AI Partnerships & Observability Challenges | TSG Ep. 952

Hey, everyone. You know this AI is making for some strange bedfellows. You're watching Textron Gang.

Hey everybody, happy Friday. Welcome to our Friday edition of Textron Gang. This might be like a red letter gang day.

We, we called in some of the outer chapters here. Forgive me, my wife is binging on Sons of Anarchy, and so I'm into like chapters now, but we've got, we've got seriously, a security All Star team assembled. We also called in the big guy here, Dan o.

He's usually on Wednesdays and he was on Wednesday, but he's gonna be here Friday. 'cause we're lucky enough to have him in our office in studios here in Boca. Um, well let me introduce everyone and we'll go from there.

I introduced Dan o already, Dan O'Brien, president, CEO of Futurum. We've got John Schwartz out in Silicon Valley. Fred Wilman up in Seattle, IRA in the DC area, Chris from the great white North, and of course, Mike Ard still licking his wounds over his Yankees.

Um, guys, it's, it, it's Friday this week went incredibly quickly, but we've got a lot to talk about. Mike, you know, with the AI stuff, I'm, I'm starting to get the opinion that I know what you are. It's just a question of the price and, um, what's going on here.

I think we're starting to see, well, I don't know, I wouldn't call it anarchy, but it's basically anybody can partner with anybody regardless. And so what we have it lately, it's, It's free love. There you go.

Andro is partnering up with Google to, uh, create an alternative for GPU resources for itself. And IBM partnered up with Grok and I think IBM and it makes just about every AI model available and so does everybody else. So we'll get into what that means in a minute, but John, walk us through what's going on here.

Okay, well, yeah, we mentioned that these are strange bedfellows. I also think of it as another way as like the enemy of my enemy is my friends. And I think that's the case with philanthropic and Google.

I think of it as a kind of the anti open AI partnership, right? Where this kind of, in this era where these companies are, are aligning with each other with, for whatever's convenient for their particular task or whatever their goal is. It's almost like this accelerated chess match with billions of dollars in play.

And in this sense, um, the idea I believe is positioning Google Cloud as a key infrastructure provider for philanthropics AI operations. It's, it's, it's interesting because there is a relationship between two companies already. Google's invested at approximately $3 billion in Anthropic.

I think it's about 14% stake. So we, we hear so much about Amazon and Anthropic, but in this case, Google has been working with them. And again, I think this is with the idea that Anthropics nemesis is OpenAI.

And OpenAI earlier this week announced Atlas, which is, uh, their bid to overtake Chrome. So we have these two companies going at it with OpenAI. So they have this mutual kind of, uh, mission to put them to put, uh, OpenAI in misery.

Now as far as IBM and Brock, it's interesting because I, IBM's been doing a lot of deals and we're, I'm gonna pass the baton to Dan before I just quickly point out a couple of the things that IBM's working on. Um, they've, they did an announcement, I believe it was last week with Oracle. They're also working with Anthropic.

So, uh, Claude AI models are gonna be integrated into IBM's integrated developer environment for software engineers. The, uh, the deal with GR is, is aimed at helping businesses rapidly depo deploy AI agents through, uh, greater speed and efficiency. And I know Dan, you worked at IBM for a long time for a bit, and I'm wondering kind of what, what your take is on what IBM's doing with Grok.

'cause I find that equally interesting is, is the GR Google philanthropic deal. Yeah, thanks John. Uh, so, you know, on the IBM M side with Grok, I think this is really about, you know, IBM's belief that AI economics are fundamentally broken the way we come at it to date.

And, um, you know, I think they're really thinking that small models, you know, purpose built inference accelerators, you know, that's really kinda where really these scaled AI use cases will find that ROI and that economic fit, right? So I, you know, I think you look at a lot of the deals they've made. They've had a, a more recent a d announcement for AMD's initial rack scale solutions.

They've got some Nvidia stuff through Core Weave, um, Andros more providing models, I think on, you know, kind of their code development side of things. So, you know, I think they're well partnered across the ecosystem. I think that's been, you know, kind of a fundamental belief of Arvin ever since he's come in is that, you know, you need to be able to play nice with the other leaders in the industry and find ways to come together to add unique value for your clients.

And, you know, I think that's really, you know, what's kind of driving them there, the Google philanthropic stuff is really interesting to me. Um, you know, I think on a couple levels, right? You talked earlier about Google's a $3 billion 14% investor.

Well, AWS is a $8 billion investor, right? And they've really been kind of philanthropics cloud of choice. So, you know, I think this is really telling us that people need as much supply as they can get and they will go find it anywhere.

