CyberWire Daily -  Turning data into decisions. [Deep Space]

Episode Date: July 4, 2025

Please enjoy this encore from our T-Minus Space Daily segment Deep Space. Parker Wishik⁠ from ⁠The Aerospace Corporation⁠ explores how experts are turning data into decisions in the space industry on the latest Nexus segment. Parker is joined by⁠ Jackie Barbieri⁠, Founder and CEO of ⁠Whitespace⁠, and Dr. Steve Lewis, Leader of The Aerospace Corporations’s ⁠SPEAR team⁠. Remember to leave us a 5-star rating and review in your favorite podcast app. Be sure to follow T-Minus on ⁠LinkedIn⁠ and ⁠Instagram⁠. Selected Reading ⁠Aerospace Advances Massless Payloads for Space Missions⁠  ⁠Aerospace Experts Are Turning Data into Decisions⁠ ⁠Aerospace recently assembled a team of highly skilled scientists and engineers who play a critical role in addressing national and global disruptions in GPS and other radio frequency spectrums.⁠ Want to hear your company in the show? You too can reach the most influential leaders and operators in the industry. Here’s our ⁠media kit⁠. Contact us at ⁠[e-mail]⁠ to request more info. Want to join us for an interview? Please send your pitch to ⁠[e-mail]⁠ and include your name, affiliation, and topic proposal. T-Minus is a production of N2K Networks, your source for strategic workforce intelligence. © N2K Networks, Inc. Learn more about your ad choices. Visit megaphone.fm/adchoices

You're listening to the CyberWire Network powered by N2K. Risk and compliance shouldn't slow your business down. HyperProof helps you automate controls, integrate real-time risk workflows, and build a centralized system of trust so your teams can focus on growth, not spreadsheets. From faster audits to stronger stakeholder confidence, HyperProof gives you the business advantage of smarter compliance. Visit www.hyperproof.io to see how leading teams are transforming their GRC programs.

transforming their GRC programs. Welcome to T-Minus Deep Space. Maria Vermaz is here, host of T-Minus Space Daily, and for today's show, I'm handing the host mic over to our partners at the Aerospace Corporation for the second installment of the T-minus Space Daily podcast. I'm Parker Wyshek at the Aerospace Corporation. Today we're talking about turning data into decisions and we're joined by Jackie Barbieri, founder and CEO of Whitespace, which is based a quick jog from old town Alexandria, Virginia, and Dr. Steve Lewis, joining from Colorado Springs today, who is the director of Aerospace's

Spectrum Electromagnetic Interference Awareness and Response, or SPEAR, team. More on that rather imposing sounding team in a bit, but first, let's set the tone for this discussion, which, apologies to the technology wonks, this is going to be more end user and data layer focused than hardware and comms. Jackie, you're a lifelong analyst, and one of those end users now helping to empower other end users with tools that will quickly glean insights from existing data sets, and at times even guiding the end users into what they want

and need to analyze that they may or may not know. What is your philosophy and big picture observation on where we're at versus where we came from informed by two decades plus working in intelligence. Well Parker, first of all, thank you so much for having me. I think in some ways we are exactly where we were two decades ago in terms of the challenge we have ahead of us. And by that I mean two decades ago we were facing a crushing amount of data in terms of the pace, variety, and just overall size of what we needed to get through in order to support decision and deliver decision advantage

in the DoD and the Intel community. And that is still true. In fact, I only think that problem is getting bigger and more challenging. Another challenge that is the same is we were faced with the challenge of figuring out how to encode and scale what are expert processes and tacit knowledge about how to interpret that information and speed it up when it's an inherently thoughtful process and human-driven process. So I think those two things are still the same. What is different is that technology in some ways has

caught up almost enough for us to envision a future where we can invert that power dynamic or that curve that we've been on, where data and need has been outpacing our ability to deliver. I think that we're at a really interesting moment right now, and maybe a tipping point, I don't want to go too far, but almost at a tipping point where we might be able to wrap

our arms around it for the first time and use it as leverage. I definitely want to come back to this near the end of the segment because we're talking about philosophical change almost in how we look at and use data. You and I met in Austin at South by Southwest at Maxar's orbital edge of Bethesda March, and you shared a little bit of your vision then. You also had a fun demo, really an enlightening demo is a better word. And your approach to arming analysts with deep insights from the right sources to make the right decision within a dramatically reduced timeframe.

