CyberWire Daily - DataTribe's Cyber Innovation Day: Cyber: The Wake of Tech Innovation. [Special Edition]

Episode Date: November 23, 2025

On this Special Edition podcast, we share a panel from DataTribe's Cyber Innovation Day 2025, "Cyber: The Wake of Tech Innovation." The podcast tech host panel included Dave Bittner, host of  CyberWire Daily podcast, Maria Varmazis, host of T-Minus Space Daily podcast, and Daniel Whitenack, co-host of Practical AI podcast, sharing a wide-ranging discussion. Together, Dave, Maria and Dan examine the intersection of frontier innovation and cyber innovation through the lens of cyber, space, and AI. Learn more about your ad choices. Visit megaphone.fm/adchoices

You're listening to the Cyberwire Network, powered by N2K. At Desjardin, we speak business. We speak equipment modernization. We're fluent in data digitization and expansion into foreign markets. And we can talk all day about streamlining manufacturing processes. Because at Desjardin business, we speak the same language you do. Business. So join the more than 400,000 kids.

Canadian entrepreneurs who already count on us and contact Desjardin today. We'd love to talk, business. When you're flying Emirates business class, relaxing in an exclusive airport lounge, you'll see that your vacation isn't really over until your flight is over. Fly Emirates, fly better. Today we're bringing you a special conversation recorded live on stage at Data Tribe's Cyber Innovation Day. The session is titled Cyber, the Wake of Tech Innovation. I was joined by two of my fellow podcast hosts, Maria Vermazis from the T-Minus Space Daily,

and Daniel Whitnack from Practical AI, for a wide-rate. discussion in front of a live audience. Here's our conversation. Dave Bittner is the host of the Cybar Daily in addition to many other podcasts. Maria Vermazas is the host to T-minus, T-minus Space Daily, and Dan is the co-host of the Practical AI Podcast and also the CEO of Prediction Guard. And you can find him at his booths out back.

So these three podcasters can cover from cyber to, of course, AI all the way to space. And so it'll be very interesting to hear what they have to say about the intersection of frontier technology. innovation, and cyber innovation. Please welcome Dave Bittner, Maria Vermazas, and Daniel Whiteneck. Come on out. All right. Well, thank you all for joining us here today. We're going to have a good conversation. I'm not so sure about what Leo said about us having some kind of a showdown or something.

like that. I would have there would be an obstacle course. Right, an optical course, or maybe they'd issue us all lightsabers or something like that so we could go at it together. But happy to be here and wow, what an exciting event in this new space this year. It's really great to see over the years how this event has grown. What I hope we're able to bring to you today is our perspectives from the unique point of view that we share as people in our unique position in the cybersecurity world, in the tech world, as podcasters, we talk to a lot of people about a lot of different things and often get perspectives that are different from those of you who are doing the day-to-day kind of stuff. So that's our value proposition for you all today. I would start off just with some

introductions. Leo mentioned I'm the host of the CyberWire Daily, which I like to say is the most popular cybersecurity podcast in the world. I also like to say that I'm six foot two and 175 pounds. So take you for what it's worth. Maria, welcome. Hi, everybody. I'm Maria Vermazas. I am host of T-minus Space Daily. I'm also co-host of Hacking Humans with Dave. We are colleagues. I worked in the cybersecurity world in the private sector for about 15 years before moving into podcasting. So I'm kind of an interloper in the space world. And I come at the space space. sector often with a cybersecurity lens. So it's very fascinating watching things changing in space knowing where cybersecurity has come from. So yeah, go ahead. Dan. Yeah, excited to be here.

So Daniel Whitnack and co-hosts of the Practical AI podcast, I co-host that with a guy named Chris Benson, who is a principal AI research engineer at Lockheed Martin. And we've been doing that now for eight years, which is definitely way before. AI was invented. And yeah, got into kind of the data science world back in kind of 2011, 2012. That was a different hype cycle. The kind of Forbes sexiest job in the world is a data scientist. So that seemed like a good idea to me. And got into data science and kind of have been in data science, machine learning, AI ever since. And also founded prediction guard a few years ago now.

