Interconnected Intelligence

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This is a podcast episode titled, Interconnected Intelligence. The summary for this episode is: <p>In this episode of the World of Intelligence, Harry and Sean discuss the evolving landscape of OSINT using interconnected and integrated intelligence.</p><p>Janes, the trusted global agency for open-source defence intelligence is proud to introduce Janes Capella, connecting millions of assured data points across Janes foundational intelligence, bringing the ability to integrate and contextualise multiple sources at high speed to deliver the single source of truth.</p><p>Optimise your mission with Janes Capella: <a href="" rel="noopener noreferrer" target="_blank"></a></p>
Multi-source intelligence and interconnected intelligence
00:57 MIN
Benefits of integrated intelligence
02:18 MIN
The complexity of the current threat landscape
02:01 MIN

Speaker 1: Janes Capella interconnects millions of assured data points across Janes' foundational intelligence, with the ability to integrate and contextualize multiple sources delivering the single source of truth. Janes Capella increases certainty and accelerates decision- making for everyone in your organization. Find out more at janes. com/ capella.

Harry Kempsey: Hello Sean, thanks for joining me, Harry Kemsley, I'm the president of the Government& National Security sector of Janes. Thanks for joining. Sean, a few words about yourself?

Sean Corbett: Good afternoon Harry, great to be here again. Yeah. Sean Corbett, ex- Royal Air Force, senior Royal Air Force intelligence officer and co- chair of the Strategic Advisory Group for Janes.

Harry Kempsey: Sure. So all this podcast Sean I thought we would focus in on a development that's actually occurred within Janes that we're now starting to talk to a lot of our customers about, which I think is relevant to anybody in the intelligence business, whether that's in national security or indeed elsewhere. And that is the ability to interconnect various elements of intelligence, but also possibly to integrate various types of intelligence known as Janes Capella. Now, before I go much on about Janes and Janes Capella, which we can do perhaps later in this podcast, I just want to spend a few moments from your perspective, Sean, about the need for an ability to interconnect and integrate intelligence. You and I have both been involved in intelligence to know that there is never a shortage of sources. Some of them are useful. Some of them less useful. There are always questions to be answered and we seek to give the best answer available with time to the questions we get asked, but the ability to interconnect and integrate intelligence. Why is that so important? What's all that about?

Sean Corbett: Yeah, very good question, Harry, and first I do have to congratulate the whole company on its Capella initiative because all in all intelligence should be interconnected, but it isn't. So we're going absolutely in the right direction here. And without turning this into yet another staff college discussion, it goes back to the heart of what intelligence is, which means different things to different people, but it really does matter and it really is important. So for me, and there will be an infinite number of definitions if you like, it's all about the assessing, analysis and assimilation of every available piece of information to try and answer a specific question or problem set. So it's getting all that together in a particular direction. And that information again, by definition, otherwise it would be just information, is going to be incomplete. It's going to be potentially from a variety of sources and in a variety of formats. Now in the past, you have heard people talking about all- source intelligence. I don't think in my entire career I saw a single piece of all- source intelligence analysis. I definitely saw multi- source intelligence analysis, but we need to get to the place now where we are literally using every available source. And that's the sort of thing that Capella does.

Harry Kempsey: So if we're trying to get to the multi- source intelligence that you talked about, I recall sitting in front of multiple screens, many of which had different source intelligence on it, but I don't remember a time when those different intelligences were connected in a way that made it easy for me to assimilate all the analysts I was working with to assimilate quickly. And it relied a great deal on the gray matter between the ears of the analysts, to remember what they'd just read and then recognize the relevance of what they've just read to potentially other things they'd read elsewhere and thereby get to a place where they gave a more complete and accurate answer than they might otherwise do. And for me, from what you've said, that is the point of interconnecting intelligence and doing it in an intelligible way so that the viewer, the analysts can actually understand the relevance of this now more complete and interconnected intelligence and use it more accurately than they otherwise would. Is that a fair summary, Sean?