You know, I think philanthropic has particular issues in that, you know, I think we're seeing some of the AWS custom silicon tra three, you know, maybe not being as great as everybody thought it would be. Um, that's coming a little bit later to market, it seems. I think, you know, there's obviously been a fairly public war of words between Daria, Modi, anthropic, CEO, and Jensen Wang.

You know, they're probably not getting great allocation when it comes to, you know, these very hard to get Nvidia chips. And, you know, you look around the market, it's really a MD and Google that are those next best options in the market. Um, you know, to Nvidia right now, you know, in certainly a MD on the GPU side, and I think there's a lot of, you know, really good interest happening in the initial rack scale solutions a MD will bring to market next year.

But Google with their TPUs and the custom silicon maker of choice for pretty much everybody who's trying to make it on their own, um, you know, partnering with Broadcom on that front, you know, I, I think that's really where Anthropics coming from is they need more compute and this is the most logical place to get it. I wouldn't be surprised to see something with a MD with them as well. Hmm.

You know, I, I, I gotta tell you, first of all, I, I think you're right, Dan. The, the IBM Grok partnership is, is so typical. IBM that, that is pure IBM partner with everyone.

You know, you want something, let me just reach into my bag of tricks. So it gives those IBM sales people, every, they, there's nothing they don't have. It's like literally, you know, going into Home Depot.

But on the other hand, part of me, part of me says, well, what about Watson? Right? And everyone out here should say that with me.

What about Watson? Right? This was the first AI that most of us ever heard of.

It was the first AI kind of major play. And poor Watson has become the redheaded stepchild even at IBM, even at IBM. Well, Al Go ahead, IRA.

Yeah, let me build on that because, you know, Watson came out, okay, Watson is gonna beat people at chess. Watson. Watson became a gimmick.

And the thing was then I've worked with people at wa, you know, IBM doing, for example, in their simulation, they were using Watson for cyber simulations and things like that. I personally think that it, it was kind of like an interesting technology, but it was more, in my opinion, it became a proof of concept for them to do something with IBM was never a provider, like, you know, the, the anthropics of the world or things like that. They were selling services, they were selling equipment, and Watson essentially was a gimmick to kind of sort of make it easy.

It was never something that they truly put into commercializing. The one thing I do have with regard to this whole rock thing that I think is frankly something people are under looking is that IBM is one of the leaders in quantum computing out there. And when you start looking at what, why are they working with Grok, again, from what I read, you know, with Grok, they have their unique set of LPM chips or whatever they are, sorry, I'm not good with remembering names.

And the thing is, in a little bit of time, it's my opinion that IBM's quantum computing will overtake the need for a lot of these faster chips that are out there because of their unique quantum computing ability. And for them to position themselves right now with grok and start to get a little bit more use out of it to start to commercialize, it'll be much more of a nicer fit to migrate people off of that and onto their quantum platform. When the quantum platform becomes, I'll just say more affordable and practical for people in large scale use, but to the Watson, again, Watson to me, they treated like a gimmick from the star.

Okay. And I actually went to, I went to elementary school and my claim to fame was I beat Joel Benjamin, the guy who trained Watson, had to play chess. Chess.

I beat his sister in chess and now I'm dating myself. There you there, there you go. So in theory, I have beat Watson's sister at chess.

Okay, now I would put you sign your yearbook, but going back, we need it more. It was a unique relationship, but you know, you gotta understand that. Sorry, I'll, I'll leave it there because I'm just gonna go B off on some tangent, but this is essentially why I think Watson's kind of an issue or not an issue.

At the same time, I think IBM is more just using this as an gap filler. Well, no, but for A while. I'll tell you something.

I saw Daniel Newman posted something on, on Twitter, not on on LinkedIn this morning, and I looked into it. The fact of the matter is money. You know, we talk about where's the money, where's the beef in ai?

IBM, what they said, they have eight and a half billion, Nine And a half billion, nine and a half billion dollars back. I don't think you can actually conflate the Watson that you're talking about with the Watson of today, right? I think you're, you're right, IRA in that Watson was a little bit less of a product and more of a technology that they could apply to kind of a consulting engagement, you know, storyline.

It was like, you know, early machine learning use cases, right? I think what they've built now is much more of a data model governance, you know, like a real platform to manage your ai. Um, I think they've really tried to partner on the large scale models where they've gone deep themselves is really on these very domain specific purpose-built models because they're really trying to really help their clients, I think, get the use cases proven out on big models and then bring them to scale using something that really helps fine tune the economic side of the AI equation and really gets a lot more efficient on the compute side.

Absolutely. And I have a question for you. Um, if I can get an LLM from essentially any dealer on the block, then what will differentiate, you know, an Oracle versus Google versus AWS versus IBM when they're all selling the same kind of basic thing?