Well, let me start with you, Dan, in this notion of practical AI. I think it's, gosh, seven or eight years ago at the RSA conference, it was the year of AI. Everybody had AI. That was the hot topic. But it was a different AI than the hot AI

we're in right now. Where do you suppose we stand in terms of the practicality of those different flavors of AI. Yeah, I think just for people's context, like a lot of things with AI, the terminology has got very jumbled, and that makes it hard to kind of decipher these different categories. What I would say is we've had AI with us for a long time.

Maybe that's more kind of machine learning statistical models, things like computer vision or spam filters or that sort of thing. And there is still a huge amount of that that's applied across industry. These sort of task-specific models, I would think of them as not general, but task-specific models still widely used across industry. So very practical in that sense, but not kind of maybe accessible for the kind of general public. And then we shifted into kind of from 2017, if you remember, Google released some models,

things like Burt and other things like that. There were computer vision models like Yellow and some others. And this kind of shifted us into a zone of what I call foundation models. So this is where someone like at Google or something like that that has access to a Bajillion Photos trains a very large foundation model. And the goal is I then take that maybe it's a general purpose object detector model that detects airplanes and people and cars and things. And I want to now detect defects in parts coming off of my manufacturing line. So rather than me starting from scratch, I take Google's model that they've trained on a Bajillion Photos and I just

fine-tune it a little bit to my use case. Again, this is still something that sort of is not accessible to a non-technical audience generally because it involves data curation, model training, lots of iteration, tuning hyperparameters and these sorts of things. Then eventually, what kind of happened was as these foundation models got larger and larger, and as they got trained on kind of more generic tasks, specifically text generation tasks, people found that they could use the models off the shelf. So now OpenAI, Anthropic, Google, whoever it is, meta, trains these very large-scale models on kind of all of the internet's worth of data. And now I can use those models off of the shelf without fine-tuning to do things like create automations

or produce content or analyze certain tasks or generate code. And so that's what has kind of led to this pervasive expansion because now that general purpose model, it's kind of squeezed out this middle between like engineering. It used to be it was engineering data science in the middle. And then over here was business domain experts. Now those business domain experts can kind of skip over the data. scientists and just put their problem right into these models which are approachable and actually

get value out very quickly. So I'm fascinated by one of the things you said there about sort of squeezing out the middle. And because you've been at this as long as you have, so you've seen this general, the general AIs come online. I guess the way I want to phrase this is, is there apparel in making these tools available to just anyone, that there is no longer the gatekeeping of being an engineer and all the things that may come with that to be able to put these tools to use? Yes. I would say that there is, and especially I would highlight that in recent times, because people are familiar now that there are certain risks with individual interactions with these models, things like prompt injections, which is malicious input into the model or maybe

toxicity coming out of these models. But actually, now what we're doing is we're connecting these models to a bunch of different systems under the hood that are not AI systems, but maybe there are other APIs, their data sources, they're external and internal. And so now really what you have is a very easy interface that could trigger a series of interactions with a wide number of systems under the hood and if you kind of now imagine and you know maybe Maria would want to comment on this but you have a natural language interface that could help you do any number of things with systems under the hood from booking a car and kayak.com to changing the configuration on your satellite to you know controlling um things in the physical world

in a manufacturing space. And so obviously, if there's kind of enhanced agency, then that produces a lot of kind of nightmare scenario. So it's really this complicated system that's popped up that I think has increased the potential for disaster, if you will. No, good times, good times. Maria, let's talk about space a bit. Sure.

