AI’s Data Quality Revolution & NBA’s Smart Play with AI | TSG Ep. 950

Hey, everyone, do we have a crisis in data? You'll find out you're watching Textron Gang. Hey everyone.

Good morning. Happy Wednesday to you. Wow, hump day already.

This week's flying by. Um, it's been a, it is been an interesting week though. And as usual, there's no shortage of good tech news to sink our teeth into.

Let me introduce you to our panel for today. We have celebrating his Blue Jays, Chris Blask, not celebrating their Red Sox, Kate Scarcella and Dan O'Brien. I don't know what they celebrate in Colorado, but they never need a reason to celebrate out there.

They just, it's not the rocky, they just keep, yeah, it's all Mount Rocky Mountain High. Mitch Ashley, and of course, still sulking in his beer. Mike Vizard, our favorite Yankee fan.

Welcome, welcome gang. It was a little baseball theme today, but we're actually gonna talk basketball fig. Go figure.

But before we do that, Mike, there's a new report out from Futurum. Um, you know, everybody, AI is every enterprise's best friend today, but it's making for, what do we call it? A lot of AI slop, a lot of data.

Well, I think when you get into it, what you discover is that within the enterprise, especially these large organizations, there's a just a lot of bad data floating around this data that's either flat out wrong or it conflicts with other data in other applications. And you expose all this stuff to the AI model, and then you're surprised when the AI model gets confused. And this issue's been going on for as long as I can remember about it, and it's a dirty little secret, and we have not addressed it.

But Dan, it kind of looks like, you know, in our excitement for ai, we're finally gonna turn our attention to maybe improving the quality of the data we collect. Could it be, is this the moment? I think it is, um, you know, recent study out from our VP and practice leader, Brad Shiman, um, over, you know, 800 data decision makers and the enterprise, you know, basically showing that, you know, the number one reason project number one reason projects fail in AI is due to bad data quality.

Um, you know, we got things like trust governance, um, you know, really kind of a top of mind for folks. But, you know, it turns out a lot of the early experimentation on gen ai, that money probably would've been better solved. You know, cleaning up the data estate, you know, you've gotta walk through that data door to get to the value of ai.

And, you know, just, I think we've, we've all seen this from some of the, the early data and the early experimentation. The data's not there in many cases. There's a very small percentage of enterprises that actually have their data house in order in order to really go where they want to go.

On the AI side of things, It, it is interesting, you know, I think a large part of it, Dan, is the crap in is crap out, right? That that's a, a, an axiom well-worn axiom, right? And so when you have bad data to start with, and then you train your AI on bad data, don't be upset when the AI is spinning out bad data.

Well, it's not just the data, it's the metadata. It's the data management, it's the data governance. I mean, you know, I think we've seen, you know, concerns really shift from more of that hallucination concern to more about exposing data that you don't want to be exposed, um, you know, to these AI models, right?

I mean, you know, I think we're at the point now where enterprises are really starting to leverage their own data, you know, alongside these, you know, kind of big LLMs, you know, from a lot of the market leaders. And, you know, as you bring your own data to the table, that's where you really start to get the magic happening with ai. But how you do that in a safe way where you don't expose that data to vulnerability and risk, you know, that's, that's really tricky.

And a lot of enterprises aren't set up for it today. Yeah. Kate, do you think we need like maybe data quality amnesty day and we can all just agree that we all screwed this up for years and maybe we can not blame each other and point fingers or, you know, are we still gonna blame somebody because, well, that's just how we're built.

I think that we will always blame somebody because, you know, God forbid we should take responsibility for our actions. But, um, you know, personally, I've, and I've always talked about a data swamp, right? I mean, hey, you guys in Florida understand swamps and how we can, you know, how people can literally die in swamps.

I, I think we're, we are horrible data hoarders and we are just drowning in these data swamps and, you know, we can't seem to, it's, it's funny 'cause in the article it talks about 80%, um, 80% of time is spent cleaning data and 80% is, it seems to be a number that we have consistently seen as we have, um, been on our IT journey like 80% of the time trying to keep the lights on. 80% of, you know, now, you know, data cleaning, you know, this is such a horrible, um, process that, that we have is that only leaves us 20% of really being able to innovate. And so I think, yes, we will always blame somebody else, um, at the end of the day that, um, for not taking responsibility for the data.

But, you know, back to you Alan. Yeah. Crap.

And then crap out. I mean, good grief. Well, it is the 80 20 rule, Kate, right?

That that's what you're talking about, the 80 20. Yeah. But, but, but seriously, you know what, this is not dissimilar to what we hear in security, right?

