Think Like a Spy To Out Think Your Competition: UK Intelligence Expert Sir David Omand

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Hello, I'm Bob Gourley, the Chief Technology Officer of OODA LLC. And today on the OODACast Professor Sir David Oman, one of the most respected intelligence professionals in the world and author of the book How Spies 10 Lessons in Intelligence. David, I really appreciate your background and have it hold it in the highest regard and I wanted to give folks a little overview of what I know of your bio. You were the first UK Security and Intelligence Coordinator. So this is coordination of all UK intelligence operations and analysis and insights responsible to the Prime Minister for the overall intelligence community. Now, prior to that you also led Homeland Security and you were at one time the head of gchq. And that's after a long career in the Ministry of Defense. Did I get that arc about right?

That's about it. I started my career back in 1969, which seems a very long time ago, during the, during the Cold War, by joining GCHQ as a youngster fresh out of college. It was a fascinating place to work. But then I transferred to the Ministry of Defence and spent a lot of time in the defence business, including three years with NATO living in Brussels. But the highlight, if you like, was being the first UK security and intelligence coordinator in our Cabinet Office after 9 11. And you can imagine what was going on in the community on both sides of the Atlantic dealing with the very new demands of gathering operational intelligence on terrorism.

Right. And that threat had been around for a long time and many had been tracking it, but there was really not the established collection networks and processing capabilities that should have been in place at the time. And so there had to be a lot of innovation. I know that. So it must have been a very dynamic time.

Yes, I mean there was a dynamic interaction between supply and demand. On the demand side, we had the need, very urgent need for operational intelligence on the terrorist networks around the world and also including in the domestic space within the uk, but also demands for intelligence on proliferators, on serious criminals. And those demands were interacting with the supply, dramatic changes in the supply situation as a result of the development of the integration Internet and digital intelligence. And, you know, we're still experiencing that interaction between the new kind of demands, which on terrorism was certainly about people. You know, we still need intelligence on the radar, cross section of the backfire, bomber replacement and all of that, the traditional stuff, but we also need intelligence on people who, individuals who mean us harm and whether they're dictators and autocrats or whether they're terrorists or serious criminals. And if you want information about an individual these days, where do you look? You look into the digital world because we are all leaving traces in the digital world of our everyday doings. So that dynamic interaction has really reshaped intelligence on both sides of the Atlantic.

Well, let me ask another question. I want to go back to gchq. When I hear those four letters, being in my community, I immediately get this sense of two things, incredible history and incredible competence. But I'm not really sure GCHQ is a household term. Can you tell us what is GCHQ and what should people know about it?

Well, GCHQ is the government communications headquarters. It's today the Digital Intelligence Organization of the United Kingdom and the Cybersecurity organization of the United Kingdom. So we have a national Cyber Security center that is an integral part of gchq. If you like poachers turned gamekeepers, a good principle. But gchq, the term, the name was changed to GCHQ in the 1950s, early 1950s, better known perhaps from the work of Hollywood as Bletchley Park. The government code and cipher school at Bletchley park, of Second World War renown and its origins in turn go back to the First World War and the cryptographic effort that supported our forces in the First World War. If you were to ask me where was the first ever signals intelligence station, the answer was 1914 and Scarborough, which is a town on the northeast coast of the United Kingdom. And it was set up in a great rush start of the Second World War, the First World War, to monitor the position of the German battle fleet, Grand Fleet, because they had installed Mr. Marconi's new invention, radio. And even as they were installing it, the British Admiralty was getting to think we could exploit this for intelligence purposes. And it's been the same story all through the intervening hundred plus years that every innovation in technology has found its intelligence exploitation.

Yeah, I think this is key and it fits in with what you write about in your book, which I want to talk about the need for situational awareness. And for me as an intelligence professional, I was always aware of that rich history and it made it real for me when I got to visit Bletchley park today, which has really been redone and it's a beautiful place and I could walk around in these huts and look at how they were set up in the 40s and, and see the intelligence center of today. It's so similar, except today we have more monitor. We have monitors and electricity and digital connectivity, but it's really the same kind of function and those routes are so important. And I wanted to mention one other thing. Right outside the gate at Bletchley park is the National Museum of Computer History. Because really the history of computers all also started at Bletchley Park. This whole digital world we're living in had its roots right there as well.