Sean Corbett: Yeah, it is. There are two very distinct themes to this. There are making sure that you're able to assimilate, as I said before, and use all available sources. And if you think just in the open- source domain, there are something like 5 billion people online, 4 billion social media users, and they're creating some ridiculous amount of 1200 petabytes of information at any one time. There is not a human being on the planet that could assimilate that even if they could actually bring them in together into one place. And of course, open- source intelligence is not just about going online and seeing what people are saying. It can be a variety of sources... Well, multiple sources in everything from written briefings, academia, economic statistics, you name it. And you've got to be able to take all those in. And that's putting to one side the conscious and unconscious bias of the analyst, which does exist, which we might come back to. So, it's the completeness of the data, but it's also the efficiency in bringing it together. I have been that analyst where I've had probably eight spreadsheets open and a couple of windows there with other stuff that I'm trying to get the relevant bit in one place by literally cutting and pasting and going to here where there's a paper I read earlier. And just being able to do that, even the best and most efficient analyst in the world, who's better at IT management than I am, will struggle. So A: they'll miss stuff and B: it's a truism. I know it's a mantra, but it's a truism that your analyst spends 80% of their time managing and playing with data and 20% or even less, in many cases, actually writing the analysis, the assessment, the so what.

Harry Kempsey: Yeah, I think that's probably one of the least well- understood parts of the open- source environment. It is recognized that the volume, variety and the veracity of open sources are all challenges that we have to overcome. But the flip side of that is it's a fantastic resource. If you know how to deal with the volume, the variety and the veracity issues, you can really get underneath the covers of open source. You can see it for the potential that it really represents, but doing that well is the challenge. And I think that's one of the things that I've been most pleased about in my time at Janes, is watching a trade craft, which is known as triple- lock. We can talk about what that means later on, implemented consistently well over a period of time, but now being amplified and enhanced by using technology that's now at last starting to produce the kind of results that it had often promised and failed to deliver. I know that from my own experience of open- source, it's full of all the pitfalls you'd expect it to in terms of accuracy, but the potential that's within it, if you can overcome some of those inaccuracies and you do the right thing in terms of trade craft can make it a fantastic resource.

Sean Corbett: That's another vitally important point. Everybody in the past has sort of thought of open- source intelligence as the secondary. But if you think about it, it has potentially less speed bumps, less hurdles in the way, then the classified stuff. You don't have the cultures of all the different collection agencies who, you know, knowledge is power and, and who for very good reasons protect, their data, protect their information, the IT systems that are designed to prevent those disparate sources of information from getting together. Open- source does quite the opposite. It facilitates bringing together. It doesn't have those inaudible pipes, those policy challenges that we all know about, and it really does facilitate bringing it all together. So, and you've heard me say this before, and I apologize for boring everybody, but, whereas 80% to 90% of all intelligence products are based on classified intelligence with maybe a sprinkling of stuff that's missing. We're now seeing that starting to flip. And it won't be that long before the open- source is 80% and the classified stuff is validation and in the very sensitive areas.

Harry Kempsey: Yeah, I do like the idea that the open- source reveals the emerging threat probably before the exquisite nature of some intelligence capabilities, even staff, to look for that problem. I do remember from the horrors of the Boston Marathon a story being told about where the understanding of what happened was in the immediate seconds after the event and the horrors that fell upon them. And then in the minutes and hours after it, where was the knowledge, where was the understanding? And of course the images of all the mobile phones raised in the air, taking photographs in the seconds after the blast tells you where the knowledge was at that moment, it wasn't in the secure environment. It wasn't in a classified environment. It was in and amongst the social media platforms, various that were now being pumped of imagery as horrible as it would have been to explain what had just happened... Or not explained, to describe, to show what had just happened.