I would love for you to share a little bit of that here. Yes, sure. So let me rewind a little bit of that here. Yes, sure. So let me rewind a little bit. Those challenges that I pointed out that we're still faced with today, in terms of getting expert knowledge encoded or scaled via software. This is a process that white space has been involved in for over a decade. So we actually started out facing and trying to help the intelligence community address the challenge of training people in new methods for applying data and

maybe nontraditional ways from nontraditional sources to get out really hard problems and to provide the citizen advantage at a speed that actually met operational need. And so we were trying to scale initially through training in different scenarios, getting people up skilled. And about five years ago, we expanded our capabilities as a company and started investing in developing tools that could augment individuals, intelligence

analysts, and operators as they're trying to get at key information to drive the decisions that they have to make every day. And so that demo that you saw, Parker, was really about how far can we push the envelope? Can we build a credible, reliable, trustworthy tech stack that can enable an operator end user to self-serve information. To me, that's like the hardest challenge. There's so much baked into that. If you're an expert in this industry, you might be shaking your head at what I'm saying. It's not lost on me how hard it is to deliver, but the truth is our team has been chipping away at this

problem for a really long time. And I'll explain a little bit about how we've been doing that. So first and foremost, you got to have good quality data. That's like table stakes. So we've gone out into market and we've identified some of the best sources of commercially available non-pixel information, that's really important for me to state here, that can be leveraged to understand and reveal hidden connections between individuals, groups, locations, events, so on and so forth.

So data is the foundational layer. The next layer here is a set of deterministic algorithms or tools that automate and speed up those workflow modules that we've been training analysts to do for almost the last decade. So it takes a process that would require an analyst, an engineer, a data scientist, and maybe several hours to get to a result.

Now, that's an API call and the results are provided in seconds, maybe minutes depending on the job size. That's exciting. Now, we're going to cross-sources, we're able to chain together these complex workflows really quickly, we're approximating human expert capability all in doing this. But where it gets really exciting is when you layer on top of that state of the art reasoning models, and you apply on top of that an LLM layer that acts as the translation between the end user

and the analytic decomposition of the task at hand. And then that reasoning model has access to all those tools and all that data, and can provide and surface new opportunities, new discoveries that maybe the end user didn't even know to ask about in the first place. So this is when we get beyond automating or speeding up

the work that we already know needs to be done, and we get into that space of opportunity and discovery, which is sort of, I think, what drives a lot of some of the best analysts I've met, is like that hunt, that hunger for the discovery. So I got to play with Iris earlier and you're showing us Iris. Who is it named after? Oh, I love that you asked this question. It's actually named after the Greek goddess Iris, who is it named after? Oh, I love that you asked this question.

It's actually named after the Greek goddess Iris, who was the messenger from the gods down to earth. It also happens to conveniently happens to be related to vision, right? So yet another reason to choose the name. And you said LLM earlier for our listeners, that is Large Language Model. Yeah. Claude is actually Anthropics flagship large language model and reasoning model. We're using Claude 4,

which was just released a couple of weeks ago. This example and use case is really centered on illegal resource extraction. If we were looking into maybe illegal mining activities or similar in say the Amazon, we took a look at different ports that had been known to be related to this type of activity in that region. And so if we wanted to go on a discovery path, like the one we were describing earlier, we just start with a location of interest.

So news reporting cued us into the fact that there was a raid at a port named Manaus in Brazil a couple of years ago. And so I wanted to check in and see what vessel activity looks like in that port these days. So first and foremost, we take a look at what port facilities are in that region. So Iris is able to tell us that quickly. And then I find, okay, this one here is a commercial port. And if I were shipping illegal goods, I'd probably want a commercial vessel to do that on, a cargo vessel or something similar.

So Iris can show us and buffer that location readily for us. Let's take a look at whether or not there are any Chinese vessels that have entered or exited that port in the last 30 days or so. And interestingly, Iris comes back and says, really there haven't been. entered or exited that port in the last 30 days or so. Interestingly, Iris comes back and says, really there haven't been. That's interesting because the news report said that there were

Chinese vessels implicated in that raid that I mentioned from a couple of years ago. Let's open up the aperture a little bit. Let's look at all commercial vessel activity in this location. Let's look at it in March 2025. Iris runs a query for commercial vessel activity in this location. Let's look at it in March 2025. So, IRIS runs a query for commercial vessel activity, and this is a heat map showing where those vessels were most active in and around the port.