And you know, huge contribution to that made by individuals like Alan Turing at Bletchley Park. A genius who then went on after the war to try and develop the ideas which had been put into operation to help the decipher German high grade German ciphers. But the very principles could then be applied. So you had computers that could be, were programmable. You know, a hugely important step in understanding that you can have a machine not just that has a single purpose, but that is reprogrammable to different purposes. All of those innovations stemmed from those few years when the survival of the United Kingdom was in the balance. The partnership with the United States, which was formed when the United States entered the Second World War and working together on these extraordinary, extraordinary innovations. So it's a great history. And one of the things I like to remember is that when I was interviewed for GCHQ in 1969, one of the members of my interview board was a man called Hugh Alexander, who by then was the chief cryptanalyst, but who was a colleague of Turing's in the famous huts in Bletchley park working on the Enigma, German Enigma machine. So that kind of continuity ran through the organization. Fascinating.

And I just, I know that has to be part of the culture today as well. It's just such an important part of history. And I've seen similar kind of attitudes and approaches and respect for history among our analysts at nsa, for example, this being able to connect with your history is so important. I have some other questions I want to ask you about history, but it may actually flow better in the discussion of your book because I'm going to want to talk about some examples of how do you vet sources and how do you trust data and how do you come up with the most accurate as possible assessment and present it well. So I want to move to the book. Well, actually before going to the book, I want to try a thesis out on you and see if this is part of the reason that you wrote this book. In this world of digital connectivity, every citizen in the western world is connected and has access to this vast information store of the Internet. They're also all targetable. That's how the Internet works, it targets ads at you and you can be targeted for manipulation. We've all seen that, for misinformation and disinformation. And the world is so full of this propaganda and, and it's really so dangerous. My thesis is this. Every citizen now needs to be their own intelligence analyst. If every citizen cannot be their own intelligence analyst, they're going to be susceptible to this just overwhelming amount of rubbish that influences them in all of their decisions. So that's the thesis. Every citizen has to be an intelligence analyst. Any reaction to that?

Well, that's very much the theme of the book. I started writing the book as I began to get very angry at how, for example, the 2016 US presidential election was being reflected in social media. There was a rising tide of half truths and distortions designed to try and manipulate us emotionally online into what we were supposed to think and, and want. Not to mention some falsehoods, some deceptions, some coming from Russia we now know, but not just from Russia aimed at widening divisions in our societies and basically trying to set us at each other's throats. So in this social media world we have to accept that respecting the value of truth itself no longer seems as important as, as saying something, even if it's not strictly true. That creates an emotional impact. So that's where the analysis comes in. That's where we've all got to learn to be able to separate these two kinds of thinking, the emotional and the rational. Now you need both. Where would we be if we didn't have emotional ambit, if we didn't have ambitions, if we didn't desire things and want things and want things to be better? But that's the emotional side. The rational side is saying to us, well, there are limits that analysis can show us on what the world can actually be like. What we tend to get is this what the Rand Corporation have called the spread of truth decay. Yeah, you have an induced feeling. I would like that to be true and if you hear it often enough, well, it might be true. And that slides too easily into. Well, for me it's as good as true and I'm going to act as if it's true and the next thing you know you've got the storming of the capitol building on the 6th of January. Yeah. So the book is an appeal for rational analysis and having spent my whole career in the service of UK government, either generating or using intelligence as you do, Bob, you know how the analysts think and you know that they're really doing their best to arrive at the best possible explanation consistent with the evidence and to avoid all the traps we can fall into as ordinary human beings, we're all susceptible to those cognitive traps. Basically, it's much easier to agree with stuff that deep down we want to be true rather than the stuff that we know isn't actually necessarily the best. So that emotional values driven, this is what I really want and what it must be, and the dispassionate what is, and the practical limits of what can be. We need both, and they have to be integrated if you're going to get to a sound decision. The mistake is just to be swept along by the emotional side and then you wake up to discover the world. Well, it's not quite the way that you rather hoped it would be.

Yeah. So I want to get more into this, the book and the model you present in the book, the SEAS model. Because in reading the book, I see it's immediately applicable to people who want to be rational, analytical thinkers and to companies, businesses that want to improve their intelligence process. I hope it is also good for the individuals who just want to improve their personal decision making. But that's where it gets tough because, you know, how do we get this kind of word to millions and millions of people who may not want to hear it? But to begin with, I think we. I want to ask you about this SEES model that you discuss. I think SEES is a great acronym. And can you walk us through that?

