 00:52:05,759
Speaker 2: troop movement, where you have soldiers like taking selfies and

950
00:52:05,800 --> 00:52:09,080
Speaker 2: posting them online and you have you even see that

951
00:52:09,200 --> 00:52:13,000
Speaker 2: in their attempt to inform and gather awareness, like with

952
00:52:13,320 --> 00:52:17,120
Speaker 2: specifically a gaza, you saw hamas they spurned out a

953
00:52:17,120 --> 00:52:20,040
Speaker 2: whole bunch of telegram channels and they want people to

954
00:52:20,080 --> 00:52:22,680
Speaker 2: know where their successful strikes were. They want people to

955
00:52:22,640 --> 00:52:25,840
Speaker 2: know where they were looking to actively attack. They wanted

956
00:52:25,840 --> 00:52:29,120
Speaker 2: people to know where their tunnels were that weren't enabling

957
00:52:29,160 --> 00:52:33,600
Speaker 2: them to get around and perform really horrific things. I think,

958
00:52:33,840 --> 00:52:38,240
Speaker 2: you know, those same principles of just wanting to gather

959
00:52:38,360 --> 00:52:42,880
Speaker 2: support or wanting to gain awareness, which is how you

960
00:52:42,920 --> 00:52:45,279
Speaker 2: know social media is used for positive things, it's going

961
00:52:45,320 --> 00:52:47,800
Speaker 2: to be used by illicit or threat actors as well,

962
00:52:48,280 --> 00:52:51,719
Speaker 2: and so that really that act of view just reflecting

963
00:52:51,760 --> 00:52:54,840
Speaker 2: on oh I posted that, you know Google review the

964
00:52:54,920 --> 00:52:57,960
Speaker 2: other day. You know, if that's even just doing that,

965
00:52:57,960 --> 00:53:00,719
Speaker 2: that's what osan analysts are doing all this time. They're like,

966
00:53:00,840 --> 00:53:03,440
Speaker 2: how just reflecting back on how maybe they use it,

967
00:53:03,440 --> 00:53:07,879
Speaker 2: how they've seen social media used positively, and thinking how

968
00:53:07,920 --> 00:53:11,560
Speaker 2: could that be manipulated in a really horrible way, and

969
00:53:11,600 --> 00:53:13,160
Speaker 2: seeing what information you can find.

970
00:53:13,920 --> 00:53:16,880
Speaker 1: You know, I work outside of a base, a military

971
00:53:16,920 --> 00:53:20,080
Speaker 1: air base, and all day I see jets and in

972
00:53:20,080 --> 00:53:22,560
Speaker 1: my whole life, I've been seeing these same jets, different

973
00:53:22,600 --> 00:53:24,640
Speaker 1: aircraft all the time, and I love it. And I'm

974
00:53:24,680 --> 00:53:27,360
Speaker 1: a photographer, so I have my social media and I'm like,

975
00:53:27,640 --> 00:53:30,239
Speaker 1: you know, oh, they're flying. But the other like one

976
00:53:30,239 --> 00:53:32,280
Speaker 1: of the other days I heard this is like really

977
00:53:32,360 --> 00:53:35,200
Speaker 1: heavy plane taken off, not normal sound right? And I

978
00:53:35,200 --> 00:53:37,399
Speaker 1: went outside, opened up my double doors, and I look

979
00:53:37,480 --> 00:53:39,840
Speaker 1: right up there and it's huge and it's big, and

980
00:53:39,880 --> 00:53:42,400
Speaker 1: I've never seen the name. And I was filming it

981
00:53:42,440 --> 00:53:45,520
Speaker 1: with my social media at my Instagram, and I was like, hey,

982
00:53:45,520 --> 00:53:49,440
Speaker 1: who knows what this is? And and I didn't even know.

983
00:53:49,480 --> 00:53:51,440
Speaker 1: It seemed like a foreign plane and someone's like, oh,

984
00:53:51,520 --> 00:53:54,440
Speaker 1: rat that plane is going to Ukraine flying from this

985
00:53:54,560 --> 00:53:56,640
Speaker 1: and it's this model and it's a snake and here's

986
00:53:56,719 --> 00:54:04,399
Speaker 1: it's itinerary flight path. And I was like, oh, I

987
00:54:04,440 --> 00:54:07,360
Speaker 1: was just trying to show something fun and something cool

988
00:54:07,640 --> 00:54:09,120
Speaker 1: that I don't usually see.