We always hear security is a top three priority. It's the most important thing. It's time we get serious about security.

It's time we do the right thing on security. We know it's a problem. Well, this is, its not even its evil twin.

This is its twin brother. We've been hearing the same thing about data for how long. These are not new problems that have popped up.

These are problems we've known about and for whatever reason we give it lip service. But we get T-Rex arms when it comes time to fund these things. 'cause they don't reach our pockets.

And you know, I think Brad did as usual, for those of you who don't follow fu uh, research out there, Brad shimmers one of the smartest people at fu him, he does so many things internally on our platform and everything else. He's really a, a rockstar and he did a great job on this report. Go check it out.

But is it going to be enough to get people to actually not just talk the talk, but walk the walk? And that, that's, that'll tell Chris, You know, in the green room, in the green room, we were talking about fishing, right? And since late spring, you know, we have, you know, I do the show weekly, you know, and I've seen this thread developing and we have blown through as predicted, the last phishing defenses last couple weeks, they're gone, you know, not, not three months ago, about three weeks ago.

And it's an example of this issue because it, our defenses or our business operations were based on like having enough and being able to find something in it. And the attackers, you know, taking phishing example had to automate things that would generally leave some obvious kind of traits. Not anymore.

You know? Now, if I was a phishing bad actor, I would target all of you individually and you would never be able to tell the difference because we rely on this brittle chain. You know, I got an email, it looks exactly right.

Well, now I maybe physically have to go check somewhere else bef you know, talk about not automating the systems. Even the humans aren't even automated anymore. And it's the same sort of reason because we didn't, we didn't anchor the data to anything.

Our business data is, you know, we have more of it and somewhere in there we can find something to use right now. But what is it attached to? What is, what is it linked to?

Where's the relationship? And I think phishing is a good example because we don't have a relationship between that single message coming in and anything that's actually going on in our business process. So everywhere you look at the data, you see these huge, you know, k to your point, these huge swamps of data that aren't actually attached to anything.

And they we're, we've been pushed to the point I think that we need to do those things. And I think we can, again, that's sort of our gig these days if we're right, you know, build a tested system. So whatever system you're talking about can navigate off something that's better than hope, Right?

So let's get real here for a minute, Mitch. There used to be this person called a chief data officer, and they were supposed to clean all this up. And apparently that did not happen.

And it seems to me it's kind of a, a, a simple issue on the face of it. It people set up these systems, but it's the end users that plug in the data and the IT people don't know squat about the data. So they just treat it all the same.

And they don't really have any tools to validate when anybody put in any of these applications. And certainly not whether or not it actually conflicts with anything out there. So I put it to you, is this whole IT thing we've been doing for 30 years, just kind of fundamentally broken.

Well, in the data world, we, we came up with the idea of yes, chief data officers, data stewards, things like that. People who, uh, work just in it, but who people in the business that know what this data is and what they can use it for and would take some role, maybe some responsibility, and it's governance and kind of grooming it and keeping it accurate. It, it's, it's a, it's a moving, it's like managing a, you know, a, uh, auto bond where it's moving fast.

It's not static, it's, it's moving and that data's changing, growing, adding to it and, and building up. And so, and we're doing different things with the data. So I think it's, it's a matter of, it's a multifactor problem.

It's a very large problem because we have so much data that we have to manage. Uh, we don't always do a good job most of the time of how long we retain that data. But the thing, one of the things we're bumping into now in the market is the semantics of what that data means.

Meaning I have a database and it has these columns in, and this, this column is called account number. Well, what does account number mean here versus the 50 other systems have an account number in it. So the metadata that the semantic meaning is really locked up in code, that's where that's represented because the logic's all there.

Well, we need that semantic meaning meaning for ai, so it can know what to do with the data, what data it needs it wants to use. So there's a big effort now to not only clean up data, but also put some metadata using ai, frankly, to do it to, um, to put some context around what that data is. So it's, it's a bigger problem than it was before ai.

Mm-hmm. I, I gotta, I gotta go. George Carlin on you here.

First of all, before I do that though, is this what a chief data officer did? Because I often wondered what the hell they do anyway, right? I just thought it was one of these CXOs, right?

We're gonna make you chief of something data. That's good. You'll be the chief data officer.

I thought That was one of the seven words. You can't say Text. Yeah.

Well, that, that's going back to George Carlin. But how come we always think about data associated with bodies of water, whether it's a data pool, a data lake. Now we've got a data swamp next to the data sea, a data ocean.

Why can't we have like a, a data mountain or a data volcano that blows its top or something, right? Why, why is data is there? Is there something?