I think you're right, Mike. I mean, the, the model layer is very likely to be fairly commoditized, right? I think it's all about, you know, the tools that you provide to people to provide those guardrails, the governance, the, you know, the fine tuning capability, you know, the rag capability, the ability to really manage models at scale and, and embed them, you know, kind of across your portfolio of applications the way that you want to.

Yeah, I I think frontier models commodity stuff at this point. But, but John, I do think you're right. That may your, you know, the enemy of my enemy is my friend, and I, I, I wonder with Anthropic, what is, is Anthropic setting up themselves to maybe be in the, uh, the, the, the, uh, you know, the, the apple of someone's eye in a bidding war between Google and AWS Dan, right?

Who, right, who both say, Hey, open AI is a threat to us. Anthropic may be the best alternative out there not having to deal with Elon maybe. And I think Google's pretty happy with where Gemini's at.

Yeah. Well, and, and that, so that, why does Google doing it more? I, I would agree with you, but why do you think Google's doing that, having Gemini in their pocket?

Is it to same a IBM thing? We wanna have a little bit of everything. I think, you know, if they can create the incremental compute and sell, you know, that's more of an infrastructure cloud play for them really serving up, you know, compute philanthropic, right?

Yeah. Dan Workloads, they need the workloads to drive the investment Justification. Well, so are they buying a customer?

I don't know that They, they're buying a customer. The customer seems to be coming to them with a need, right? You know, Dan, you just, you said something interesting and I, I totally agree with this idea that IBM through all these partnerships, has kind of methodically, I think they put themselves in a really good position in the AI race, actually.

And I, I ran into a couple of analysts last week at Oracle World or ai, whatever they call it now, and they mentioned two companies that they thought were probably, uh, as well positioned as any amid all these players. And they were IBM and Google. And, um, I mean, I just found that interesting.

They, you know, they'd be IBM through their history. I mean, it hurts them, it hinders them at times, but it also helps them because they've been through every conceivable transition or wave in technology and they've adapted. That's why they've been around 120 odd years.

Absolutely. Well, I think Google's in fairness, probably the full stack leader in ai. Yeah.

I think we talk about infrastructure to the model layer to the application layer. You know, they're really probably further ahead. I don't think either of the hyperscalers could make a great argument against that.

IBMI think is positioned to sell much more as kind of an orchestrator for big enterprise across all of the suppliers that they're gonna use, right? All of their customers are on multiple clouds. All of their customers are working with the major ISVs across E-R-P-C-R-M, et cetera, et cetera.

And I think IBM is positioned itself with the unique capability on the consulting side to help bring all that together. And then some unique tools on the, um, you know, on the technology side of the house with Red Hat and Watson and what they're doing on that front to kind of be that middleware layer across the technology stack. Yep.

Yeah, I mean, I kind of fundamentally look at this like IBM is doing, IBM, like BM was always a hardware manufacturer. They were a service provider. They were an infrastructure provider.

And what they're doing here is essentially growing their capability to be, you know, the AI model, you know, like make their AI models, allow their customers more resources so that they could sell directly. And, you know, again, this is IBM being IBM in my opinion. And, you know, I look at everybody else and I'm sitting there thinking, okay, IBM has developing an infrastructure.

They were never truly good. I mean, they have a cloud environment from what I understand, but they haven't really pushed it. You know, what they've done is they've tried to focus on, you know, quantum computing has been their little niche for a while.

Like, so people come to them now to buy Quantum Plus for quantum access to run their models and things like that. Adding on some AI models on top of this just kind of, to me, makes natural sense. And I mean, I look at everybody else, and maybe I'm under thinking it compared to you guys, but it's sort of like, well, when somebody goes with Oracle Cloud, somebody else goes with AWS It's just natural for me on the, you know, the, um, sorry, anthropic going with Google for, you know, infrastructure.

And maybe I'm wrong 'cause I, I thought it would be, I thought it would've been gl uh, Google, GCI. But, um, anyway, we'll see how wrong I am in the near future. Well, Let me, I'm gonna need to wrap this up 'cause we gotta jump to our next block.

But I will say this, we gotta look at these deals in the context, you know, of that Bloomberg, uh, uh, diagram, right? You've got a four and a half trillion dollar, uh, Nvidia. You've got, I don't know how many hundreds of billions of dollars of open AI and the deals of an Oracle up 34%, whatever the deals there are flying, the, the, the money passing back and forth with each other.

They're, you know, so this is a reaction to that. This is part of that story. And when you take it in its totality, wow, we're gonna have stuff to talk about for years.

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Hey folks, we're back. And yeah, we're gonna have a little chat about cybersecurity and ai. The folks over at CrowdStrike have a survey talking to both security and IT folks.