For folks who aren't familiar with your beat, can you describe for our audience what your day to day is like? Sure. So on T-minus, I cover the global space industry. So there is still a large perception in much of the world that the space domain is owned by governments

and the military of various militaries. But just last year, the global space industry was a $614 billion industry with, I want to say, like 70% of contracts actually going to the commercial sector. So it is a massive, massive industry. We're expecting it to be worth over a trillion quite soon. And a lot of what I cover is talking to commercial companies where, granted, you scratch underneath the service of a lot of contracts and there is a government off and under there. But the space industry, which has historically

been extremely hardware-focused, hardware forward, that's where the cool, the coolness of space comes into play where everyone goes, oh, space is so advanced. And that's usually, yes, on the hardware side, it most certainly is. On the software side, they are very, very, very behind. And this becomes a really fascinatingly scary place when you start talking about the growing threat landscape and attack surface of space systems, which are becoming increasingly interconnected, increasingly consumer-focused, and increasingly using commercial off-the-shelf parts. So I would say my pitch to a cybersecurity

audiences, having come from the cybersecurity industry moving into space, I was shocked at how far behind on some cybersecurity basics the space industry is as a whole, because there's still largely a perception that the military's got this, we're good, we don't need to worry about it. Also,

why would anyone want to hack a space system? Which is really sweet, but just not the reality of geopolitics nowadays. We've talked a lot about various nation-state rivalries that certainly also exist in the space domain. Space is increasingly becoming militarized. The rule book for space, which was the 1967 UN Treaty for Peaceful Uses of Outer Space,

something like that, dictated that all treaty signatories would use space for only peaceful purposes. And that has kind of gone out the window in the last few years. we saw in 2022, the opening salvo of Russia's invasion of Ukraine was actually a cyber attack on Viasat commercial space systems. And that has sort of been like the big, bad sea, bad stuff can happen in space too in the cyber realm people. And I'm not trying to do fud on the space sector here, but there is a, I think of the space sector being about 10 to 15 years behind a lot of the conversations that we've been having in the cyber realm for quite some time,

which I still find just honestly really surprising because going into the space sector, I said they're super advanced. On the software side, though, they've got a long way to go. Yeah, I mean, you talk about being behind, but also combined with this, I guess, notion of safety or the elite nature of space

that it won't be touched. I remember decades ago, in a previous career, I was working in the television world, and I was having a conversation with a satellite engineer. He was the person who was responsible for getting the signal to the satellite and then back down again. And I asked him, I said, John, you know, you've got this transmitter here and you aim your dish at a satellite and you fire up your signal. What's to keep you from taking someone else off the air? And he looked at me kind of puzzled and he said, David, we're gentlemen.

Yes. Why would anyone want to do something bad to something space release? Right. And he was dead serious. Yeah. I suppose now that notion is kind of adorable. Still prevailing, though, I would say. Right. So we had this story recently that we both covered about, I think it was Wired Magazine. There were some researchers at the University of Maryland and elsewhere who basically they just put out some antennas. Yeah, just one, I think. Just one little. antenna, yeah. And started listening to the frequencies that satellites use, and they discovered... They discovered a whole bunch of very sensitive military criminal investigation, anything sensitive you can possibly imagine, being sent in the clear. And it should not have

been. This should all have been encrypted, but it was being sent very much in the clear. So pretty much as long as you had an antenna that was just listening, you could spy very easily on the operations of the whole bunch of military and police operations and very, very sensitive missions. I think they used an $800 antenna and they just kind of parked it on the roof of a nearby parking garage. There was really very little sophistication here.

And it is sadly, honestly, that easy. Going back to the Viasat attack in 2022, hacking satellites is really cool and sexy. I know at DefCon, we love to take a look at that. But it was the ground systems that were hacked in the Viasat's case. Just unpatched systems. in the ground systems. It was just a matter of basic security hygiene that wasn't followed.