It all comes back to the word drowning in data. Thats the Problem. That's, that's where it is right there.

On a serious note though, Dan, you may know this, you may not, I don't mean to put you on the spot. Is Brad's report open to anyone watching this? Can they just go maybe get an executive summary or something here?

There's definitely an executive summary out there. Uh, there's also a reg forum on the website you can sign up to get, uh, you know, get pushed some, you know, incremental color on this. Good.

That's important. So, Dan, Let, Dan let me ask you something more about this stuff. So there's a lot of business people out there who are a little cynical about anything to do with analytics because they're like, I got this report from the IT people, and it's very nice and well presented, but they look at it and they go, but I know that the data that was used to create the report is crap because I entered the data and I know that the data's kind of deeply flawed.

And now you're telling me an AI agent's gonna come and gimme more of those reports, and they're kind of like chugging their shoulders and going, that doesn't solve my business problem. So do we need to have a real conversation? Well, I think anybody who's in the business of creating data needs to embrace that garbage in garbage out principle, right?

Um, you know, all of the business users across a company are really responsible, you know, for the data that that company has. And, you know, putting much more emphasis on education and training and really helping people understand where that data goes and what it's used for when they input it. I think that's a, you know, one way to help tackle the problem.

I mean, back to Alan's earlier point, I think they call it a data lake. 'cause you know, people are, you know, people are just polluters. They're just dumping it in the lake, right?

You know, they don't know what they have. They need to get rid of it, they dump it in the lake. Um, you know, you're kind of pooling it all together.

But, you know, mid shocked about this a little bit earlier. Data is in all these silos, all these applications, and we're creating more data today than we've ever created. So this problem is like growing at an exponential pace.

And, you know, it seems like there's a couple strategies out there, right? You're getting these kind of cross application, cross, you know, cross cloud, hybrid, you know, kind of data lakes as a way to kind of get everything all in one place using AI to really get more, you know, we talked about metadata, more information about the data that you have. Um, and then I think, you know, more recently, we're actually starting to put some emphasis on, you know, governing that data, making sure that, you know, the accessibility of it, you know, to the right applications, to the right agents, to the right people, you know, is all kind of in there.

So I think what's, what people struggle with on this is this is all work you need to do before you get to the value. And right. And I think that's the problem in making an IT business case for this is this is all essentially a prerequisite for that project that will then deliver the bureau business ROI, right?

And I think, you know, you gotta take a little bit of a longer term view, um, on your business to, to really get behind why we need to do all this work. All right? I'm almost done with my rant here, but I think Dan put his finger on it.

We need a data literacy program. Most of the end users are data illiterate and have no idea where that data goes, why they're putting it in there, and they just think it's a chore and it doesn't much matter. And oh, by the way, if I spelled somebody's name wrong or the company's name is wrong, who cares?

They'll, somebody else will figure it out someday soon, right? Yeah. I wanna, I wanna confirm a rumor.

I heard a rumor that Techstrong TV was starting up a new show called Data Hoarders. Is that true, Alan? Well, Mitch, we, we, I can neither confirm nor deny.

Okay. All right. Good deal.

Hey, by the way, check out the, uh, signal report that Brad also, uh, put together. com/signal. He's got a data intelligence and analytics report built with ai.

It's really cool. It's awesome. And The data intelligence platform is, is really the, the tool that companies are using to solve this problem.

Yeah, getting it all into one place where it can be centrally managed, governed and made accessible To you, but I'm still not sure. Does it go to the lake? Does it go to the sea, the ocean, not the swamp, I hope Just on the weekends, Alan, Just on weekends like me.

Anyway, hey, we're gonna take a break. We're going to come back. New undergrad has declared for the NBA draft, and they're talking about, it may be the number one pick.

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And of course, everybody's, uh, hopes for their teams spring eternal. So everybody thinks that they're gonna be in the NBA championship this year. And of course, AWS is once again, touting a, an alliance with a sports league.

And, um, so now we're gonna get a lot more data. They claim they're gonna change the fan experience. And of course, we had talked about some of these issues in the past, and Alan will always remind us that this is all about gambling at the end of the day.

But Chris, um, is AI gonna give us a better sports experience and or are we just gonna get inundated with more data that we don't understand? Well, I, I'm hoping that, that we'll get AI hallucinating some, you know, good highlight reels, right? We can see some things we'd like to see anyways.

But no, I I think it's a, it's an interesting applied use case, right? Because imagine you were the, you know, we were talking to sports before this, because of course, again, the Toronto Blue Jays are going to the World Series, not that anybody noticed. And you know, you, you're the coach of a a team like that.