And it suggests at least that they're getting a little concerned that they're falling behind that bad guys are investing more in AI more rapidly. The attacks appear to become more sophisticated, especially when it comes to phishing. They're also maybe starting to increase in volume and things are getting a little challenging out there.

Chris, what's your take on this? Because I know you've been kind of talking about this, but to me, this shows like, there's some actual evidence that this is happening, Right? You know, and as I said on the Wednesday show, right?

One of the things I like through this calendar year is this conversation. 'cause we keep touching on the same things in the last block. If you haven't, if you're watching just this block, go find the other one before this, because we've been coming along this path, let's, let's say, since March, right?

And Dan, you know, these conversations with you on, on the chips and distribution and how this infrastructure works out. You know, when I talk about inevitability curve, what I mean is taking something that looks like it's bound to happen and saying, okay, let's assume it does. Let's go back to the present.

What happens between now and then and to the point of this segment in this spot in this month. You know, we've blown past earlier this week, remember the conversation about fishing. We've blown past certain things this month in the last couple weeks that we've seen coming through the year.

If in fact we're going, the adversaries in this case are gonna have these capabilities, what does that mean for defenses? Well, we keep going through those stages, right? And we're the salt water, uh, the salt, uh, typhoon conversations earlier this year.

You know, the, you have to assume that you're completely mapped out. You the gap between what you probably should have done and what you actually have done is known and adversaries have the abilities to attack you personally. Like, not none of this, I will make some automated, you know, attack to go after a sector or a geography or whatnot.

No, you personally, your company, everything about you that can be automated right now, what does that mean? Well, as cybersecurity people, there may be a smug moment. There's a lot of, as we discussed in this, you can go back decades and, and talk to people of Gene Sanford and Fred Coen.

And folks have been doing this for a long, long time and say, you'll recall we told you that unless you do these things someday you will get ants. Well, we've got lots of ants, fortunately, I think we have a lot of answers, but they're not down the mainstream of what corporations and organizations are used to doing in cybersecurity is changing faster than their organizational processes normally change. Not that I have opinions, Fred.

I Sorry, can I go, go ahead. Yeah. So I'm gonna go on my normal soapbox.

I hate surveys. I hate the use of the term ai. 'cause when you start going to surveys, you're asking a bunch of people, and most of these surveys do not pre-qualify who it is.

It's like they go out, Hey, please answer, sending to a random mailing list and somebody gives it to their dog to answer. And so what you're do, so the surveys are one problem, but then there's the other issue of the generic use of ai, which most people do not understand. They think it's some magical entity.

AI these days is just pretty much computing for all practical purposes. And when you're saying, gee, with the growth of ai, I'm experience, it's like with the growth of, I mean, just being there, the criminals are gonna modify their efforts, taking advantage of whatever technologies come along and do what criminals do. That's where the money is.

Or if they're gonna be, well, actually we we're kind of lucky that people, you know, there's a lot less people just doing things maliciously, it's turned more into a business than just people being vandals. So that's a good thing. But the reality of the situation is that yes, ai, and I use Dr.

Evil quotes for ai, is really nothing more than a set of algorithms that allows you, for example, to personalize phishing messages. It allows you to go through and sort data to allow you to more personalize your attacks to the targets, as Chris was implying. But people have gotta understand that stop b******g about this stuff and start realizing that there are likewise AI tools that are commonly in use that they don't realize they're using, for example, like securing nail gateways or implementing AI models like they have been for more than a decade and a half anyway.

And that yes, there's a bit of an arms race, but for the average technology user out there, they need to make better use of the tools they have and stop being ignorant because the vendors to their credit, are implementing ai AI models into their tools that is stopping the more advanced ai. And if people would make better use of what they have, they would be, I don't wanna say immune, but they would be better protected. And I will now get off my soapbox and pass it to somebody else before I have an aneurysm.

Fred, what about you? You're on the front lines here, man. Uh, well, I, uh, IRA, it's always tough to follow the, the standard silt box, but what I wanna say here is, um, you're right at comma, there's an awful lot of, uh, agility that we don't have today, regardless of what technology you choose to use, you know, and the things that we talk about that's been hyperbole for like 10 years, 15 years, about whether or not the hygiene problem is something that we can, you know, tame and solve.

Here's an important set of factoids for you. Over the last two months, we've had 12 rce, nine of which have been o days. So we can say all the things about what we have and what we know, but those are truths.

So we're not prepared for those things because we don't know about those things. And the rapidity for which, and the veracity of which they're being weaponized is something that is new. Now, in fairness, I'm a detection engineering vendor, so I have an opinion on it based on what I do for a living.