And you can see there's metadata associated with each of these regions. All right, well, let's drill a little deeper. What types of commercial vessels were active? And we're seeing these vessel types here, some of which could be used for moving around illegal goods in large quantities. But what's most interesting are the flags of these vessels.

So folks that work in maritime and deal with sanctions evasion, they're very familiar with things called flags of convenience. Certain countries make it very easy to flag your vessel and have very low bar in terms of the requirements to do so. Some of those countries are listed here. So this is getting interesting. All right.

Now I've asked Iris to tell me for all vessels that were located in that port during the month of March, tell me where they went in April. What's really interesting to me are these vessels that are going around the southern tip of Africa and up towards China. See, where this track ends is actually the temporal limitation on my query. It's not the spatial limitation on the data. I did a subsequent query because as I zoomed in here,

this is actually data for just one unique vessel. And what stuck out to me is that this vessel is, ooh, it's actually a pilot vessel. Why would a pilot vessel that's meant to bring the captain or a pilot to a larger vessel when it's arriving in port, be traveling this long distance. You know that. Does Iris know that?

Does Iris know that? And how does she know that? Iris knows that that's a pilot vessel because that is reported in the data. Even if I didn't know that it was suspicious that a pilot vessel would be transiting this long distance, I would be interested in this track. And it's what looks like it's going to be a terminus somewhere near China. So what you call this human machine teaming because you're bringing your own personal expertise to the query that you're generating and expanding. Yes. This is definitely human machine teaming. This vessel actually made multiple stops at

different Chinese ports in the subsequent month. I'm going to just jump ahead to the bottom of where my exploration took me. I made all kinds of different queries. I asked about device location data and if there are any devices correlated with that vessel, where they went. But this was the most interesting thing. At the very end of my exploration, I said, okay, well, tell me where this vessel was on June 4th, which happened to be the day before I did this exploration. And it

was located in two places. One was that port in Manaus, and the other was the Taiwan Straits. And that defies physics. So that indicates possibly that this vessel might be worth further investigation and interrogation. And that's something I was able to accomplish aided by Iris. Again, that human machine team is so key because I would learn something from Iris and that would prompt a new question.

And that new question would provide an answer that would prompt yet another question. And I was able to do this in minutes rather than hours or days of painstaking work combing through data and maybe crossing across multiple databases. Iris is able also to surface for me other cross-reference data like imagery, radio frequency data, mobile device location data, so on and so forth. So that's just a quick look at some of the work that Iris is able to take off my plate, as well as push me to

be able to accomplish on my own in a much more efficient manner. Excellent. So thanks for that dive in on Iris. It's guiding the user through how to progress the prompt. It's acting like an analyst. So I want to come back to that. And the question coming your way, Jackie, is who taught Iris what she knows? But I want to segue to Steve now and his SPIR team that again at Aerospace stands for Spectrum Electromagnetic Interference Awareness and Response. Some similarities in how this team is using data in unconventional Aerospace stands for spectrum electromagnetic interference awareness and response

some similarities in how this team is using data in unconventional ways and Steve I want you to just kind of tell us a little bit about what is spear doing If there's anything you want to build on from what Jackie's provided to date Please go for it. Yeah, absolutely. I recognize that acronym is quite long, hence the Spear Team. There's another piece of this that I think is related to the conversation and that's something we call massless payloads.

Massless payloads, it's software. Very similar, we looked at Iris as an example of a software tool that can be used to process data to extract insights. And so the Sphere team actually pioneered this idea of massless payloads to process commercial data sets. payloads to process commercial datasets. Our initial endeavor was looking at datasets, unfortunately, that were not ideal. It was what data is available from existing hardware as it is on commercial systems without asking

a commercial data provider to make any changes. Just give us the data that is available, and we applied software, massless payloads to process that, to look for insights. The data was often data that's dumped on the floor and not even used. It just happens to be available. The first example was observables or measurements from radios on commercial spacecraft.

And more specifically, GPS or GNSS radios, so the Global Positioning System, Global Navigation Satellite Systems. And so we took those observables, a mix of more exquisite, if you will, to some that are just, they are what they are. Not as many decimal places that we would like to have. We created a Pathfinder prototype to just explore that area, to process the data, to detect and characterize manufactured interference.