989
00:54:09,200 --> 00:54:09,560
Speaker 2: But then I.

990
00:54:09,560 --> 00:54:12,600
Speaker 1: Stopped and thought about it after I got that from him,

991
00:54:12,600 --> 00:54:14,799
Speaker 1: and I stopped posting those photos.

992
00:54:15,040 --> 00:54:19,919
Speaker 2: Thank you are those videos.

993
00:54:18,360 --> 00:54:21,360
Speaker 1: But I still video the F thirty fives because everybody

994
00:54:21,360 --> 00:54:23,000
Speaker 1: knows we got f thirty fives and they do their

995
00:54:23,040 --> 00:54:25,680
Speaker 1: little loopy loops. But I sat back for a second

996
00:54:25,719 --> 00:54:28,239
Speaker 1: and I just said to myself, Well, if this dude

997
00:54:28,280 --> 00:54:32,720
Speaker 1: who I don't know can show me the make, model

998
00:54:32,880 --> 00:54:35,480
Speaker 1: and then number of this aircraft that I didn't know

999
00:54:35,880 --> 00:54:38,160
Speaker 1: and the flight path, well then what else are they

1000
00:54:38,160 --> 00:54:39,480
Speaker 1: watching on me? When I post up?

1001
00:54:39,760 --> 00:54:40,000
Speaker 2: Yeah?

1002
00:54:40,080 --> 00:54:42,320
Speaker 1: You know? Am I you know? And so loose lips

1003
00:54:42,360 --> 00:54:44,560
Speaker 1: seeing ships. So I took it upon myself to just

1004
00:54:44,640 --> 00:54:47,400
Speaker 1: try not to photo the crazy. I'll look at it

1005
00:54:47,440 --> 00:54:49,719
Speaker 1: with my own eyes, enjoy.

1006
00:54:49,520 --> 00:54:50,480
Speaker 2: The view for yourself.

1007
00:54:50,680 --> 00:54:53,640
Speaker 1: And then with all the aliens that fly the f

1008
00:54:53,680 --> 00:54:55,560
Speaker 1: thirty fives. Man, I'm gonna film you all day because

1009
00:54:55,600 --> 00:54:57,360
Speaker 1: you're all a bunch of aliens flying around my shop.

1010
00:54:57,400 --> 00:54:58,879
Speaker 1: I don't even know. Like they sit there and they're

1011
00:54:58,920 --> 00:55:00,680
Speaker 1: like in this, they fly straight and then they just

1012
00:55:00,719 --> 00:55:02,480
Speaker 1: go like this and they're just like their engines are

1013
00:55:02,520 --> 00:55:04,960
Speaker 1: down and they're flying slowly in front of us. You know,

1014
00:55:05,239 --> 00:55:07,759
Speaker 1: you know, you grew up on a base, so.

1015
00:55:08,080 --> 00:55:11,280
Speaker 2: What you raise a good point too. Like again, like Osin,

1016
00:55:11,400 --> 00:55:13,520
Speaker 2: I think when we talk about it, it ends we

1017
00:55:13,600 --> 00:55:16,200
Speaker 2: end up taking the role of, you know, educating folks

1018
00:55:16,239 --> 00:55:20,040
Speaker 2: on how they can better find, you know, intelligence or information,

1019
00:55:20,160 --> 00:55:22,960
Speaker 2: whether that's for a case or for an intelligence product.

1020
00:55:23,120 --> 00:55:25,520
Speaker 2: But then also it's like the scare tactic of if

1021
00:55:25,520 --> 00:55:27,759
Speaker 2: we're able to do this for bad people, now you

1022
00:55:27,880 --> 00:55:32,560
Speaker 2: know what to avoid personally, Like it's a good exercise.

1023
00:55:32,600 --> 00:55:34,839
Speaker 2: And for you it took that comment or that bit

1024
00:55:34,840 --> 00:55:37,080
Speaker 2: of feedback from someone where you're like, wait a minute,

1025
00:55:37,800 --> 00:55:40,239
Speaker 2: I don't want to just hand that out to everybody publicly.