This is a very familiar story in the cyber security world. And yeah, that allowed the attackers to completely disable a whole swath of communication satellites over Eastern Europe, basically hiding Russia's opening salvos in their invasion of Ukraine. So that was just one example of how cyber and space often are intersecting nowadays, because they are both seen very much as a front line in conflict increasingly in geopolitics. And of course, they are increasingly intertwined. Is it fair to think of space systems as ICS systems, as industrial control system? Yeah, that's often how I try to think of it because satellites themselves are basically not as not solely anywhere. Largely they are just transmitting data. They're not doing a whole lot

of thinking on board, although that is changing. Edge computing in space is still talked about a lot. It is going to become a thing, but it's not really a huge thing yet. But yeah, it's a A lot of the things that are going on in space are really a lot of the same problems you see with industrial control systems. But increasingly more systems that are using space as a dependency are becoming cellular-enabled, you know, directed device is huge. Satellite IoT is becoming bigger and bigger. So a lot of the familiar problems that we've talked about with ICS cybersecurity for decades are absolutely going to be applying to space systems as well because more and more organizations, more and more commercial entities, more and more governments are becoming dependent on satellite connectivity for. all these different applications. We'll be right back.

We'll be right back. So we've got a lot of people who are interested in investment here in our audience today. It's a big part of what the Data Tribe Challenge is about. of course, and I'm curious, the two of you, to what degree do you believe with AI are we in a bubble? Now, I'm thinking of two different bubbles. There's the financial bubble, the investment bubble. There's a joke about the only company making money off of AI right now is Nvidia, right? Because people are buying cards. but there's no question that it is the hot space to be in to to put your money um so there's that bubble but then there's also i think the general sort of uh public fascination with it are we

are people going to get over that we're enamored with it right now um dan let me start with you yeah well um first off i have to say sometime this week our podcast episode will go out which is on this topic is AI a bubble. So thanks for the question because I'm glad I'm glad I prepped. Yeah, I would say there's, I mean, first off, in reference to the Nvidia thing, certainly they're making a lot. I think the ones that are making a killing in the, in the AI space are the services company, so the KPMG, Deloitte, Accenture, et cetera, because you sort of have all these general purpose AI systems, but everybody knows it's kind of like when you buy a CRM. It's a general purpose CRM. You need some consultant or like a NetSuite or something like that.

Often you need actually a services layer on top of that because it's too generic for your company. You've actually got to build in your domain knowledge. You've got to connect your data. You've got to do the integrations. You've got to do all the security work. And so there's a big business right now in that on that side of things. But you've seen a couple of things. The Nvidia, I think now $5 trillion valuation. You saw what was kind of coming out from Powell. I believe it was last week when he said,

we're not in an AI bubble because I won't name the names, but they have revenue or something like that was his statement, which I think we could infer certain things. But yeah, I think there, there is the reality that AI companies are actually bringing in revenue which is kind of an argument

for the we're not in a bubble side of things I do think that there's still speculation because I very much hope that kind of a generic chat application and chat bots

proliferating everywhere like the chat interface is not the killer AI app. The sort of killer AI things are those verticalized domain specific AI plays that, like I say, figure out how to take that general purpose model or those general purpose AI systems and infuse them with domain knowledge and data integrations that are specific to a particular vertical. So I think those are particularly strong in terms of their potential for revenue, where there's, I think we've already seen this over the last couple of years, but you've seen many companies that were maybe had speculative investment that were just

kind of maybe thinner layers, thinner generic layers on top of an already generic AI system. And those have kind of been consumed by the underlying AI system. So things like code execution, web search integrated with the AI model, tool calling, those sorts of things are now kind of part of the platform and are part of AI platforms that you just kind of expect to be there. And so I think those are, from my perspective, the more riskier side of things

because those kind of are being consumed by that underlying platform side. On the cyber side of things, since that is the topic of this conference, I think you've got a series of plays that are, you know, AI for cyber. This would be, you know, application of AI agents to help, you know, the SOC or whatever. And then you've got, you know, security for AI. With me coming from the AI perspective, I'm coming much more from the kind of security for AI side of things.