That was the goal. The great thing about the Spear team, as we talk about the big problem to solve, software's great, data's great, but there's another piece to this. How do I advance this technology, these techniques, these tools, and transition them to operations? And we saw an opportunity, in this case for aerospace, the Sphere team, to pioneer a Pathfinder prototype, demonstrate it, identify who might be transition agents, and show that Pathfinder prototype and the opportunity, and then look for a transition path.

And what it's turned out to be is prototyping opportunities for commercial companies to come in and then build prototypes to process these types of datasets to solve problems with the transition agent or an end user following along the whole way. The Spear team's role becomes, we want success, so help out the prototyping activity, help out with transition, and part of that is using these tools to explore,

what do the tools do best in solving these problems? What are areas maybe for improvement? And so that's what the Sphere team is about. It has evolved, it's expanded quite a bit. And the Sphere team now is the engineering back shop for an organization called the Joint Commercial Ops, and also supporting some other government organizations as well. So to go back to massless payloads, and frankly for me,

it's taken a while for me to kind of grasp that. The first time you hear massless payloads, you wonder how is that possible? But what you're essentially doing is you're taking onboard hardware and you're not changing how it operates, but you're changing what you get out of it. You're not changing the data, you're changing how you're using the data, how you're applying it. Am I correct on that? Absolutely. And you're so you talked about data sets that are left on the cutting room floor. Essentially what you're doing is you're doing the restaurants just special. You're serving ingredients the next day that weren't needed in the main entree.

But what you've actually done in some of these use cases is you've created an award-winning entree that is now something people are going to your restaurant for. Can you tell me the anecdote you told me a couple of weeks ago about your work with a commercial company of asking for access to their data set and how you brought that to the government, essentially creating a new tool overnight that then has translated into actual commercial work with the US government?

Yes, and that Pathfinder prototype was called Deep PNT, data exploitation and enhanced processing for positioning, navigation, and timing. So, Deep PNT. And you're right, this actually came out of And you're right, this actually came out of my school work. And where it really came, to be honest with you, is I don't want to call it desperation. But as I was working on my degree program, I was told we need to really see this experiment in a real-world environment. Well, for me, it was detecting jammers and spoofers for

GPS for school and I didn't have access to that. So, I had access to public GPS receivers that were near GPS test events that I, at the time, couldn't afford to go to. So, I looked at data from that GPS receiver and discovered, oh wait, I don't have to go to that event. I can just use data from

this device and I can see what's going on, at least enough. I can't know everything. I can't comprehensively solve the entire problem, but I can provide an element of it and something that was good enough. And so, you know, taking that much further, I was able to work with a commercial company that had data from systems

that were already deployed, and they shared that data with me, and I, you know, wrote some basic software code just to do an initial proof of concept, and I wrote some basic software code just to do an initial proof of concept, and I was able to demonstrate with that dataset, we could provide something valuable, global persistent awareness of manufactured interference, or I should say possible or probable manufactured interference. Then with that success,

the key was then finding a way to transition that to industry. And that's really where the success came, right? From FFRDC to industry, and ultimately to operations where it is now. A commercial capability where it's providing value to real world users. And it sounded like when you were telling me this the initial company that you worked with was part of an award but beyond that there were other

companies part of that award so from a minimal investment on the part of one commercial company and some collaboration there was an actual use case, a value proposition that emerged, and it was a force multiplier for engaging these commercial companies to support the United States. Absolutely. And even better than that, that was one prototype. We've actually done five other prototypes across multiple mission areas, and each of those have led to commercial opportunities.

So it has been an exciting ride working with commercial, working with the government, and end users as well. We'll be right back with more from the Space Nexus. Did you know Active Directory is targeted in 9 out of 10 cyber attacks? Once attackers get in, they can take control of your entire network. That's why Semperis created Purplenight, the free security assessment tool that scans your Active Directory for hundreds of vulnerabilities and shows you how to fix them. Join thousands of IT pros using PurpleKnight to stay ahead of threats. Download it now at sempris.com slash purple dash night.