You have these tools available, will you use them or will you not? Oh, yeah. Oh my beer, absolutely all day long.

So what does that mean? Which lead leads us into this story? And as we're talking about the last segment, you know, we have all this data, what do we do with it?

Well, and, you know, uh, this is a, you know, wonderful thing about competition sport, uh, in a capitalist environment, a sports team competing for all their work for the very top, um, you will get the hard drives and the, you'll store the data, you'll use it, you'll follow all the things that we do, you know, that we complain about because we are the folks who build the systems the teams are using, and you'll run into the same sort of problems, right? So I can't imagine that that goes forward three years, you know, with the kind of data, you know, just off the, off the last last topic, the kind of data you could produce doing that I would produce if I was the coach of the Blue Jays right now, without solving some of the problems we're talking about, how do I navigate that? How much of it makes any sense?

Did I just make a thousand hours of hallucinated highlight reels that don't have any use? So They're gonna figure it out. Look, I think there's two, there's two different paths you go down with this AI data in sports.

One, Mike, as you said, is for the guys who bet on two cockroaches climbing up a wall, are looking for any edge they can, and they'll, they'll latch onto some quick AI stats to try to, you know, give them an edge on, on which cockroach is gonna win the race. But then there's another piece of it, and it's the Moneyball aspect of it, right? And, and this is really, so I'm a huge football fan, right?

And this is really reared its head in football where you see so many more teams. Go for it on fourth down. And, and when you, you know, when the, you talk to the coach, you know, you end the f controversial fourth and three at your own 40.

Well, the odds are forever in their favor, right? Because they know what the AI has given them odds of what's their chance of success. Dan Campbell on the Lions, uh, Shanahan, on the Niners, uh, uh, the guy on the Rams, Sean McVey, they're big proponents.

They do their homework, they use ai, they use, and they, and they have the stats at hand to know, Hey, I should go for it. I shouldn't go for it. I think that's a great use where AI contributes.

I, you know, go ahead, Kate. Well, you know, Alan, you just, I think name the key for ai, and that is speaking about the coaches. That's, that's the problem that we have with all this data and is that we don't have experts who are able to understand what data needs to go into AI in order to get the results, um, that you're in, in order to try to get the results that you need to make the, the decisions.

So you named really very, very competent people understanding the data that they need, understanding the data that they want, being able to put that into AI in order to get out the, the, um, the results that they're looking for. That's the key that we continue to, to, to miss, are these experts. We, we think anybody can do this.

Anybody can't manage this data, and that's, that has to change in the story. So, you know, um, it is, Well, I, I think all these teams have data experts. Now you are alo Moneyball.

Oh yeah, for sure. Definitely, right? Who are running this show, they show Moving that way, right?

I mean, you know, I, I think what we're actually seeing now is a lot of the data that's been used behind the scenes that's really transformed how people build teams. And I mean, look, you, you brought up Moneyball earlier, Alan, you know, you can make a simple, you know, that obviously transformed baseball in a big way. You know, all of the statistics and analysis around the value of the three pointer relative, that extra point relative to the shot percentage, you know, the, the game has completely changed in basketball, you know, moving outside the arc.

We've seen a lot of that in football. You talked about that, that earlier, going forth downs, that sort of thing. I think a lot of this has been used behind the scenes, uh, from the teams and how they manage and how they play and game strategy.

I think it's now really coming to the user side. And I think there's a couple things behind that. You know, one, I think part of this is AWS's ambition as a broadcaster, right?

They're, they're able to watch on Thursday night football, you know, broadcast on the Amazon Prime app. You know, you're actually able to watch a different stream that embeds all of this data for people who are really into it. You know, knowing that Aaron Rogers threw almost a 70 yard Hail Mary to try to win the game, and measuring the arc of that, how far it went, that's interesting to people.

So I think there's a clear entertainment side as well. I think there's also, you know, a huge movement in this country around sports betting, right? Daily fantasy sports, you know, being able to bet from your phone and, you know, this is all in theory, making these, you know, these users more educated, you know, they're, they're taking in these data points and, you know, live betting games and that sort of thing.

So I think, you know, I think this has been happening for a while. It's been more kept in secret on the team side. I think that's getting democratized out to the fan base and part of it's pure entertainment value.

And part of it, I think is the symptom of, you know, this huge movement towards sports betting in the country. I agree, Dan, I definitely betting ISS a big part of it too. Also, you know, I think we're moving, this is sort of the phase of exposing, as you were saying, externally to the, uh, fans, to customers, for them to be able to, you know, leverage it, use it, enjoy it, bet on it, whatever they do, sort of the next phase is to be able to, how do we, how do we catch up to what the human action of doing analysis of games is watching game tape.