But the critical moment is we're talking about companies and people that have invested all of this money. There's another trailing, uh, indicator here, which is the number of folks in cybersecurity that are no longer operating with the same budgets, capacity or expertise in major industries because they're being cut. And so compare those two things, right?

To some of the things we see happening in national infrastructure. Cisa, what do we do with CVEs NVD and what's happening across the entire industry? And these are going in the opposite directions.

So I agree with you a hundred percent, or I think right now we're in a place where there should be deep concern because there's both alacrity from the business on the impact that this possibly can have. And there's also, you know, velocity and veracity of what adversaries are doing today. Let me, let me add a nuance very quickly.

The nuance, however, is it has nothing to do with ai. It has very much to do with the poor funding, the poor infrastructure, the reduction in resources from the government and everything like that. Not in ai, because preach, Preach it, preach it, And I will, I will pass it on.

Yeah, Lemme try to take that and, and riff right back across it. So, so Fred, I think is, I think what we're showing with like CVS and, and, and, and zero days and so forth is the, the fragility of the infrastructure, right? You know, the fact that we're relying on someone to identify a vulnerability and tell everybody, and respondent time is like the banks I remember in the, in the early nineties, right?

And everybody was, you know, saying, no, the online banking will never happen until everyone has a three physical tokens and, and turns out the banks don't care. Well, it if your bank, but the reality is that the insurance cost of just saying, you know, fine, we'll just pay. That means that I don't have to pay the infrastructure cost, which is exponentially more.

And that's where we've been to date, right? So the idea that we're going to continue to be as secure as we've been, because we have things like the ability to identify, uh, vulnerabilities and share them fast enough is flawed. So we need to go back to, and I take your your point Ira, like the acronyms, you know, yet again, we're calling something artificial intelligence to be clear.

They're large language models. And I, I agree emphatically with almost everything you said. And the differences for the purpose of an audience like this don't matter, right?

This really is literally just what it is. It's a matter of acceleration. The adversary now can move at this speed because of whatever technology increase.

You know, if you couldn't see that coming, you were missing things. The, the specific aspects of the fact that these are semantic models, large language models that have certain cap other interesting capabilities is mostly irrelevant, particularly to viewers today, right? Just understand that just because it was comfortable for the last 35 years or so, to do things a certain way and avoid doing other things, doesn't mean those other things aren't still there.

You need to know what's going on. And waiting to get an alert and being able to jump and press the button at the last second was never a long-term plan. Yeah.

Chris, So I go back, I something you said on the, the banks, right? Just taking the, taking the insurance fee and, and something Fred said, 'cause Fred, I think clearly outlined there are more and worse threats coming, and yet budgets are being cut and the people are being cut, right? Like the skills, like is this a fundamental Yes.

Shift the risk tolerance Of the Yeah, it is. So no, but here it's not a fundamental shift. It's a fundamental secret that's coming out, right?

Between these three gentlemen on the bottom of the screen and myself, we have over a hundred years of cyber security experience sitting here. And I'm gonna ask all three of you in your entire careers, and most of us have been at this for 30 plus years, the four of us, right? Have we ever been at a time where we felt secure, where we felt cybersecurity, got the budget it deserved, where we felt that we were one step ahead of the bad guys?

Never, never, ever, let's not kid ourselves and think we came from, from Nirvana and we're descending into the seven G layers of Hayes. We'd been in hell all along for 30 years. Guys, it's getting worse though.

It's getting worse for the things Fred said, right? We are in all of a sudden, at least for the last, let's say eight years, we've seen security budgets actually, they freed up a little bit. They let you buy your shiny new toys, they let you buy the latest app sec this sec, that sec, that sec.

Every other sec. And now these boards are saying, wait a second, I'm tired of buying you shiny new trinkets when you haven't fundamentally changed the risk equation. Tell me, what is my risk?

What is my exposure? Oh, and by the way, whether it's ai, bi or pot pie, these phishing things are getting better. These guys are using better tools, right?

Well, I come, Alan, I fully agree with you, but I still come back to it, is we are, and this is probably not a good thing, but we are in many ways dependent upon the vendors out there to implement AI into the tools. God, I can't believe I said that. To implement better algorithms recorded it's I into it's recorded.

I know. Yes. It felt good on it though, didn't it?

Yeah, I'm glad. But we are dependent upon them to use tools and we're dependent upon vendors who are well equipped and not just the latest and greatest vision that came out of a VC and all of a sudden just got like a hundred cajillion dollars that we have to rely upon them for a large extent. At the same time, and again, I'm kind of biased 'cause my current company does this, but we need, frankly, CISOs to go ahead and understand business aspects of cybersecurity.