1026
00:55:40,840 --> 00:55:43,279
Speaker 2: So yeah, like be careful even like things that seem

1027
00:55:43,400 --> 00:55:48,360
Speaker 2: super innocuous, like sharing that you are somewhere. Like something

1028
00:55:48,360 --> 00:55:50,080
Speaker 2: that you would see quite a bit is if folks

1029
00:55:50,160 --> 00:55:53,239
Speaker 2: are deployed, they have their cell phones with them and

1030
00:55:53,280 --> 00:55:55,279
Speaker 2: they might post where they are and they might share

1031
00:55:55,320 --> 00:55:58,920
Speaker 2: a location. Don't do that. I do, Yes, I.

1032
00:55:59,040 --> 00:56:03,279
Speaker 1: Do, they do. I know there's protocols and they're like, hey, don't,

1033
00:56:03,320 --> 00:56:05,640
Speaker 1: but I don't know if they don't know how to

1034
00:56:05,640 --> 00:56:06,480
Speaker 1: stop their thumbs.

1035
00:56:06,560 --> 00:56:09,239
Speaker 2: Yeah, and then you also have And the reason I

1036
00:56:09,239 --> 00:56:12,600
Speaker 2: know they exist for you know, US folks, But where

1037
00:56:12,640 --> 00:56:15,720
Speaker 2: I've been looking is looking at them for Iranian troops

1038
00:56:15,800 --> 00:56:19,799
Speaker 2: or Russian troops. You know, there are communities where they

1039
00:56:19,920 --> 00:56:22,960
Speaker 2: can join online and it's to share funny means and

1040
00:56:23,040 --> 00:56:26,359
Speaker 2: to share photos of them playing with XYZ weapon. Right,

1041
00:56:26,719 --> 00:56:29,560
Speaker 2: they are very helpful for me to figure out where

1042
00:56:29,600 --> 00:56:32,880
Speaker 2: these people are and what threat that might pose to

1043
00:56:33,000 --> 00:56:37,440
Speaker 2: US or coalition forces. But those same things exist for

1044
00:56:38,120 --> 00:56:42,680
Speaker 2: US troops. And be very careful with what you're posting,

1045
00:56:43,000 --> 00:56:46,360
Speaker 2: and for the most part, ninety percent of people are.

1046
00:56:46,400 --> 00:56:48,840
Speaker 2: But that like you said that one time, when you

1047
00:56:48,880 --> 00:56:51,680
Speaker 2: share something it could be important. You got to be careful.

1048
00:56:51,719 --> 00:56:53,560
Speaker 1: It seemed vital to me. When I got that feedback,

1049
00:56:53,600 --> 00:56:56,000
Speaker 1: I was like, you know what, yeah, I was like,

1050
00:56:56,080 --> 00:56:59,520
Speaker 1: ah shit, pardon my French. I was like, you know,

1051
00:56:59,719 --> 00:57:02,360
Speaker 1: I that you know, I hadn't a film.

1052
00:57:02,239 --> 00:57:06,120
Speaker 2: That aircraft and we live and we learn, but now

1053
00:57:06,160 --> 00:57:06,440
Speaker 2: you know.

1054
00:57:06,640 --> 00:57:09,000
Speaker 1: Yeah, you know, it's like, well if they.

1055
00:57:08,680 --> 00:57:13,080
Speaker 2: People listening now also know like oh me, And also

1056
00:57:13,239 --> 00:57:15,240
Speaker 2: like if you you know, you work in the space,

1057
00:57:16,160 --> 00:57:20,680
Speaker 2: you work with either technology or capabilities that are so

1058
00:57:20,840 --> 00:57:24,200
Speaker 2: exciting and it's really cool to see them like you

1059
00:57:24,200 --> 00:57:27,720
Speaker 2: were describing, and you probably have a community who also

1060
00:57:27,760 --> 00:57:30,680
Speaker 2: thinks whatever it is, it's really cool and exciting, and

1061
00:57:30,720 --> 00:57:33,320
Speaker 2: you want to share it by all means, do that,

1062
00:57:33,520 --> 00:57:36,360
Speaker 2: but be careful, like maybe not to the public, you know.

1063
00:57:37,440 --> 00:57:40,120
Speaker 1: Yeah, I tell my daughters and my kids, like you know.