Um, you know, Dennis Rodman says that he sits there and watches everybody shooting free, shooting from the field to count how many rotations of the ball it takes. So he knows where to go to, to do the, to pick up the, the rebound. I don't know if he really does that or not, but, you know, it's those kinds of things that are real analysis that if you have, you have the right data and if you have the horsepower, maybe some AI along with that, now you can actually do week to week player to player play by play analysis and say, not only on the 40 yard line in this situation, you know, does it, is it this odds?

It's in this game, in this weather, and with this team, um, if they wanna run one of these three formations, we have a 63 chance percent chance of getting the fourth down. All right? Has anybody here actually downloaded DraftKings and ever used it and kind of played with it?

Okay, so I did. It's freaking ridiculous and incredibly complicated. And you cannot just make a sim.

Well, you can make a simple bet if you can navigate through it, but ultimately it's like you're presented with, uh, trifectas. And if this guy passes this ball and this guy actually shoots at three and within 10 seconds or whatever, and I'm making that up, but you get the general idea. It's incredibly complicated.

Well, well, listen here, boomer. No, listen here, boomer, That's a boomer thing. 'cause I'm gonna tell you something, Mike, I, we are contemporaries, you and I, and for people of our generation, you are right.

Making a bet was calling, you know, Louis downtown and, and making a bet, and maybe you put a little slip in or something, right? And, and it was a straight bet you if you got exotic, you took the points. But I'm telling you, like my sons, their, their age, the, the 20 something year olds, the people who, you know, we're no longer the focus of marketing, right?

But the people who are twenties and thirties, they love the sophistication of those bets. Who's gonna touch the ball first? Who's going to catch the first pass?

Is it gonna be a runner a pass that first play? And it's, it's an adrenaline junkie thing. One bet's not enough.

Let's triple parlay that, right? com, he's a shareholder. You know, Martin, well, he, he recently left, but Martin built the Caesars online, uh, gaming as we call it, platform.

And, and I've talked to Martin extensively about it, that you are not the target, Mike. My sons are the target, and they love those, let's call 'em data rich kind of bets where we, and it's parlays and it's exotics and it, and it's all those things. And they, they got so many different things going on, bets going on.

I don't even know how they track it, but that's what that UI is for. And that's why it looks so, so sophisticated. My, my friend, the Boomer, I, I, I, I'm gonna, I'm gonna suggest that I can feel the people using the ai, you know, so Dan, to your point in the entertainment, you know, uh, um, and Mitch, to your point in, in the back end of the data, you know, watching the, again, the, the, I'm not the sporty person Donna is, but during the eighties and up to 92 and 93, when the Toronto Blue Jays won the World Series, twice, I got into that, that data and baseball is classically the data game, right?

You know, watching all, all that and thinking about it and applying it so much, like business and security and everything else, and watching it last night, I listen to you guys talk, I was thinking I could feel the people in the booth behind the ones who were really getting it, the, we're talking about, if you're watching right now, you're out there, you're working for whichever channel I was watching, because the timing and the production of the clips right after the putting 'em back up there, that's to me, is someone really getting AI back in the technical bit and tacking through the mess of the products and getting it online, real time in front of millions of fans and in the back room. Yeah, I guarantee it. Some of these teams have somebody on their staff who understands, who can watch this show and understand, you know, not just ai, but the last six months of ai well enough to apply it to this.

Oh yeah, if I was on those teams, I would have all the baseball stats and everything every player has done, oh, you know, hour to hour through all the playing days for the last six years, all mapped out all the same time. Because now you can, Here, here's my prediction for the NBA. They're gonna come up with a four point play from half court sponsored by AWS and DraftKings, And you know what?

And the, and the odds are hitting that four point shot, they'll be damn in there. And then you could parlay it with a three, a three pointer. And who gets the rebound if he misses?

And that's what people want. You know, I, I, I'll end this segment with this. I was, I was at a, a wedding this Sunday, and I was talking to a bunch of 20 somethings, right?

Grooms, the, the groomsmen and all that. And, um, th they, they love this. They ab and you know, they were saying Roger Goodell is coming into his 18th year as commissioner of the NFL.

And, and typically that, that's how long a, the longest commissioner reigns, right? Is 18 years. And looking back at the 18 years of Goodell, certainly the NFL has grown everywhere.

You can measure it internationally, TV money, TV ratings, any way you wanna measure it, how much of it is due to gambling and to stats like this, right? Data and stats, driving the, and, and, you know, driving legalized gambling has made the NFL probably the greatest marketing machine, perhaps in the history of the world and more power to 'em, right? And, and, uh, people, and I, I should mention, it's not just a guy thing.