Certainly, certainly. SEES is S E E S. And that's what the good analysts do. They're seeing. So the first S in SEES is situational awareness. And that's accessing data about what is happening on the ground or in cyberspace. We all have factual questions that start with what, when and where. And before we start arguing about what to do about some situation, let's nail down some reliable situational awareness. Now, this is where the intelligence analysts can say, yeah, but be careful. Your choice of where to look for evidence can distort the picture. You see, this is what happens with the Internet filter bubbles, where we're only accessing information which has already been filtered to suit our worldview. This is where you can fall victim to rumors, for example, on the Internet, sometimes deliberate deception. So my first lesson in intelligence. My book is called housewives think colon 10 lessons in intelligence. Don't worry, I'm not going to go through all 10. But the first is we need the humility to recognize that our knowledge of the world is always fragmentary, it's incomplete, and it is sometimes wrong. Right. So do the best you can be very careful that you're not being swept along by fake news or deception or rumor. But just be conscious that this is not a, you know, this is an art, not a science, you know, and.

You also build a point in the book that be careful, do not buy in 100% and lock yourself into what the intel analyst told you yesterday, because they may come with a change.

Absolutely. And a second lesson in intelligence is that facts, which we've established, you know, we've got our situation awareness by themselves, they're done. Even with the best artificial intelligence, the best data, science, data needs explaining. Let me give you a simple example. The, the defense lawyer you have, the young accused, his fingerprints were found on the fragments of a bottle that was thrown at a police patrol. So he's in court and the prosecution say, well, we have the fingerprint evidence, it's incontrovertible, it shows he's guilty. And the defence lawyer stands up and says, no, no, this bottle was seized from outside the accused house. It was out there in a box waiting for recycling. The mob charge passed, they picked up the bottle. It's, it's not. His fingerprints are of course on the bottle, but the reason is innocent. And the same forensic fact, the fingerprint has two different explanations. That's what lawyers do. I mean, what is the best explanation consistent with the available facts? During the Second World War, British intelligence found that these lawyers, the high grade barristers, were the best people to put in charge of crafting deceptions to fool the German High Command.

Brilliant.

They were experts at presenting a case consistent with all the information that's available to the Germans in that case, but carefully marshalled to lead them away from the truth and towards the desired deceptive conclusion. So the second output of the SEAS model, the first E in seas, is explanation. Why, how, why do we think that Russia was responsible for the Notpetya cyberattack? How did it end up costing global business over $10 billion and nearly destroying Maersk, the world's largest shipping company? So you need to explain what has happened in that case. The most probable explanation is, yes, it was indeed the Russians and they launched this attack through some tax software, aiming at some Ukrainian companies. It escaped into the wild, it did all this damage, but they didn't intend to do that damage. Now, if the explanation our analysts had come up with had said, yeah, this was a deliberate sabotage attempt on global capitalism from Russia, you would have had a very different response to Russia. So in the world of international affairs, getting the explanation right is really really important. And what you've got to do is you test your alternative hypotheses against the data. And you look, this is the point that some people kind of hesitate over. You look for the explanation with the least evidence against it, not necessarily the one with most in its favor. Because if you look hard enough, you can always find some evidence that appears to support an argument, however crazy. That's what QAnon does. But it should only take one solid piece of contrary evidence to show anyone whose mind is open that an explanation is, is inadequate. Sadly, in 2003, in the run up to the invasion of Iraq, we kind of looked too hard for evidence that appeared to support what we thought was the case and which turned out not to be. You know, so we. The infamous stockpiles of weapons of mass destruction. But you can always find stuff, evidence if you look hard enough. The key is to look all around for the information.

Right. And you know, this, I just wanted to say this model is so applicable at those big, big strategic issues, and I think of so many where it is, but you just remind me that it's also applicable to our daily life. Because this is how the fraudster steals the money of your, your elder relatives as well. They tell them things they want to believe and get involved in a conversation. And the person wants to believe, if I do this, I will find love or I will get riches and just do this. And they get pulled in through the same kind of, I don't know, cognitive traps or biases.