Sean Corbett: And I have another really good example of that. The invasion, for want of a better phrase, of ISIL into Mosul, I was the United Kingdom's J2 at the time, obviously feeding the operational commanders. And while we knew something was going on, of course, we didn't know exactly what was going on. The first time I had any inkling that they had actually invaded Mosul and were there to stay was when a dear friend academic of mine phoned me up and said," Surely you're seeing what's happening in Mosul?" I said," Well, we're just kind of monitoring." He says" No, no, no, they're in Mosul and they're here to stay." And I said," With all due respect, how do you know?" And he said," Because I'm there." Now that was directly, and it was probably 12 hours plus before we had to do all the collection, the validation, et cetera, et cetera, that actually confirmed that from intelligence sources.

Harry Kempsey: And that's one of the areas that I've seen with the discussions around Janes Capella, the ability to interconnect and then integrate intelligence becoming quite powerful. So a couple of examples we've seen in recent times of a customer, who's looking at open- source unverified data. This is not something that they can say is assured, it's not necessarily the ground truth that they want, but the ability to then take those reports and interconnect them with Janes' content to enrich them. It does two things. One, it gives them more complete answers than they would've had otherwise from the snippet of information of an event. Then it also, to some extent anyway, helps with the immediate verification or otherwise of it. It is saying that a particular weapon system is being seen in a location that it's been seen there before, as shown by a Janes Capella ORBAT reference, for example, would give it more veracity than less. So that ability to enrich and verify is one of those things that I've seen quite frequently being used now around the Janes content and particularly with Capella and its ability to integrate and interconnect. So why now, what in your opinion, Sean... You've seen a bit about Janes Capella, you've seen a bit about the technology that's in it. What is it that you think that makes it so relevant to now? What's going on today in the contemporary environments that we're both familiar with that makes this so important at the moment?

Sean Corbett: I think just looking at the sheer breadth and depth of threats that are based now, many of which are not traditional, but you've got everything that's coming in together and it's probably the most complex time since the Second World War. So you've got the rise of great power competition. Again, China, that's nothing new, but they're actually getting to a stage now where we really need to pay attention. You've got a resurgent Russia that wants to play its part on the global stage again. You've got the whole, and we've spoken about this before, the whole implications of COVID. I was reading something the other day about the ungoverned spaces in Northwest Africa really fermenting that area, that now could bring the violent extremists. And they always thrive on those ungoverned spaces particularly while we are distracted. There's not the economy necessarily to be able to look at that, but the economy is really important. And one thing that's really important to stay, I think about connecting it all up is that it's not just about defense and the military or even security. It's about the entire whole- of- government approaches, which ours and many governments are trying to do. So you've got to bring together the defense side, the diplomacy, the economic, and really what we're talking about with Capella is taking all that disparate data into one place where you can actually say where the so what is, so it doesn't really matter what that data is or what the problem set is. It's doing that process. What I would add to that is that the reason that now is so good for Janes is that the provenance and the richness of the data that you already have that has been verified and all the rest of it, is that foundational level intelligence, which to be fair the intelligence community has struggled to keep up with because it's been looking at all the emerging crisis. So it's true what they say, garbage in, garbage out. But the fact that you've got that foundational data there, it's bringing it together with some of the new stuff and some of the sort of alerts to come up with that so what.