When I say 20 something bros, it, it, women too are into this, right? Because it is, it's a, it's a statistics thing. It's math, it's arithmetic.

So more power to 'em. Anyway, let's take a break. We're gonna come back and, well, I was, I was down at a, uh, a conference in Houston last week a little bit, talk about it.

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Contact us today and tell your story to the world in the most powerful way with Textron Group. Hey folks, we're back. And yes, we're gonna talk about this qualis conference that Alan went to.

He is gonna lead us off on it. It's one of our field trip reports. And Qualis had some news while we're there, and you did a bunch of videos.

So walk us through what happened, my friend. Absolutely. Thank you.

So, yes. So here, here's the thing from my non-security friends out there, right? Qualys has been a bellwether rock in the security space for 25, 28 years now.

Actually, Philippe Cortia founder was a very good friend of mine. Mitch knows him or knew him, unfortunately, he passed away about three years ago. And, um, but I've been covering the Quas security conference, the QSC, I dunno, 10 years, maybe more as here at techron.

And I was an attendee. 'cause they always made a good party as well. It was always during RSA back then at the, uh, at the St.

Pierre. It's a nice, a nice, uh, hotel in San Francisco this year. They changed it though.

It was no longer the QSC. It was rcon, R-O-C-O-N, RCON, RO, standing for risk, uh, risk operations. This is a fundamental, this is not just a name change for the sake of a name change.

This is a fundamental change in how you are looking at security, right? Qualys is no longer looking just at vulnerabilities, remediation, patching, mitigating, right? A, a known vulnerability or something like that, or scanning endpoints or what have you.

They're taking security back to what we always said it was about, which is about managing risk. You are never gonna solve or fix every vulnerability. You are never gonna make a hundred percent crack proof hacker proof system.

Well, maybe if you unplugged everything and took humans out of it. But that's not what real security is. Real security is about what is an acceptable risk for me to conduct my business profitably.

And so we have a knock network operation center. We have a SOC security operation center. Qualys is now proposing something called a rock risk Operation center, where we, we look at all of these different factors in the context of risk and what's acceptable or not working, not just with the security people, not just with the network or IT people, but with the financial people, with the governance, people managing your business to an acceptable level of risk.

I think it's brilliant. I think it's long overdue. I think it's really time we focus in on that instead of playing whack-a-mole with every vulnerability and thing that comes out here.

Right? And Kate, Chris, Mitch, you guys have been in security with me a long, long time. We, we've always talked about it, but this is the first time I'm seeing a major security company not named Arrow, that does GRC or something talk about managing risk at that level.

And so kudos to them. The the whole conference was, was PO poised upon it. And mark my words, this thing is gonna move from a Qualys user conference to an industry-wide conference about risk managing risk and risk operations.

Well, maybe we've turned the corner, Alan, you know, we've pursued that one more thing. If we can lock that down, we won't, uh, we won't, they won't get in, right? It's, it's not all about defense.

We kinda moved into the response that's gotta be part of this. And what you're talking about is really is elevating to say, look, we know we're gonna get hacked. We're gonna get fish, we're gonna get compromised here and, and we're gonna have security defenses and, and operations and actions in place.

But that, that, that threat surface is getting bigger and bigger, right? We're adding AI to it. We're doing, using more data, all the things we've talked about on the show.

So one more, you know, one more cog in the wheel in the system. And the Rub Goldberg system is not gonna solve the problem. So it's really how do we make sure we're doing, putting our, our bets, speaking of betting, putting a chips in all the right places where we think we've got the greatest risk and also the greatest loss.

Um, and, and, and it is a bit science and art, right? There isn't one answer to it. 'cause everybody's business has got different dynamics, and it's interesting to see that Squalls is stepping up to do this.

Well, yeah, I I think everything, you know, inside this story and, and everything, uh, you described and everything they're doing is exactly right. But, you know, just for conversational purposes, let me push back on the premise that we haven't been doing risk, uh, well, because I don't see this so much as I, oh, the framing is risk, but I see this more as continuous risk management as opposed to periodic, right? And if we go back to, you know, for me, you know, in 1992 it was risk management was by a firewall.

Oh, I thought that's when the Blue Jays won the series again. Oh, That they did actually. But that was by a firewall.

And because in the security community at the time, you know, there's lots of discussion about everything you need to do before you can get online. And me being young and stupid thought, well, yeah, no, but you're, you can't, so buy a firewall right now. And then, you know, early in the, you know, the NIST cybersecurity framework, you know, it seems to be 2005, 2006, I remember, but the initial draft was before and after bang Tim Roxy's thing.