but there's certainly a lot of interesting things happening on the AI for cyber side. But I do think there's a lot of interesting open problems on the security for AI side because these systems are getting more complicated. Like I said, it's not just a model where you put a guardrail on top of the model and that solves your security problem because you've got supply chain vulnerabilities that have to be taken into account. in terms of the model assets that you're bringing into your organization. You've got hosting and code execution problems, which have to do with both the code being generated from the models, but also the code running the models and where that's communicating and the dependencies there,

the networking around those environments where you're hosting the models. And then you've got the online side, which of course involves all of these things like insecure, tool calling, prompt injection, sensitive data disclosure, toxicity, etc., which have to be monitored at the kind of application level and have to be integrated back into centralized alerting and monitoring. So there's no shortage of problems there that people will necessarily have to deal with on that side of things. Maria? Okay. To address the, is AI a bubble in the space? I guess in this case, it would be a vertical, kind of to piggyback on what you've been saying. Certainly,

when I'm feeling very cynical, there are many players in the space domain that are just slapping AI on a product and going here, we're fixing a thing. But there are two, there are several areas in which I'm actually very excited to see AI playing a huge role in advancing space capabilities. Firstly, on the cyber side, actually getting to what you are saying, given my thesis, which people are free to disagree with, that I think the space industry has. has a long way to go on the cyber side. I think AI can prove to be huge in helping them catch up at speed

because certainly with geopolitical tensions being what they are, that is a really growing, pressing need. On the application side, just thinking strictly within space as a vertical, this is something where AI has just been absolutely transformative because, again, with a lot of space systems being very hardware focused, they're really good at hoovering up terabytes of data

to, you know, thinking of things like Earth, Earth observation, where you have a satellite taking hetabytes, terabytes worth of data every day in high resolution. And historically, a lot of that data just sat on a server with nobody to look at it unless someone thought about it and then had the ability to go and pour through all of it. And now with AI, insights can be gleaned very quickly from this massive amount of data. And there are a number of space companies that are also putting AI or hoping to put AI on the satellite itself.

So the data doesn't have to be beamed down and then analyze. it is being analyzed in real time on the satellite with the insights being beamed down very quickly. So speed and revisit times have been historically a really big problem with satellite imagery and data being useful, especially in combat situations or other situations where illegal fishing is something that's being monitored.

But there are a lot of industries that are popping up within the space vertical that are now able to take advantage of the high rate at which AI can pour through this data. So like insurance is a big sector that's really benefiting from this. Climate change and disaster relief is another one. A lot of local governments are now able to study the landscapes of places within their countries to see how things have changed after a flood and then predict things to AI, what kind of

mitigations they need to take, which I actually interviewed the World Bank. Yeah, the World Bank about this about a year ago. This is something that would have taken some countries like decades to do, and now they can do it in months. I mean, the speed at which the improvement can happen is just massive. So I'm, while I can be very cynical about AI personally in the space domain, I have just been blown away to see how people are using it. And there is a whole cottage industry blowing up within the space domain of people just trying to figure out, now that we can do all this stuff with the satellite data, what can we make with that? And people are really trying to figure that out, that kind of killer app idea.

So it's coming. Yeah, I also wonder, like, what would it look like if the bubble burst? And obviously, lots of people would lose lots of money. That's a bad thing. But at the same time, the Internet didn't stop working after the dot-com bust, right? In some ways, it enabled, it cleared things out and enabled new things to happen. One thing I think is interesting from our point of view as podcasters. I know certainly for me, we have the perspective of getting pitched.

by everybody. Everybody wants to promote the thing that they're doing, their company, their service, their product. So we get to see and survey a lot of the things that they're going on. Where I'm going with this is there was a story today, actually in today's Cyberwire. MIT had teamed up with a private company.