And now a word from our sponsor, Spy Cloud. Identity is the new battleground, and attackers are exploiting stolen identities to infiltrate your organization. Traditional defenses can't keep up. Spy Cloud's holistic identity threat protection helps security teams uncover and automatically remediate hidden exposures across your users from breaches, malware, and phishing to neutralize identity-based threats like account takeover, fraud, and ransomware. Don't let invisible threats compromise your business. Get your free

corporate dark net exposure report at spycloud.com slash cyber wire and see what attackers already know. That's spycloud.com slash cyber wire. Welcome back. Here are our partners at the aerospace company and the NASA space station. We're here to help you understand

how to make your business a better place to work. Welcome back. Here are our partners at the Aerospace Corporation now with more of the Space Nexus. So this question is for both of you. Both of White Space's solutions, Iris specifically, but I know that you have other offerings, Jackie, and Spears work, they strike at the notion that we are not confined to what we have today, to what we know today, that there's some value in the unknown. There's always another lens to look through or ways to use the tools that we have at our disposal.

And both of you have proven it doesn't take decades to transition to that, you know, that operational state. It doesn't take months for an RFI to be issued and fulfilled and responded to. I would love to hear about your response to how the space community is receptive to this idea. Jackie. I really can't say enough about how receptive the space community has been to new, unknown efforts, capabilities, solutions, and the pursuit of that. And really, it's not just the space community.

I kind of put you in a box there. Well, I'd say it's most front and center culturally with them. You're right. It's not just the space community. But I think it takes a lot of courage on the part of a government end user, regardless of what organization or agency they're a part of, to say, hey, I have a problem, and I'm open to creative solutions to that problem.

I'm not going to prescribe the template that it needs to follow. I'm just going to tell you the outcome that I'm looking for. I think that at least we've had the honor and the privilege to be part of a cell called the Tactical Surveillance Reconnaissance and Tracking Cell, TACSRT, that very much has that ethos and in their engagement with every vendor on every effort. And I think that's yielded some really incredible breakthroughs. In particular, direct support to operational end users at a pace and of a quality that most people just assumed wasn't possible before they tried. So I think

that it's been really productive. It makes it really fun actually also to work with them. And I think it's been a good outcome overall for the DoD end users. And that's the most important thing. I would completely agree with you. You know, I mentioned before the organization, one of the organizations I support, the US Space Force's Joint Commercial Operations. It's been exciting and I totally view my role as to help

stimulate some of that creative energy in exploring these datasets. The environment is getting, it's complex already, it's getting more complex. The datasets that we're looking at today, AIS is one example that you shared with us. Is AIS going to look the same tomorrow? Is there going to be maybe a different data provider or we need to pivot to a different data provider for some reason?

And there's probably going to be nuances and we've got to be ready for those nuances with software that can adapt to it. Hard problem, complex problem. I think the community is open to finding some creative, resourceful, rigorous solutions to try to solve those complex problems with the amount of data that we're dealing with. You mentioned earlier, Jackie, and I really appreciated your comment about making good use of operators' time.

I used to be a space operator. We're busy. We only have so much time, and we can only train on so many things. We need help to maximize our time, so we focus on what is the most important problem, you know, at any given moment in operations. And so we need help with that. And I think the community is really open to finding ways of

helping with that complex problem with the amount of data that we're dealing with. Well, Steve, kind of building on that, one of the main reasons that drove me to start the company in the first place was my appreciation very early on and in my career of the gap that could exist between the operator need and what could be provided to them in terms of decision support on a given timeline, given certain resource constraints.

And it felt like it took a Herculean effort to try to even close that one centimeter closer. And I think that with the right combination of culture and requirements, I want to say in the big R way, but expression of needs from the customer perspective and a vibrant commercial ecosystem that is eager to help solve those problems, we're getting closer and closer every day. The rate at which I see those two things coming together

is really exciting and inspiring. And I think that we have so much farther we can go. I'm going to flip-flop my last two questions because that segues perfectly into... The technology aside, this cannot be an easy transition from a mindset from an ethos perspective. So Jackie, I think you're kind of in there. Well, both of you really. You're in these movement spaces. You have stakeholders working with you who are willing to move.

What has to change for any stakeholder to be ready to operate, analyze, and act effectively in this agile way? Well, I think we have to be willing to look at our standard processes with a critical eye. And I'll say that even we have to do it inside of our company too, right? So when you have experts building technology that would augment or in some cases replace tasks that experts currently have to do, it can be very challenging.