Yeah. And you know, any of the TV shows will give you examples where, you know, the partner comes home very late, says they've been working late in the office. Very quickly you can get a rumor, you can misunderstand. So nailing down explanation is really hard, but it's important. And therefore the third, the second E in Cs is about estimation. So if you've got some decent facts that you've managed to get data that you've got on the situation, and if you've got a really good explanation you think stacks up, you can use that to try and estimate how events might work out, particularly in the shorter term. So this is a kind of modeling. It's what the scientists are doing with COVID 19 every day as we speak. They have data, they have models of transmission and how the disease operates, and they're providing governments with estimates of, on the basis of these assumptions, this is what we think is probably going to happen over the next month or whatever. And it's what's called in the trade bayesian inference, after the name of the 18th century cleric Tunberry from Tunbridge, Tunbridge Wells in England, Thomas Bayes. And some of your listeners will have heard of Bayes theorem. You work scientifically back from to infer causes from observation. You've got facts on the ground. You can work out from that what is the most likely situation to have caused those facts to be in that place, as it were. And the Reverend Bayes at his first demonstration, there's actually a billiard table. So you have a pattern of balls on the billiard table and what was the starting position? And you can estimate that from your observation. So you use this data about the world to explain the recent past and you try to give particularly governments, but equally companies, estimates of how are things going to unfold. You've opened a new branch in a foreign country, you've got information about your set, what's going to happen to the market over the next month or so. But the important point here is these are probabilistic estimates. They're not predictions. Certainly British intelligence analysts try to avoid using the word prediction. Nobody can tell the future. There are no crystal balls. But within limits you can estimate things, particularly if there's what's known in the trader's path dependency. You've got a sound explanation that's not going to change overnight. But there are again all sorts of problems here, traps you can fall into if you're not careful in trying to look ahead. It's crucially dependent on the assumptions that you make right. And if you get your assumptions slightly wrong. So making the assumptions public and being prepared to argue about them is an important part of that kind of modeling. Just to round it off. The final S in Cs is actually quite different because my experience is that you get really focused on producing these estimates of Cs, telling people how things are likely to unfold, the world is likely to unfold and something completely unexpected comes and hits you in the back of the head. There's another way of giving the decision maker or the businessman or the government information, which is quite useful. And that's the final S, which is strategic notice. And that's working the other way around. That's not starting with today and yesterday and trying to work forward. It's starting with the future and trying to work back to the present. So what are the developments in the future that you can think of? Which were they to come about? They might not. But if it happened, would we discover we're in some kind of difficulties or perhaps there are opportunities? Would we be in a position to seize them. Give you an example. Suppose China is the first to develop a workable quantum computer at scale. As far as I know, nobody's done it. I mean, Google have made some great progress, but they're not really quite there yet. Suppose China found a completely novel way of doing it and they did it. What would they do with it? Would they give it to the Bureau of State Security who then have access to ability to decrypt virtually all of our Internet communications and major financial flows around the world, our defense communications, because they're all based on algorithms which use public key encryption which would be susceptible to that. So that's an example where it may not happen, but you can envisage it might happen. And the message for the policymakers is you better put some money into developing some quantum resistant algorithms so if this happens, we're not caught out. And I'm glad to say that the National Security Agency in the US and GCHQ in the UK are busy working on this as we speak. But it's just an example of, you can think of all sorts of things for the future. Now, unfortunately, pandemics, COVID 19 wasn't really one of those. We had strategic notice, both governments had strategic notice, but probably we didn't do enough to prepare for it. So my fourth lesson in intelligence is if you devote sufficient effort to acquiring strategic notice and you use it, you don't have to be so surprised by surprise itself. You won't be able to predict exactly when it's going to happen. You can't predict when a volcano is going to explode or the San Andreas fault is going to suddenly open up, but you know that it might happen. You can put an estimate on a 1 in 100 year type event and then you can take some sensible precautions. So you put all those together. The first three letters of Cs working from yesterday and today forwards and this final strategic notice working back from the future. And you've given yourself quite a powerful set of tools. After all, if you think about strategic notice, that's why we insure our houses and our cars. I mean it's very unlikely we're going to lose them, going to burst into flames if they did. It's a big loss. So aim off, you can actually find a market, you can buy insurance. You have used that strategic notice of the possible loss to put yourself in a good position and in all sorts of ordinary life, and certainly in business life, buying foreign exchange forward, if you're a big exporter, for example, all sorts of Things you can do to manage those risks.