1064
00:57:40,360 --> 00:57:43,800
Speaker 1: The Instagram is more of a brand building content situation

1065
00:57:43,920 --> 00:57:45,960
Speaker 1: where if you have a business, it makes sense for

1066
00:57:46,040 --> 00:57:48,760
Speaker 1: you to be there to show your products and to

1067
00:57:48,800 --> 00:57:51,640
Speaker 1: show your brand and to be there just consistently. That

1068
00:57:51,760 --> 00:57:54,720
Speaker 1: is a brand. And if you're an individual civilian and

1069
00:57:54,760 --> 00:57:57,840
Speaker 1: you're just starting at thirteen, fifteen, eighteen years old and

1070
00:57:57,840 --> 00:58:00,520
Speaker 1: you're making your own name and you're building a brand

1071
00:58:00,680 --> 00:58:04,160
Speaker 1: of that name, so you know, my name would every

1072
00:58:04,200 --> 00:58:07,120
Speaker 1: picture I post is under my name, Brad. It's like,

1073
00:58:07,240 --> 00:58:09,560
Speaker 1: that's the brand I'm putting out there. That's what I

1074
00:58:09,600 --> 00:58:12,040
Speaker 1: want you to see is underneath those you know, it's

1075
00:58:12,080 --> 00:58:14,760
Speaker 1: just very you're building a brand, and if you're a business,

1076
00:58:14,760 --> 00:58:16,520
Speaker 1: it makes sense. If you're not a business, you should

1077
00:58:16,520 --> 00:58:19,400
Speaker 1: think about it. Yeah, that's that's all I'm saying.

1078
00:58:19,560 --> 00:58:22,640
Speaker 2: It's a very good point, and I think, yeah, ultimately

1079
00:58:22,680 --> 00:58:26,640
Speaker 2: something like is I think our responsibility as people in

1080
00:58:26,680 --> 00:58:30,320
Speaker 2: the ocent space is just acknowledging that dual role that

1081
00:58:30,360 --> 00:58:33,520
Speaker 2: we hold is because we deal with publicly available information,

1082
00:58:33,720 --> 00:58:36,680
Speaker 2: so important for us to have these conversations where you

1083
00:58:36,720 --> 00:58:39,600
Speaker 2: can say, hey, here's all those really cool things that

1084
00:58:39,640 --> 00:58:42,120
Speaker 2: we could locate and all the really awful things we

1085
00:58:42,200 --> 00:58:46,080
Speaker 2: can hopefully, you know, have a positive impact on, but

1086
00:58:46,200 --> 00:58:51,200
Speaker 2: also used as an educating education opportunity where it's you know,

1087
00:58:51,200 --> 00:58:54,120
Speaker 2: I call it a red TeV exercise, but like, yeah,

1088
00:58:54,240 --> 00:58:56,800
Speaker 2: what can I find about myself or my you know,

1089
00:58:56,840 --> 00:59:00,200
Speaker 2: my TEK, my presence, whatever it is and clean it up.

1090
00:59:00,400 --> 00:59:03,320
Speaker 2: It's not hard to do, like switch your account to private,

1091
00:59:03,800 --> 00:59:06,640
Speaker 2: you know, be judicious about what you're posting. Be judicious

1092
00:59:06,640 --> 00:59:09,320
Speaker 2: about the context that you offer with what you post.

1093
00:59:09,440 --> 00:59:11,680
Speaker 1: Photos that you post it up. You know, you're like, oh,

1094
00:59:11,800 --> 00:59:14,720
Speaker 1: are those still up on the internet. Take a look

1095
00:59:14,760 --> 00:59:16,800
Speaker 1: you should find out. Yeah, maybe go take them down,

1096
00:59:16,840 --> 00:59:18,560
Speaker 1: you know, get rid of them, or fix it up

1097
00:59:18,560 --> 00:59:20,280
Speaker 1: a little bit, you know, clean yourself up. Like we

1098
00:59:20,400 --> 00:59:23,520
Speaker 1: shower every day, right, mostly most of us. I try

1099
00:59:23,520 --> 00:59:26,840
Speaker 1: to come on. It's probably the same type of thing, hygiene, right,

1100
00:59:26,840 --> 00:59:30,240
Speaker 1: we should begetic about it, except for the bad guys,

1101
00:59:30,240 --> 00:59:33,280
Speaker 1: except the BA but for that dirty dirty pig pen.