So you have to stay and you have to be willing and able to stay in that place of tension between we have a very high bar for how well this needs to function, but we're also willing to try really hard to see how far we can push it and how close we can get to that expert level performance. And so that is a special set of cultural characteristics that need to exist, I think, both on the provider side and on the end user side, where they have to be open. I think need and being overwhelmed

and the pace of operations and the growing complexity you tapped into, Steve, I couldn't agree with that more. Sort of being overwhelmed by the sheer nature of your job will also maybe make you more open and willing to try new things. I hate to say that, but I do think that's, it creates a conducive situation and makes people more open-minded

about different options to explore. Yes, change is difficult, especially when you're dealing with humans. So something that comes to mind on this topic specifically, and having seen many different tools out there, and actually having seen something on a popular platform that a lot of us use, comments about some of these type of tools, data

mining tools. I think it's important to be very objective, very honest about what a tool can do and what it cannot do. And it is okay if it cannot comprehensively solve everything. It probably won't, and that's okay. Communicating what value that it does have and what scope of the problem that it's solving is better because then you can integrate it into operations. A lot of the capabilities that I work with is exploiting imperfect data sets.

There are equipment artifacts that we have to try to filter, and we don't ever have full knowledge of all of them. There are known unknowns, and they're probably unknown unknowns. So as we try to train them. You know there are unknown unknowns. We know there are unknown unknowns. We know there are unknown unknowns. Yes.

But to get that in an operator's hands, I got one shot often, right? I don't want to hand them something, promise it does lots of things. And then they discover that it doesn't have the value they thought it did, and they won't come back. So it's very important to communicate, probably more important to communicate what a tool cannot do, or at least some level of confidence at what it can do or cannot do.

I think that is very important as we explore, you know, this data science, AI, ML, and these technologies to mine data and provide meaningful insights. Steve, I have to jump in because what you just said is one of the fundamental lessons that an analyst has to learn. I don't know, sir. And they have to, it takes a lot of courage to do that and to not tap dance. But that is the crux of how you build trust and how you participate

as a, as a team player. And I think when people flip that switch and they lean into that, their credibility only goes up. So I could not agree with you more on the human level that's so important. I think it's, it's equally, if not more important from a technology perspective. And I think that brings me back to my question that I put a pin in, Jackie. Who taught Iris what she knows? So Iris has been developed by experts who have been involved not only in actually doing this type of analysis in an operational environment, but also training others to do it. So the guardrails, the heuristics, the tacit knowledge is actively being transferred every

day by those experts to Iris. I'm saying that conceptually, I'm kind of hand waving, but in terms of the prompting, the directions that she's given, the guidelines that she's supposed to operate within, that's all crafted by SME's. And you're one of those SME's. You have two decades in Intelligence Plus, and you've taught this tool, and you and your team have taught this tool using courses, how to act as an analyst in guiding the end user through the analysis process to be a crutch for them to lean on or a trusted advisor in their ear.

That's exactly where we're headed. I wouldn't say she's fully trained yet, but she's pretty capable. Think of her like an eager, recent college undergrad, right? She has a little bit of training under her belt. She's capable. She's got a lot of tools at her disposal, but she's becoming more and more expert every day. And that's partially because she's

being used to support real operational requests every day from end users in the combatant commands. And so every time a new requirement comes up, Iris gets a new tool, and Iris gets new instructions on how to use that tool, and how, most importantly, not to use it. So again, yes, that's coming from experts, instructors, practitioners, all working together with the engineering team and as part of the engineering team to make Iris a reliable and trustworthy

companion. And so we'll bring that back to the end to the trust component. How can end users, how can acquirers trust that these, let's say, what we're selling here right now, and use lowercase selling, is we're selling a quicker path from data to decision and much more rapid decision making in real time where you have real high stakes for the end user. Steve, how do we build trust in this approach? And then back to Jackie for closing. Yeah. And I think that's what the Sphere team has evolved to,

is working across industry, working with data providers, commercial service providers, the government, FFRDCs, UARCs as well, to help do additional data curation, you know, and I'll back up, during the prototyping to help identify what's the scope? What might this tool, this approach or technology, where might it have the most value? What is it, you know, is there one thing that it does really well, or maybe a few things? Where might it need more work, and what areas can it just not do?