Harry Kempsey: Yeah, I think there's another side to that, which I've certainly been very close to inside Janes for the last period of years. We have been inundated with, as I'm sure many of the listeners to this podcast will have been, inundated with the promises of technology, such as machine learning algorithms driving artificial intelligence, for example, NLP and all these other amazing technologies that they would answer all our prayers. They would be the panacea to everything we've ever wanted to do. However, I think the number got above 50 different companies, all specialist AI, defense related companies who presented to Janes the panacea of all our concerns. And actually what we found is that the technology, as powerful as it is, takes an inordinate amount of time to be trained, to become useful. And it was only when we began to realize what we had done in defining and connecting all the dots within the Janes existing content that we began to realize the power of having a stable and standardized data model such as Janes has to actually train these algorithms. And so that we can in the end, decided to do it for ourselves, that ability to amplify what Janes has done and thereby amplify the trade craft of the analyst is probably for me, one of the greatest things that Janes has achieved in recent times, it defined the future of Janes because we are able to do things that eventually many AI technologies would be able to do, but they wouldn't have the headstart, the very significant decades of headstart that Janes has got, but equally they wouldn't have the provenance. And many of our customers have found that on inspection of Capella, even where they have their own ability to do some of the things that Janes has done for itself, to use a quote that a customer used in recent times, they're doing the work that Janes has done. They're trying to classify various types of intelligence, equipment intelligence. They're trying to understand the systems, the subsystems. They're trying to give definitions to those things. They can reliably detect them in text and other media, but they're running into all the headwinds that Janes overcame some years ago. And then on inspection of what it is that Janes has achieved, the phrase," If we use Janes we'll be standing on the shoulders of giants," meaning Janes is already cracked a lot of that code. We can move that customer forward and we're doing so regularly now with some of these customers around Janes Capella. In essence, what I'm coming to is having the data, using the data and all the work we've done on that data to really understand it in a data model way has enabled us to really, really powerfully use the technology. And it's that synergy the data, the depth and breadth of Janes, the provenance of Janes' data with the technology that's making Janes Capella so exciting for Janes and why it is the next generation of Janes. I mean, we've been around 123 years. I think this capability, Janes Capella, is the definition of why we'll be around for a good number of years yet, because we have the data, as well as the understanding of that data. We also now have the technology and how to use all three. For me, that's been the pivot point for the last few years and the success Janes has brought to it. So for me, Sean, in short, the reason I'm so excited about Janes Capella is the fact that we've managed to crack the code, not just on having the data, making it assure the data, but then knowing how to use that to train your technology to then support with positive feedback, the analyst.

Sean Corbett: Yeah, absolutely. And just to reiterate that, it made me smile what you're saying about AI. AI is not a means to an end. So I know most companies bid for good reasons, but not all good reasons. You know," If you don't have artificial intelligence on it, then clearly you're not on the right sheet," but you've got to apply the artificial intelligence to something. So there's two elements of it. You've got to have the data and it's got to be good and it's got to be able to be interrogated if you like, and algorithms brought against it. So the algorithms have to be good. And I think you're very lucky with some of the very, very good developers, coders, et cetera, that you've got, but it's only as good as the data. And that's really where I come in because at the end of the day, we are trying to make really high- quality, high- confidence, high- veracity analysis, or at least enable them, that you can only do with that data delivered in a certain way.

Harry Kempsey: Yeah. So when anybody listening to this is curious about Capella there are probably two or three things that we want them to leave this conversation understanding, but by the way, if any of them want to see more about it, then go to Janes. com, we'll try and put a link on the podcast for you to take an easy route to that, go to Janes. com, follow the Capella route, and you'll find plenty of examples there of how Capella is helping other customers and what it is able to do. But I'll give my answer after yours. I'll let you go first Sean. What are the two or three things you'd want anybody to know about Janes Capella, what we've done with connected intelligence that you'd like to leave them with as they leave this podcast?

Sean Corbett: So firstly I'd say it's not a product in itself. It's a way of doing business that allows the analyst to be much more efficient and to make sure that they have all the data available to them and it's presented in a way that you can do that so what. So for those analysts out there, don't think that this is replacing the analyst. It's actually enabling you. And that really is probably three points in one there actually. And the other thing I would say is in terms of maintaining relevance, the focus still has to be on the quality, the assurance of that data that Janes does so well and add to it. And one of the, and I know you're going to come on and talk about that as well, and one of the real advantages about Capella it doesn't just use Janes' data. It can use any data, which is probably the real headline banner that makes this different from everything else.