All right, well, I like the SEAS model a lot because you capture all of these lessons in a very succinct way that is relevant not only to, at the strategic level for governments, but for businesses and for individuals. So Cs. I appreciate that. I want to ask some questions though, a couple other points like data. I get the point that data, this information is really neutral, we have to act on it. But how do you know that the data is accurate no matter what the source? It seems like any source I can think of can have issues. Whether it's, you know, imagery or sigint. If you're a government or the press, the media, social media, every database that you can buy, if you're a company, all these sources have issues. So how do you know that the data you're getting is accurate enough to act upon?

Well, in a sense you don't know that. So the starting point of wisdom is to recognize, don't just blindly trust it. So you've got to sample it, you've got to check it out. If you're taking information from the Internet, you know, there's a be very careful. There are reputable sources and there are less reputable sources. If you're applying artificial intelligence algorithms, which the intelligence community does do, to try and look for patterns in the data, then is the algorithm you're applying unbiased? And this is quite a big problem in data science because these things have to be trained on data. And if all the data comes from us college kids of a certain age, it's not going to be representative of the global population. So don't apply this to the global population. So think, you know, data scientists have to be very careful to try and make sure that they're not starting from a position of bias because the data they've got and then selected by some algorithm is itself biased. The moment you get into actually then starting to explain the data, of course, there's a whole different set of problems. One of the advantages of using the SEAS model is that each of the four letters, each of the four parts of the analytic process is susceptible to slightly different errors. So it makes, you know, problems easier to spot. We all know about groupthink, for example, that's a very well known. But do we think enough about mirror imaging when we do our explanation? You go into the car showroom, you're going to buy a used car. If you think that the guy selling you the car has the same objectives of you have in buying it, you're probably going to get things quite wrong. So you have to understand what is motivating. The guy who's selling the car doesn't mean to say he's acting in bad faith, but from his point of view, there are things that are important. From your point of view as the buyer, there are different things that are important. And it's when you can understand both and you bring it together, then you're going to have a really good conversation. And the same principle certainly applies if you're trying to work out, goodness me, what is President Putin's motivation in launching the digital subversion at the United States at the time of the 2016 and then the 2020 presidential elections. And it's very easy to fall into the trap of imagining he is thinking from the same way as the American or British analyst is thinking. He's coming from a different background, a different training. The environment he's in and the history that he has learned about his country is going to be very different from the history of Russia that you would have learned at an American university. So this sort of mirror imaging. There's another one that's one of my favorites which has a wonderful word called perseveration, which is borrowed from the medical profession. And perseveration is when you cling to old ideas long after they're sell by date. And it's very easy to do. You're comfortable with that explanation. It was true in the past, but you know, events have changed and you just, you haven't quite caught up with the new information. So that kind of open mindedness really important in business life. You know, it was taken for granted that when you opened the subsidiary in Country X that it was a great place to be and the political climate was absolutely right and all the rest of it, it may have changed, but of course the executives who took the decision are still there and they have that perseveration. They're committed to it in the same way as an intelligence analyst that told the President, Mr. President, this is how it is, we think it is may well be slightly reluctant to go back and say, Mr. President, it's not like we said it was last week. It's different now. And of course in public all politicians hate having to admit to changing their minds because they'll be accused of doing u turns lying to the public. But actually the sensible people change their minds when the evidence changes. Right.

You know, this leads into this topic of cognitive bias that you write about in your book. And I think most people in the intelligence community have studied cognitive biases and a lot of business leaders do too. We try to examine it on our channel where we can. It's this human tendency to, well, you make errors and there's a lot of reasons for that. And we can study that and then try to mitigate those biases. And one of which you just mentioned, this anchoring bias, this has been my position and therefore I'm holding onto it, I'm anchored whether it's intentional or mentally or unintentional. But there's so many others. And I wanted to ask your views on the most important biases from your opinion. And how can we as people mitigate our cognitive biases?