Ironically, the presentation I'm giving next week at InfoSec world is the art and science of being a ciso, which fundamentally includes the fact that CISOs have been using technology and, and advances in technology as a tool, but not as a strategy in how they run their program. Because a COO, for example, if they want to determine if they're gonna put a new factory in, they go to the operations research department or whatever they call it today and have it mathematically modeled in cybersecurity. We're not using that.

We're basically saying, oh, there's all these ais and blah, blah, blah. And they use them as a specific tool to implement a technology better not in how they manage their program. Not in how they can go out and say, if you give me XI return Y, which is available, but they don't know how to do it because they don't have the business background of everyone else.

No, But here's fundamentally though, guys, this cybersecurity crisis, this cybersecurity, and I don't wanna use the word crisis, that's penny, penny, but the cybersecurity posture that we find ourselves in is bigger than any one company can handle. And that's been a problem in cyber for a long time. Maybe 50 companies in the world can really do their own cyber soup to nuts.

The rest of them, they rely on public private partnerships, they rely on the vendors ira, they rely on, on, on the community to do this. And, and, and it's failing. That's the fabric of, that's ripping.

Go ahead. I, yeah, yeah. I think, I know we're at time as well, but I think this is, it's not a crisis.

It's a, it is an evolutionary crux, right? You, you could be a CISO and have learned all how to do this and taken all the lessons and, and brought 'em into corporate environment and succeeded to this point. But the conditions are changing, right?

This is a Cambrian, you know, pick, pick your biological model. And unless you're coming at this to say, how do I actually secure this or break it? If you wanna think about it that way, then you're just following rote notes as you say, Alan, from folks, you know, those of us who were back there in the days knew that this, what we're doing right now is not complete.

So these rules are wearing out. They're not going to work next year. Maybe not next months.

Fred, you've been at cso, I wanted to give Fred a chance, Sarah, Fred, you've been at Seeso multiple times, you're now helping them. What do you think? I think the fundamental thing here is we have to be, we CISOs have to be careful what we wish for, right?

You wanted to see at the table, we gotta see at the table. And actually what that means is you're treated like a business risk like everything else. And so now we're looking at the difference between operational risk.

Like let's say I'm a large shoe manufacturer and I can't manufacture shoes for a day. The difference between that and a breach expense, cyber insurers and so on and so forth, not even comparable. So it's a real true, just another business risk, and that's hard for cybersecurity professionals to understand or to agree with.

But that is the discipline. So, you know, somebody I think, uh, might've been Chris Gates a while back was saying, you know, it's really interesting or concerning that we've, you know, gotten to a place where all of this, the magnitude of what we're dealing with in the industry has gotten so much bigger. It's the same as Chris and Ira said it's the same thing just at, at a much higher velocity.

But the bottom line is, is that if we wanted this to be something, the business treated as a business problem, it is, we just don't like the outcome of it. So if we're talking about the value, I, I'm gonna give you the last word, then we gotta move on. Yeah.

Oh, no, I appreciate that. The issue though, that a lot of people are not addressing here, in my opinion, is that in many ways, a smart CISO has begun to outsource a good portion of their problems. So for example, we're outsourcing to the cloud providers, we're outsourcing to Office 365, Google App, Google apps, whatever you call those things.

And that is taking away a lot of our vulnerabilities as well. And we need to understand that there are ways for smart CISO to o not offshore, but you know, to outsource a good portion of their infrastructure, which will reduce their threat profile. But what, but when the stuff hits the fan, it's still the CSO who's in the hot seat.

That's Right. That's correct. Um, I'm not saying it's perfect, but I'm saying a good portion of it can be handled if you are relatively small company or even mid-size by outsourcing a good portion of your security to others.

Agreed. Guys, I'd love to talk to the three of you for the next two days on this, but we can't, we gotta take a break here on tech strung Ben Gang. We're gonna come back and talk about observability.

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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 as Alan alluded, yeah, this next topic, we'll have a security hook into it because what we're seeing here from a report that the folks put out from Splunk, which you know, might arguably be a little self-serving on their side, but it makes an interesting point.

It says that Observability is starting to be everywhere. It used to be Observability was kind of driven by DevOps. Now we're starting to talk about IT insecurity.

You see the networking folks talking about it. Heck, even the average IT admin is starting to figure out that maybe they need to do more than just monitor stuff. But Alan, is this an overdue conversation?

What do you Think? Um, I quite frankly, Mike, I I, I think this is, uh, it's not overdue. It's a little behind the times.

First of all, let me, let me just say, I've always admired Splunk, big, big fan of Splunk and their observability report, they think they've been doing it now for two or three years, is actually a good observability report. Splunk generally does good reports. We've helped them some over the years at Techstrong with, with their report.

So kudos to them. But this, this is kind of a gee whiz cap. Thanks, captain Obvious kind of thing to me.