I think it was safe security, is the name of the company. And they put out a report, a white paper, a report, whatever you want to call it, about AI and the proliferation of AI among ransomware groups. And it was factually problematic, let's say. And some high-profile researchers looked at this and said, this is not based in reality, this is wrong, these numbers, there was a number like 80% of all ransomware attacks make use of AI. And they were naming ransomware groups that don't exist anymore or stopped existing before we had the current AI boom. So there's all sorts of problems with this report. And they pulled the report.

What's interesting to me is the combination of an organization like MIT with a stellar reputation, partners with a private company. they team up to do some research together certainly funding has changed hands to make this happen and then something gets published that's full of errors factually wrong in a lot of ways and we have to

weed through that right we have to decide is that newsworthy in this case the story is the retraction so there's a you know the stricent effect just kind of working against them on this one. The story is the retraction, so it's going to get way more attention

than it otherwise would have. But I'm curious, you know, particularly for you, Marie, I don't know what your process is, Dan, for guests and things like that, but sorting through the noise, the marketing noise, that is so pervasive, particularly, again, with AI stuff, to try to curate, to figure out what is it we're going to share with our audiences,

that we've earned who've come to trust us. What does that process look like these days in this? Is it fair to say, let me start with you, Maria, that is a high noise environment? Yeah, it's certainly is. It's gotten a lot noisier. Yeah, and I sympathize a lot with companies because, again, I worked in the private sector.

I was on communications teams, and I know that especially if you're in the B2B realm, it is really hard to figure out a compelling narrative every time you have like a GA. It's just really, really hard. So, you know, companies are trying to figure out how to make their story sing so they can get that media coverage, and it's a hard problem. The issue that I'm seeing is I'm getting a lot of pitches where the story is really garbled

and lost in there. They've really leaned heavily on AI to write them something, which is fine, but it really needed an edit. And the volume I'm getting now is sometimes getting a little overwhelming, and it is getting more challenging to cut through that noise, because I know that listeners to T-minus trust that humans are actually curious. the work that we're doing. We're reading it. We are writing it. Like, that's the human touch is extremely important in what we do. And AI is huge in space. So, like, we're reporting on space a lot. I've interviewed a ton of companies who are using AI in space to practically miraculous effect. But the human touch is so important. And it's, it just feels like it's getting exponentially

harder to keep up with. Dan, what are your thoughts? Well, I guess our hot take on this is we just ignore everything um because uh we've we've we've always so uh chris and i just sort of think about what we want to talk about and then we and then we find folks occasionally there will be an outreach from you know someone that that is legitimate you know i think um uh waymo outreach and we work through their press team and found someone we would want to talk to so there occasionally something like that, but our advertising, we do have some ads, but that is completely, actually, I don't have anything to do with any of that. All of that comes to a separate team, and I don't even know what the ads are that are going to be run, and there's

no connection between that and the content. So a lot of it is just, maybe it's a selfish thing, because it's my weekly excuse to spend an afternoon talking about something that I want to learn about or something that, you know, we found interesting and outreach. So a lot of what we do is outreach now on that side. And maybe we're missing things because of that, but it's fun that way. Fun matters in this stuff. It matters a lot. So yeah. Yeah. We're going to get to questions here in just a second. But sort of, Piggybacking on to that, I think, and also I guess in some ways related to that MIT story, look, there is, I'm not against sponsored content.

We have sponsored content. It is part of how we keep the doors open, right? You've got to, you have to pay people. It takes teams to do the things we do, and you have to make money to keep the doors open. And sponsored content is one of the ways we do that. But we are overt, perhaps overly overt. when we have sponsored content that to say

this is sponsored content so that people, there's no ambiguity there. And what I'm seeing, what I'm sensing is that there's more and more of a gray zone when it comes to sponsored content

of not being overtly called out. And my personal feeling is there's something kind of icky about that And maybe I'm naive and innocent and cute. But I wish we had higher standards when it comes to that. Yeah, I think it also puts some pressure on kind of all of us in the room when there's such a proliferation now of generated content as well. you know, I'm not saying the MIT report generated their report with AI, but, you know, everything is just kind of AI slop now. And it's hard for us to kind of work our way around