And I was into either, you know, uh, sim or or ISAC at the time. And my, my thought was, detect we need, we can actually detect now. So around 2005, six, you could reasonably actually see what's going on and think about that and not just, you know, defend or respond.

And now what I see inside this story is, yeah, it's not just about detecting, right? It's about, you know, constantly. It's not an incidental thing.

I think we can now, and I think to, to my point I guess is that what's right in context in 2025 is that your risk profile should be continuous. You know, what I see inside that story is it's 24 hours a day because I, if I'm a bad guy, I'm attacking you with customized AI tools that are getting better by the hour, 24 hours a day. So you can do your 90 day or your periodic or whatever, or you're, you know, one, you know, once in a while, incident response process all you like, I will bury it.

So risk is now getting to that level. You know, without, you're not doing that. You're no longer managing risk.

You were last year, Kate, bring this full circle for me so far as I can tell, not all data is of equal value. And so we need to do a risk assessment of the data. So doesn't that just bring us full circle and say cybersecurity is really a data management issue and go see block A I totally agree with you.

Yeah, no, it's, it's funny because, you know, when I was reading the article and, you know, hey, I, like Alan said, we've been doing this for a long time, and I were, and, and look in, in many of the, um, like the Q radars and things like that, they had risk indicators and, and, and different points to, you know, what makes risk. So for me, I'm like, you know, I'll never forget there was a perfectly good sock, a perfectly good knock. 0 being built right next to the sock and the knock.

And then when I'm reading the article, I'm like, oh my goodness. And now we're gonna have a rock, you know? And it's Sounds like something at a Dr.

Seuss the Sock and the Knock with the Rock Yes. And Green Eggs and Ham. Yes.

It's, I I'm not skeptical, but yeah, I guess I am skeptical. It's just this is sort of, I love the idea, but it's sort, we're just rebranding it in. Well, its, well, it's more, it's more than a rebrand though.

You know, I, I had a good chat with my friend Summed, summed. Tarka is the CEO of Qualys, but Ed's been a quas 20 years, right? He used to be Philippe's right hand guy.

Um, this is a, this whole rock play elevates the conversation aquas for Qualys to the ciso, the CEO, the CFO, the board. It's bringing security. It, it's, it's abstracting it up a layer to decision makers who have budget, who, and you can't, when you go to the CFO and you are gonna tell him that you have 3000 major CVE vulnerabilities out there.

He, he's looking you like, you're speaking Latin. I, I agree, I agree with that. And yes, for Qualys, it gets him into the C-level suite.

But for us who have been part of cybersecurity, I mean, I can tell you I've talked about risk forever and you know, and then you start talking about bringing in this disparate type of systems, they start talking about identity. And then I started to read all these different, um, um, security technologies that they're gonna be bringing in. And, and, and I'm like, oh man, once again, you know, yes, we, we, yes, yes, yes.

Single pane of glass. I, I'm not trying to be well Negative. No, I, I I, I, I don't disagree with you.

I, let me just one other, I got two letters for you, AI, because not to sound like Dustin Hoffman in the graduate that was before 92 and the blue chase, Chris, you know, the AI here is what makes this doable in many ways, right? Because they're gonna have agentic AI working with your existing sims, your existing, uh, security tools, even your identity and access management system, so that it all kind of reports in coordinates, correlates, and makes decisions based upon your risk profile, or at least helps better populate the data you're going to need to calculate that risk. Well, let me take the data hook that, that you and Mike both put out there.

'cause I'll just say yes. Right? You know, and this, you know, the, the frustration I hear in your voice, Kate, you know, you know, this has been said enough in this episode.

How do I say it again? So, Fred Koler and I, over the weekend are having a conversation about, about fully attested systems. And IBM tried this, I think in the eighties, we couldn't remember the operating system where like every file changed and so forth.

It didn't make it log sprawl. You know, we weren't ready to do that at this po at that at that time. But again, for all the reasons we talked about it, every segment today and every, you know, segment for the last, you know, year, we're at the point where we need the data to be, you know, call it what you will sane, uh, I would say attested grounded related to something so you can navigate it.

Because we're right now throwing AI horsepower at it, which works great. It's good brute force approach, but all the edge cases, you know, from the common things we hear about in the popular articles to the, the things we geek about, it's like you get into how do you actually navigate that data? And, you know, we'll find that, at least in the security space.

And I think in the information, the data space as a whole, 30, 40, 50 years ago, people said, here's how you do it. We just have not yet done that. So it's not really inventing anything new.