The rise of Open Telemetry, open Telemetry Rose, you know, five years ago, it's the second largest behind Kubernetes itself. It's the second largest project that the cloud native computing, uh, foundation, everybody uses Open Telemetry. In fact, the entire observability space is based on Open telemetry.

You don't think so? No, Absolutely not. I think Open Telemetry is a lovely idea and is the second biggest project in terms of contributors.

But actual usage out in the field is not nearly as high because open Every observability tool, open Telemetry is hard to use and hard to instrument and used to be lot Accept. If you're gonna, let me, let me, let me, let me preface that, Mike. If you're an end user organization looking to use the open source, open telemetry and the crappy interface it comes with is your observability tool.

You are correct, but that's not what Splunk is talking about here. What Splunk is talking about is every single observability provider is open tell under the covers for my security friends on the panel. It's like when every IDS had Splunk and every vulnerability manager was nessus it, it has become the defacto underneath along with Prometheus, which is another open source tool.

And, and the, and the folks at, uh, with the G Labs, and I forgot their name now, Gana Grava Grafana Labs. Yes, thank you. You, you, you'll find that there are plenty of IT organizations still using the proprietary telemetry data collectors because they're easier to manage and they're smoother and they're just better now, you know?

Is that the long-term trend? Probably not, But no, I think they're going to observability because also the whole observability thing gets, that's look data dog injustice now and gives you a nice interface on it or, or, or PagerDuty or any of these kinds of collectors. But let me, let me put this out to you and I'll bring it to the side.

I'll bring it home to cybersecurity for us. I, I posit that observability is this generation sim, And remember, you know, we all did our sim, we all did our sim exercises, spent a couple million dollars on sims and it was going to, it was gonna collect everything from everywhere and show us anything and everything. We're still waiting.

I I think we have a Fred, you're shaking your head. Go ahead. Uh, first of all, sorry about that.

Agree, uh, as, as a somewhat of a contributor to that problem, but the, the observability metrics, I think you're absolutely right. The instrumentation required to make sure your cobe clusters run effectively, that you have observability into all of the, uh, outsourced services that we now call upon, right? It's huge when somebody says, why does this, why did I not get the right results back from my model from such and such, right?

And I have service levels that I have to adhere to. The first indicator something is going wrong, whether it's cyber or otherwise, should probably be, you know, the bellwethers of observability. I mean, we, we certainly think about it in this regard, and I know, you know, Mike, I I would say maybe folks that have data centers are probably more in line there.

But if you use a cloud service provider, all of this instrumentation is something you've gotta, you've gotta weaponize effectively if you're gonna run a business. I mean, there's no way not to anymore. So here's my, here's the question I'm trying to get to in my head, and I guess I'll throw this at Dan.

So if we have three or four different disciplines that are investing in observability, should we just have one observability platform? And maybe that's tied to one data lake and we can have security and networking and DevOps kind of all using the same telemetry data. It makes a lot of sense, Mike.

You know, uh, a lot of challenges in getting there for sure. Um, but you know, I, I think in the, the, you know, the perfect world for observability, you know, it's driving your data observability, it's monitoring your infrastructure. It's really built into how you build applications so that you can, you know, optimize those applications as you're building them, make them really efficient.

Um, you know, it's feeding into the financial side on the finops and really helping to, you know, bring the cost into, into containment. Um, I thought, you know, it's interesting, you know, this survey coming outta Splunk, you know, Splunk's really pitching their platform now as a data fabric. Yeah, right?

Um, you know, this thing's really got tentacles that's kind of going everywhere. So, you know, it's a novel concept. Like what if we could actually see what was going on?

Um, I think there's been other talks on this show about it's actually pretty expensive to collect all this data. Yeah, it's, and if you're not using it for something to really optimize and drive business value, this could be a, you know, just a, a money hole that we pour money into. Uh, but I, I like the vision, Mike, of, you know, that kind of unified data set that goes everywhere across the organization.

Um, but I think, you know, a lot of challenges in getting there, observ, it's everywhere. Well, I'll just say, yeah, like Java, I mean, I mean, I'll Yeah, sorry. I'll just say that there's another aspect of observability.

Maybe you consider it cybersecurity, but observability can help you determine availability. I've been involved in very large infrastructures where one element, a bizarre element could be impacted and go down because somebody set a stupid rule that had a cascade effect to take down a major system. And by having observability and knowing where that's coming from in real time, you're able to much more accurately pinpoint in all in the ideal world where the situation originated from, so that you can go back and fix it quickly.

'cause otherwise you could be losing millions of dollars a minute or avail, you know, availability if it's critical and so on. Yeah, the, the topic is taxonomy, right? So I'm gonna start with, you know, from the last segment, you know, artificial intelligence is the wrong word.