Identify that early on so that we can find that best path, and then later in the process, once it's transitioning and transitioned, providing independent data curation. And it's a mixed bag of data curation from the raw data that's fed into these tools to maybe it's processed data that's coming out of the tools. But helping to provide some independent data curation for further enhancement, future enhancement, helping to provide some independent data curation for further enhancement, future enhancement, looking for

data corruption, whether intentional or unintentional. And then the second function that our team does is mission assurance. Again, having some other domain experts, data science experts, to join the fund. You know, we're not there to look for problems. We're there to ensure success, you know, across the board. Successful adoption, successful continued development and enhancement, you know, to

further refine and improve. So I think, you know, as far as that trust piece, our role is to help facilitate that trust, not alone, but with that broader group. This is such a hard question, Parker. I think that because it depends on the end user, it depends on the acquirer's context. I personally believe that the best way to gain trust and when we've seen it grow almost instantaneous, go from skepticism to eager interest, is when an end user sees Iris, in our case, achieve a result or find a thing that they knew was relevant, but there was no way for us to know or Iris to know a priori was relevant. So results,

I think, you know, reps, you know, using it to maybe try to replicate or benchmark against known knowns, Steve, to your point. And at least then they know and they have some confidence that those that can be covered down on. And maybe that's all they feel comfortable using it for. And that might be enough to get a lot of work off the plate so that the experts can work on the harder things, which are those unknown unknowns. And so I really think it is benchmarking, comparison, and the opportunity to take it for a test drive that really drives that trust.

And just an extension of what you said, another role, we facilitate live testing. Some of the examples are turning on cooperative emitters and having a truth source emit and working with the data provider, working with the tool developer to understand any adjustments that may need made and the performance of it as well. And when I say live, I mean real world live, unscripted, but sometimes with some extra knowledge. That's the timeline we're working on here. That's the timeline we are using artificial intelligence and human machine teaming from an operational standpoint. It is right now. It is two seconds ago. So just a fun, maybe not so fun question

to close on, are your tools and this technology going to put smart critical thinkers like me and other analysts out of business? And what's the timeframe on that? Oh man, I don't think so. I firmly believe we are entering into the golden age of the critical thinker. We're going to be super empowered in a way that has never been the case in the whole history of humanity. I think that I've asked new types of questions that I didn't even realize were, I would just discount them or completely avoid them, try to work my way around them

because it would require too much processing time or too much manual effort. So I don't think so. I just think that as an expert critical thinker, Parker, you're going to have the opportunity to pursue and conceive of new questions and develop new answers. And that's really exciting to me. We can't beat that ending, so we'll leave it there.

Jackie Barbieri, Founders, CEO of White Space. Thank you so much for coming on the program. Dr. Steve Lewis, Director of the Aerospace Corporation's Spear Team. Thank you for coming to talk to us. Thank you to the T-Minus and NTK team for having us. Thank you for joining us here in the Nexus. Until next time. That's T-Minus Deep Space brought to you by N2K CyberWire. We'd love to know what you think of this podcast.

Your feedback ensures we deliver the insights that keep you a step ahead in the rapidly changing space industry. If you like the show, please share a rating and review in your podcast app. You could also fill out the survey in the show notes or even send an email to space at n2k.com. We're privileged that n2k CyberWire is part of the daily routine of the most influential leaders and operators in the public and private sector, from the Fortune 500 to many of the world's preeminent intelligence and law enforcement agencies. N2K helps space and cybersecurity professionals grow, learn, and stay informed.

As the nexus for discovery and connection, we bring you the people, technology, and ideas shaping the future of secure innovation. Learn how at n2k.com. Our senior producer is Alice Carruth. Our producer is Liz Stokes. We're mixed by Elliot Peltzman and Trey Hester with original music by Elliot Peltzman. Our executive producer is Jennifer Iben. Peter Kielpe is our publisher. And I'm your host, Maria Varmasis. Thanks for listening. We'll see you next time. Hey everybody, Dave here. I've talked about DeleteMe before, and I'm still using it because it still works.

It's been a few months now and I'm just as impressed today as I was when I signed up. DeleteMe keeps finding and removing my personal information from data broker sites, and they keep me updated with detailed reports so I know exactly what's been taken down. I'm genuinely relieved knowing my privacy isn't something I have to worry about every day. The Delete Me team handles everything. It's the set it and forget it piece of mind.

And it's not just for individuals. Delete Me also offers solutions for businesses, helping companies protect their employees' personal information and reduce exposure to social engineering and phishing threats. And right now, our listeners get a special deal, 20% off your DeleteMe plan. Just go to joindeleteeme.com slash n2k and use promo code n2k at checkout. That's joindeleteeme.com slash n2k, code n2k.