It's saying we have finally can't get away with brute forcing this. We need to go back and say, oh, how does this work? Right?

I think there's various answers to that. But you know, again, from my perspective, it's semantics of the tested systems. But, but we had to put the boundaries on it, you know, so talk to Call up Fred, have him lecture.

You find out where you're missing the parts that you can do today. Do those, You know, Alan, we, we've spent what, the last 30 years in the bad news game in security. Mm-hmm.

Remember, you remember in Still Secure, when we attend vulnerability management and intrusion detection, we occasionally have a customer call. Well, that's just a bad news generator. I don't need to know more.

I don't need to do I already know Have problems. Got enough bad news. Exactly.

Yeah. Uh, especially when it, uh, the, the emails went to a kernel by accident on one day. I remember that.

How do you shut it off? It was quite an instant. Well, we we're still in this, like, if we have more data, if we have better data, if we have it in a single pane of glass, I think that's all, it's all part of the answer.

But I think we need to get to the point where we can flip the switch to, it's not about more data, better data, better tools, yet another tool. It's how do we flip it from data into action? How do you have the conversation with the COO, the Dan O'Brien of the world and say, know, I don't wanna buy another tool.

This is what we wanna do. Like we've done this analysis, we use AI to do this. We, we are looking at the trends.

We're using whatever are the best resources so we can put the best plan in place and put our money, where's gonna make the biggest difference? I think that's what you're talking about. Quality.

Yeah. And two letters to you, Mitch, too, ai, because that's my answer for everything today. But I know, but No, no, but Right.

But seriously, you know, I Be, it, I beg to differ with all dear respect. And here's where I'm gonna differ. 'cause we just established earlier that the AI that we're creating is based on flawed data.

So now you're telling me that, that AI is gonna save us from our cybersecurity issues. I Like, I agree. I agree.

And, and let me just say, I, I mean, I still think that we're looking at this problem wrong. I, it goes on behavior. We, we have to look at the behavior at the end of the day in IOPS instead of IOCs.

I, you know, I know that this is sort of out of left field as we have sort of been talking about sports analogies and things like this, but we're not getting this right yet. I I really, you know, this whole ahead of the threat, we've been, my goodness, since 2003, you know, No, since 92 and 93 according to Chris. Yeah.

Obviously based on the World Series. Yes. They started, they got calendars that year.

I, I like this concept of rock, right? I mean, I think this is really good marketing for Qualys. And I think to your point, Alan, this is gonna elevate the conversation a little bit from them.

Uh, I think it's a little bit of an acknowledgement that, you know, secure is effectively a nirvana state that we'll never achieve, right? We'll never have enough people, we'll never have enough skills. We're never having enough technology That's To spend, truly get to risk zero.

And so do I think that we will create some new function within the organization called the Rock? Actually, I'd call me a doubter on that, right? But rock is more of a philosophy for how you operate the soc and the knock, how you prioritize, how you align, you know, to kind of business priorities.

That makes much more sense to me, Right? I would just let us say, you know, you gotta skate to the puck and I just wanted to get a hockey reference in there to complete the Good work, Mike. Good work.

Raised it, Mike, nice Job. How about, what's the game where you throw the bean bags in the hole? A corn, corn hole, corn hole, I bet a corn hole reference or something.

There you go on there. Anyway, look, it was a great conference though. There's a lot more to, if you're interested, uh, you know, you could check out our videos and, and we probably did in two days.

I think we shot 20 something videos. So, um, there's a lot of videos, there's a lot in there. There's a bunch of Quas customers and execs, and analysts and so forth.

So it was, uh, it was an enlightening conference. I, I, you know, it'll be interesting to see how this, it, the industry, right? Because here, just in our little group of six, it looks like we've got some doubting Thomases and some, some big believers Daydream believers.

So, uh, we'll, we'll see where it goes. But guys, I gotta, I gotta pull the plug on today's gang. We're about outta time.

We've all got more stuff to do in our day. Uh, if you've got time as usual, we have our great text Drunk TV shows immediately following this, including maybe some of the Qualys interviews. So check that out.

If you're not watching this during the stream, you could watch it on demand, on Text, drunk TV, on our text, drunk tv, YouTube channel. Or the way I like to watch it is I download the OTT app under my Amazon fire or Apple TV or iOS or Android device. And this way you can watch it on a big screen.

Mitchell looks down, right? Handsome on a big screen. Yeah, baby Uhhuh.

So check that out. We will be back tomorrow with more gang, more news, more gang members. Until then, though, this is Alan Shimel on behalf of Techstrong, have a great day, everyone.