Security information management, sim is the right word. And from the earliest days we've been arguing about this, you know, how do we even have, you know, the cv, right? You know, common vulnerability, enumeration, enunciation, we're, we've been circling this forever.

How do we even have the same language in the logs? And Mike, you know, so if we come back to this, let's have a data lake, let's have all of the no, right? We need to, to get taxonomy and to, to the point of this segment, I think this is a good evolutionary, uh, indicator.

You know, we need, we're getting forced into dealing with the fact that we need to enunciate things the same way. Country of origin. You want a current issue right now, get a couple of us, you know, working group geeks in the same room and just say that phrase, we'll lose our minds.

However, you know, I work, uh, we all live in this global supply chain environment of physical and, and virtual things. And if I can't understand the basics of the information, the attestations that I'm getting from wherever I need at this moment, without hoovering it all up, making a massive data lake, you know, taking up a data center that covers the state of Arizona, then it, then I'm missing basic things. So this is such a long thread.

How do we even see, you know, you know, Alan, you and I have fun on this show with IT security information and event management, right? The fact that they put an an E in that acronym flawed the entire market for 20 years And did the artificial intelligence before the i is the i after the E, there's no C there, but, um, but so We did in, in logs, but When we were doing the sim thing, the, the holy grail then was, oh, it's a big data problem. We need Hadoop, we, we need, you know, big data.

And then it went from that to, well, we got this big data thing, but now we need machine learning to, to go through all that data and make it actionable for us. And it never quite worked. So now we're onto something else.

Wait, forget the machine learning. We got as Iris says, ai, and that's going to solve our big, you know, our basic big data problem here. It may, I think AI's better than some of the other ones we've had at it, but it, it really to Mike, to your point about, you know, collecting all of this data and using it across the breadth of it, including security, it's still an actionable intelligence problem.

It is, I'm hopeful, I'm hopeful that the natural language interface will mean that I don't have to learn these arcane programming languages that the observability platforms put out there, and so that I can figure out what's going on. But Fred, you know, I think part of the problem with observability is when I talked to one person about it and they were like, well, that all sounds great, but I have no idea what questions to ask in the first place. Well, I think you're right.

do to ask questions. Now you can ask questions in English or whatever language, but the important part here is you, you arrive at that conclusion of what questions to ask because you know your infrastructure, right? Only you can prevent forest fires.

If you don't understand that then, and you don't understand the observability metrics that drive your business and you don't understand your service level agreements, then none of it's gonna matter anyway. But I think the centralization of that problem is something that we've been, you know, wrestling for, it's all harmonics, right? Originally we said, Hey, everything's on a mainframe.

Then we said, we need terminals. Then we said, we can need mainframes now we need cloud. It's all the same harmonics of going back and forth between distributed and centralized and distributed and centralized.

Whether it's compute, it's data centers, it's, it's data, it doesn't matter. It's all the same. But what we know now is we have enough capability that we can distribute that effectively without having to, as Chris put it, which is create the lowest common denominator in the taxonomy that reduces the value of any of it.

So now we have that ability, and I think that's one of the really interesting moments that AI cannot provide, is that I just have to ask you simple questions and understand the context. Well, the nomenclature, are we saying all the same words? Doesn't matter anymore.

That's okay. Excellent. Right?

You can, yeah, with ai, we can traverse multiple canonical tech taxonomic domains, right? And that's truly fundamental, right? And, and you know, I, in my last little rant, you know, I, I hope I made sense because those of you out here watching, you can't imagine the frustration because 20, 30 years ago we're like, oh, this shouldn't be that hard.

And we're still quibbling about what an acronym means. I don't know if we could ever solve that, but I know, Fred, you said it exactly right. We can now do this.

You can say, you can literally talk to your interface. Here's what I want, and have it give the answers. And it, you know, trans, i i how to put this in fewer words, little, we, we tried so hard to have little translators at edge domains.

It's really, really hard. It's almost as hard as the actual problem. And I get the feeling we can actually do this one now when we gotta end.

So yeah, Guys, I gotta pull the plug people. It's the weekend starting. People gotta go.

I'll leave you with the, with this though, all of this great stuff. Does it make us any more secure? Think about it.

This is a great week on Text Drunk Gang. I hope you've enjoyed it. Of course, we'll have our full text Drunk TV following this.

And if you're not watching this on the stream and you had the time to watch it at your leisure, whether it's on our OTT channel or the uh, YouTube channel, text from tv, YouTube, or text from tv, hope you've enjoyed it. We'll be back with more panel. Fantastic.

Thanks guys. We'll have a great weekend everyone. We'll see you Monday with more TechOne Gang.