that. I think that's true, whether you're on this stage or whether you're just doing, you know, your work as a VC or a startup or whatever it is. Yeah, because then reputational damage can go two ways on that. So if we get a press release that was poorly written and there's factual errors in the press release, I am not a fact checker. I'm not a literal rocket scientist. So I have to be able to trust what I am reading is correct. So if we publish something like this MIT report, we didn't, but I'm just saying, for example, ends up that was incorrect, then we also lose our reputation. So that's part of the calculus that we're always doing in our mind is, I really hope I can trust this press release or this PR contact or this person that I'm interviewing that what they're saying

is actually true. Yeah. And we run corrections because sometimes errors are made. All right. Anybody out here have any questions? It is hard for me to see. Raise your hand. Yes, sir.

Come on up and come to the mic and introduce yourself and let us know what you'd like us to answer. Yeah, absolutely. My name's Evan. My question, I guess, is for all of you. So the phrase earned audience was just said, right? It's no secret that podcasting is a very crowded space especially when people have access to the same news wires and things like that.

How do you, as the host, capture those ears for the first time and ensure that they continuously come back to you and you alone when they could go to anyone else that's covering the same topic? That's such a great question. Dave, I feel like you. You're the OG. You should really answer that same. Well, so, look, we've been doing the Cyberwire for, at the end of this year, it'll be 10 years.

So there is definitely a component of being one of the first to market with a quality product that uses high standards for audio quality, for editorial quality, all those kinds of things. So it was much easier for us to set ourselves apart at the beginning 10 years ago than it would be today. I believe that is why we have not seen many people come and try to compete with us. right daily news production is a lot of work i like to joke that we make it look easy but it's not right it can be a grind um the other thing i'll say uh i'm a big fan of um steve martin's uh autobiography is called born standing up and i highly recommend um you read it a friend of mine who happens to be in the audience was saying that he has all of his salespeople read it because the like of a stand-up comedian is being told no, it's getting up in front of a group of strangers and having them not laugh

at you before you learn how to laugh. So it's persistence and learning how to do your pitch. Steve Martin in his autobiography says, be so good they can't ignore you. And I think that's a big part of it too. You're not going to be great right out of the gate, but keep at it. I like to think that I'm much better at my job than I was 10 years ago when I first started. I know I am. And so you provide value for your audience. You respect to them. You don't waste their time.

And provide something that's entertaining and valuable. And hopefully they'll stick around. I know the three of us have all been lucky that for whatever reason, what we've been doing has resonated with enough of an audience for us to be able. able to keep doing what we do. All right. Is that answer your question? Yeah. All right.

Next up. Yes, sir. Yes, Dave. Maria, thank you for your time. I was curious, Maria, from a software innovation, you know, sides of things in space. What do you think that may cause of the lack of innovation on software level is, given the fact that we now had accessible hardware, but Jets and Nano and Raspberry Pi

and Python models that can be run using. onics one time of point, as well as accessible data, things like GNSS, lattice that's available with electromagnetic signals and things like that. So why aren't people building these solutions with the data that's available and the hardware that's available when you really just need a procuter with VS built and Python sold? Well, it's a great question. So some of it is just making money. It's a matter what are we building these systems for in a lot.

Again, space is a business largely. So, but yeah, there are a lot of. lot of PICO satellites and CubeSats that are using literally, it's just a Raspberry Pi in space, but is that enough for a business to achieve what they want to do? It might be for maybe a small mission, but it might not be equal to do what they need to do at scale. Hardening systems for space is indeed a challenge. It is something that is very extensive to do. But we are actually, to your point, we're at a really interesting intersection right now in the space industry, and it's one of the reasons I love covering it right now is because it is getting cheaper and cheaper

to send things into space, we are seeing more companies willing to experiment with setups exactly like what you describe, trying to say, like, we're going to make the leanest machine we can possibly make with literally as much commercial parts as we can. Can we achieve what we're trying to do and deliver value or customers with this? Increasingly, we are seeing the answer is yes. So I think some of it is legacy mindset where not that long ago, I'm going to say that 20 years ago, we were able to measure satellites in the hundreds, like maybe each hundred. Now we're well over 10,000. And we're looking at probably 100,000 a lot sooner than always might think. So things are accelerating exponentially, probably, maybe not logarithmic, anymore exponentially.