1102
00:59:33,560 --> 00:59:37,520
Speaker 2: Leave everything out there. That's so helpful to me. I think,

1103
00:59:37,640 --> 00:59:42,360
Speaker 2: like that's always like this a great day when I

1104
00:59:42,400 --> 00:59:45,840
Speaker 2: come across like an account, a channel something whatever it

1105
00:59:45,920 --> 00:59:49,680
Speaker 2: is where I'm investigating something that it could take a

1106
00:59:49,680 --> 00:59:52,200
Speaker 2: long time, right when you're picking out a big problem

1107
00:59:52,360 --> 00:59:55,200
Speaker 2: like you know, fruit movement and x y Z location.

1108
00:59:55,640 --> 00:59:57,680
Speaker 2: I just love it. When I come across an account

1109
00:59:57,680 --> 01:00:01,160
Speaker 2: with this guy who shares daily updates video he tells

1110
01:00:01,200 --> 01:00:04,200
Speaker 2: me what it is I'm looking at. I'm like, thank you,

1111
01:00:04,560 --> 01:00:10,520
Speaker 2: thank you so much, you know, double heart, you telled

1112
01:00:10,520 --> 01:00:12,960
Speaker 2: me a lot so oh.

1113
01:00:12,680 --> 01:00:16,160
Speaker 1: B yeah exactly, yes, just keep it up, keep it up,

1114
01:00:16,200 --> 01:00:18,439
Speaker 1: you know, and uh, it puts a whole other spin

1115
01:00:18,480 --> 01:00:19,960
Speaker 1: on I got five on it. You know when you

1116
01:00:20,040 --> 01:00:22,120
Speaker 1: got five cast working with you, right, it's like I

1117
01:00:22,160 --> 01:00:26,360
Speaker 1: got the whole other spin to that. H that term

1118
01:00:26,440 --> 01:00:29,720
Speaker 1: and uh five cast dot com is that how we

1119
01:00:29,760 --> 01:00:31,840
Speaker 1: would look you up if you're in the defense departments

1120
01:00:31,920 --> 01:00:34,400
Speaker 1: and in the industry, and you're in the eyes of

1121
01:00:34,440 --> 01:00:37,120
Speaker 1: the world, and uh, you want to you know, reach

1122
01:00:37,200 --> 01:00:41,720
Speaker 1: out to abby D. That's her abby D. You're going

1123
01:00:41,760 --> 01:00:44,880
Speaker 1: to have to find her through five cast. So I

1124
01:00:44,920 --> 01:00:47,920
Speaker 1: have stolen an hour of her soul for you to

1125
01:00:48,160 --> 01:00:51,760
Speaker 1: hear about. You know what we just talked about right

1126
01:00:52,160 --> 01:00:55,320
Speaker 1: our footprint, how she can read it. I just want

1127
01:00:55,320 --> 01:00:57,760
Speaker 1: to let you know that, like what I was saying

1128
01:00:57,840 --> 01:01:01,760
Speaker 1: is like she's laughing because it's true. And so we

1129
01:01:01,760 --> 01:01:04,000
Speaker 1: we have to understand that because five cast is out

1130
01:01:04,040 --> 01:01:06,240
Speaker 1: there throwing a net looking for things that they need

1131
01:01:06,280 --> 01:01:08,960
Speaker 1: to look for, and you know people have been out there.

1132
01:01:08,960 --> 01:01:11,280
Speaker 1: So hey, if you're in the industry, you know of

1133
01:01:11,320 --> 01:01:13,560
Speaker 1: the Department of Defense, and you're listening to this podcast,

1134
01:01:13,560 --> 01:01:16,320
Speaker 1: which you guys do you're write in and so check

1135
01:01:16,360 --> 01:01:18,240
Speaker 1: out five casts, hit them up, tell them you heard

1136
01:01:18,240 --> 01:01:20,080
Speaker 1: it on soft rep. That'd be cool, you know, give

1137
01:01:20,120 --> 01:01:22,520
Speaker 1: me a shout out, and yeah, right, and say you

1138
01:01:22,800 --> 01:01:26,760
Speaker 1: heard the Abby the Abby episode, and don't forget to

1139
01:01:27,160 --> 01:01:29,480
Speaker 1: buy our merch at soft rep dot com and that's

1140
01:01:29,480 --> 01:01:31,720
Speaker 1: at the merch shop. And don't forget the book club

1141
01:01:31,760 --> 01:01:35,080
Speaker 1: because it's soft reap dot com forward slash book hyphen Club.