Harry Kempsey: Yeah. I would agree with that. I distinctly remember watching some analysts trying to fill out an intelligence analysis, [ASCOPE/PMESII 00:19:22], an acronym that basically talks about a series of things they want to find out about a place before they go there. And they spend a lot of time trying to fill it. And then they went to the Janes data products and they managed to find all the answers they needed to fill all fields in this table they'd created in the ASCOPE/ PMESII analysis they were doing. What Capella is doing is giving you access to that content even more quickly, an even more complete answer than before, thereby allowing analysts to spend a lot more time doing the value- add. That's the first point for me, it's an even faster route to value- add analysis time than you have before. The second for me is the point you just made about the ability to interconnect and integrate third party content, your own content, as well as the Janes content. But even if you don't need more time, even if you're working in a non- time sensitive environment and you are not pressed for time at all, you've got all the analyst time you need, and you seldom need open- source information because you have everything you need to do your analysis in your specialist area, you're a signals intelligence analyst and you only ever look at signals intelligence. The thing that Capella is doing for many people, even in that environment, is opening their eyes to things that they just haven't realized they didn't know. It's giving them access to things, making things visible to them in a way that they just hadn't realized they didn't know that: the unknown unknown, to coin an overused phrase. Or even the unknown known. The fact that in my information system, there is an answer to a question that I haven't even bothered to ask, didn't know to ask or that I'm asking and they didn't know had been asked before and answered. That is what machines do better than humans, because we don't have an infinite capacity of memory. We certainly don't have an infinite capacity for recall. And what Janes Capella is doing is now enabling access to that knowledge pool, that knowledge base, call it what you like. It's about giving you access in a meaningful way. And as you say, it's not a tool set. It's not a platform. It's not software per se, as in a product, it's a capability. And if I left the audience with anything from this conversation, interconnecting intelligence is what we've been wanting to be able to do for a long time, integrating intelligence into a single understood picture, something that we've aspired to do and throw in a lot of money, time and effort at for many years and not achieved it. Capella has taken a huge leap forward in both those fronts. But for me, the most important thing that Capella's done this amongst all the things I've already talked about is that ability to see what I didn't know that we knew or indeed to give me a reason to ask a question I hadn't even thought to ask, because I didn't think about the problem in the way Capella has now revealed to me by giving me insight to things that I otherwise wouldn't have spotted. So I'm conscious that time will evaporate on us very shortly, Sean. Thank you for your time on this podcast. I think when we come back to this conversation next time, we'll bring a couple of other people into the podcast and we'll start exploring this world of intelligence that's interconnected and integrated around some use cases, because I think there's a danger here that this conversation can become quite theoretical and quite clinical without being necessarily very real. So we'll bring some people in on the future podcasts to try to bring this stuff to life for them to see what they can see with Capella and understand how it's used.

Sean Corbett: That sounds really good actually, because as you say, we could get too theoretical here and it'd be nice to dive into some real live issues. And I guess if anybody out there wants us to cover anything, give us a challenge, as long as it's not too obscure. I think we're pretty up to speed on a lot of things go in the world. And of course, as you said, we've got lots of contacts there in the various different regions, so that'd be quite a good thing to do.

Harry Kempsey: And we'll do that for sure. Well Sean thank you again for your time. Next time it wouldn't be just two of us. There'll be three of us at least I'll make sure we get a second guest and we can both ask them a difficult question to see what they come up with. Sean, thanks for your time.

Sean Corbett: Thank you.

Speaker 1: Janes Capella interconnects millions of assured data points across Janes' foundational intelligence with the ability to integrate and contextualize multiple sources delivering the single source of truth. Janes Capella increases certainty and accelerates decision- making for everyone in your organization. Find out more at janes. com/ capella.


In this episode of the World of Intelligence, Harry and Sean discuss the evolving landscape of OSINT using interconnected and integrated intelligence.

Janes, the trusted global agency for open-source defence intelligence is proud to introduce Janes Capella, connecting millions of assured data points across Janes foundational intelligence, bringing the ability to integrate and contextualise multiple sources at high speed to deliver the single source of truth.

Optimise your mission with Janes Capella:

Today's Host

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Harry Kemsley

|President of Government & National Security, Janes

Today's Guests

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Harry Kemsley OBE

|President Government and National Security, Janes
Guest Thumbnail

AVM (ret’d) Sean Corbett CB MBE MA RAF

|Strategic Advisor – US Intel/DoD, UK Govt, NATO (Structure Data)