But yeah, we are moving in that direction. So some of it is people who want to work in space, literal rocket scientists, want to make the coolest hardware that they can. But now that we have a lot more business folks moving into the space industry and or businesses that are realizing that space is a vertical that they have some integration with, we are seeing more of exactly what you're described. So I think it's a stay tuned. Missions in space do take years. Cycles take a long time, whatever Yuan must might say. It takes a lot longer than it might think to get so bit to space. But these Thailand are shrinking.

Just in the last year, we had a mission to space that the Space Force did that took like a 24-hour turnaround, which is insane. So it's just things are going so fast. So yes, we will be seeing more exactly what we were talking about. Right. Thank you. Yeah. When I was in college, my one of the first, my roommate was an electrical engineer and he was also a ham radio operator and uh he and a bunch of his

ham radio buddies actually got a little like one foot cube thing that went up on the space hall and that was how you had to do it but they like to your point about it being cheaper and cheaper to get it in the space i guess that lack of financial gatekeeping leads to opportunities for innovation yeah and that's part of the reason why a lot of us space nerds are very excited about what starship the SpaceX Starship will provide. That'll make it even cheaper to get even bigger things in this space, and that will unlock a whole... There are so many businesses that are literally waiting for Starship to be viable

because it's just going to all on here and more opportunity than the X-Balkan 9 did. So it is just the fact that it's getting cheaper and cheaper you get in the space, there are more confluence that can work in space now. It is pretty much Greenfield. I really believe that. So if someone can figure out how to make their business work with a space aspect, there's lots of play there. All right. We've just got a couple minutes left here. So we're going to rack things up.

I'm going to ask each of you. Let me start, Dan, with you. What's your level of optimism when it comes to cybersecurity, AI? As you look towards the horizon, where we've been and where we're going, what's your temperature these days? yeah i i'm fairly optimistic um i think part of that is driven just by doing iterations of my conversations with people on the podcast but also um discovery calls through the business i i have seen a shift in the past five to six months um a real shift of sophistication of people that are in in enterprise settings around um number one the optionality they have in terms of kind of model and platform, et cetera, but also an understanding of maybe what they need to do from a security standpoint around AI. They might not know exactly how to enable that, but there's certainly more sophistication, less

education there. There's still very hard problems to solve, but my optimism is high. Marie? I am more optimistic now than it was a year ago. but sort of beating the drum that I did at the beginning of this talk, the need is so great in this cyber realm in space. I see more organizations taking it more seriously. Certainly shifting geopolitics, again, has really raised a temperature on this. So I'm more hopeful now, but I would like to see things moving more.

Dave, what are you? I just pray, you're going to ask you. I am by nature and optimistic person. So I would say I am overall optimistic about where we're headed. I think the acceleration, the rate of change that got goosted by AI. Seems like that's going to continue for a while. And I think I tend to not bet against people. I think we are clever and we tend to be able to think and get our way out of the problems that we create for our

ourselves. It can be messy, but I think the long arm of history shows that things too tend to get better. So I'm going to go with that. That's nice. All right. Thank you all for listening to us. I'm Dave Bidner, Maria Vermasas, and Daniel Whiteneck. Thanks so much for joining us. the Tribe's Cyber Innovation Day. My thanks to Maria Vermazas and Daniel Whitnack for sharing the stage with me, and to everyone in the room who joined us for the discussion, we're pleased to bring it to our CyberWire Daily audience, and we hope you enjoyed it.

I'm Dave Bittner. Thanks for listening.