1142
01:01:35,120 --> 01:01:36,840
Speaker 1: I really love books, Like I got a lot of

1143
01:01:36,840 --> 01:01:38,880
Speaker 1: books like right right here in my desk. Don't even

1144
01:01:38,920 --> 01:01:42,040
Speaker 1: make me pull them out, so Abby, I think I'm

1145
01:01:42,040 --> 01:01:44,120
Speaker 1: going to wind us down after an hour of conversation.

1146
01:01:44,360 --> 01:01:46,720
Speaker 1: I just plugged the merch store by our merch and

1147
01:01:47,280 --> 01:01:50,800
Speaker 1: five cast, you know, trying to put a global net

1148
01:01:50,840 --> 01:01:52,640
Speaker 1: of security over the bad guys.

1149
01:01:53,440 --> 01:01:56,000
Speaker 2: Yeah, thanks for having me. I think this is a

1150
01:01:56,000 --> 01:01:58,400
Speaker 2: lot of fun to just sit and go about you

1151
01:01:58,400 --> 01:02:00,600
Speaker 2: know why I do what I do, And you know,

1152
01:02:00,800 --> 01:02:03,400
Speaker 2: let me geek out a little bit on you know,

1153
01:02:03,440 --> 01:02:05,640
Speaker 2: relating a lot of the stuff that we do for

1154
01:02:05,680 --> 01:02:09,240
Speaker 2: our users to you know, the average person. I think

1155
01:02:09,240 --> 01:02:12,000
Speaker 2: that's really enjoyable. And yeah, if people have questions, so

1156
01:02:12,120 --> 01:02:14,600
Speaker 2: free to reach out. I love geeking out about this, clearly,

1157
01:02:14,720 --> 01:02:16,920
Speaker 2: I'll do it for hours on end. So if you

1158
01:02:16,960 --> 01:02:19,560
Speaker 2: have questions, you want to chat, Yeah, reach out to

1159
01:02:19,600 --> 01:02:21,920
Speaker 2: us or contact us via fivecast dot com.

1160
01:02:21,920 --> 01:02:25,480
Speaker 1: Perfect perfect, Well, thanks so much And this is rad

1161
01:02:25,560 --> 01:02:28,520
Speaker 1: On behalf of Abby D and her five cast posse

1162
01:02:28,920 --> 01:02:33,400
Speaker 1: and me as softwareps saying peace. In a world where

1163
01:02:33,440 --> 01:02:37,680
Speaker 1: information overload can cloud judgment, one platform stands out in

1164
01:02:37,760 --> 01:02:41,920
Speaker 1: the intelligence landscape. Meet five cast, the future of data

1165
01:02:42,000 --> 01:02:45,840
Speaker 1: driven decision making. Five cast is not just software, it's

1166
01:02:45,840 --> 01:02:50,960
Speaker 1: your strategic advantage. Using advanced analytics, it turns vast amounts

1167
01:02:51,000 --> 01:02:54,880
Speaker 1: of data into actionable insights, ensuring that security and defense

1168
01:02:54,920 --> 01:02:58,880
Speaker 1: professionals stay ahead of the curve, from identifying threats to

1169
01:02:58,960 --> 01:03:03,760
Speaker 1: predicting trends. Five cast empowers you with the intelligence you need,

1170
01:03:03,960 --> 01:03:07,640
Speaker 1: when you need it. Trusted by global organizations, it's the

1171
01:03:07,720 --> 01:03:11,480
Speaker 1: tool for those who refuse to settle for the ordinary.

1172
01:03:11,920 --> 01:03:15,000
Speaker 1: Are you ready to transform your data into decisive action?

1173
01:03:15,800 --> 01:03:19,960
Speaker 1: Visit fivecast dot com now and step into a world

1174
01:03:20,200 --> 01:03:21,760
Speaker 1: of clarity and foresight.

1175
01:03:29,600 --> 01:03:32,040
Speaker 3: You've been listening to self red Radia