Well, let me answer the second part of your question first, which is that most of these cognitive biases are unconscious biases by definition. Therefore you don't know you're being biased. Somebody else that you trust will probably spot, oh yes, Bob here, he's falling into this confirmation bias. He's just taking this as evidence of what he thought all along. He's not looking at it the way he should. So that's, I think analysis is a team sport, is the best way to think of it. So big decisions, whether they're big business decisions or personal decisions, find somebody you really trust and talk it through. And they may well spot that unconsciously, you know, because it suited your prejudices in one way or another or what you previously had believed, you've fallen into one of these traps. So make it a team sport. There are literally dozens of these biases and you can buy textbooks by the psychologists. I think some of the most important are perseveration, which is you need a capacity to change, be open minded. And there's a lot of evidence that, particularly in military circles, when you've taken a decision as a commander, which was very difficult to take, and you agonized over it and you've said, no, we're going to advance on the left. And then the intelligence officer comes in and says, boss, I think the enemy's not where we thought they were. You now should have been advancing on the right. And the instinct is to say, well, I'm not sure about that. Your evidence looks a bit flaky. Go away and do it again. Come back with more evidence when you're a bit more certain. It's called cognitive dissonance. And it's the difficulty of holding in your mind the fact you took this emotionally charged decision on which people's lives will depend and now you're being told, but actually the situation on the ground was different and you probably shouldn't have taken that decision. It takes a lot of open mindedness, a lot of courage. And military officers know, you know, order, counter order disorder. You don't want to be forever changing your mind. But when it really matters, that ability to say, no, we're going to stop here, let's just take, take an hour, let's look at all the evidence again. Is this information now the basis on which we should take our decision? Mirror imaging and transferred judgments. So assuming that somebody else is going to take the same kind of judgment as you would take is a very easy mistake to make in life generally, but certainly in the intelligence world, particularly because you're dealing with people you don't know a lot about. These are terrorist groups, they're criminal groups, they're dictators. You can't really see into their minds. There's a distinction, by the way, in the intelligence world that you will know very well between secrets and mysteries. And secrets are things you can find out. If you've got the trade craft, you've got the capabilities in principle, you can find out secrets very hard to do and you won't know them all. But mysteries are different. Mysteries. The facts don't exist on the ground because it hasn't yet happened. The dictator has not yet made up his mind whether he's going to invade his neighbor. And even if you've got the best placed agent you could possibly have, even they're not going to be able to tell you because the great man hasn't yet made up his mind yet. That's exactly what the President and the National Security Council want to know from the intelligence analyst is what's likely to happen next. So it's very important for anyone taking and in business life to distinguish as an analyst or a planner or strategic planner, what you're telling the boss when it's, we are confident we've got information that bears this out. You know, we call it intelligence, but let's just say information that bears it out. And when you're saying this is our best judgment, but we don't have specific intelligence, we don't have specific information that this takeover bid is going to happen. But based on our experience and what we know so far, we think it's very likely that this bid is going to be made and the two are different. And if you get the two confused, and in 2003 there was the run up to the Iraq war, we didn't make it as a community as clear as we should have done to people like Colin Barr when we were actually Saying, we have hard evidence of this. And when we were saying, no, this is just our extrapolation. This is our best judgment of what might be right.

You know, you bring up several other points. One is, I believe one of your counterparts when you were at GCHQ might have been Mike McConnell. Was he director of NSA when you were there? So one of my mentors, and one thing he drove into people like me was things that his bosses had told him when it comes to presenting intelligence, Colin Powell told Mike McConnell when he was the JCS J.

2.

Tell me what you know, Tell me what you don't know. Tell me what you think, and make sure I know the difference between those three.

Yeah, absolutely. That's it. Absolutely exact. My good friend Greg Treverton, who chaired the U.S. national Intelligence Council for many years, introduced a third category. So you have secrets and your mysteries and you have what calls complexities. And this is the really hard bit. This is when your estimate of what the dictator is going to do next depends on the dictator's assessment of what you might do next. Ah, and the classic case of this was the first Gulf War, where Saddam Hussein had parked his tanks along the border with Kuwait. And we were all agonizing, is he going to cross the border and invade Kuwait? And that's what started the first Gulf War. But what would be the consideration in his mind before he decided to cross the border? It would have been, what would the United States do if I crossed the border? That was an occasion, it is said, when the US Ambassador in Iraq was summoned in to see Saddam and he said, what is your view on my dispute with Kuwait? And the answer came back, as per the State Department brief, we don't take sides in. In inter Arab disputes question, would he then have interpreted that as meaning, the US Is not going to intervene if I settle my quarrel with Kuwait by just taking the place over? Saddam didn't want to pay back the money he'd borrowed from Kuwait to fund his war with Iran. But of course, he got that terribly wrong because he did invade Kuwait. And the United States, the United Kingdom and other allies put together the first Gulf War coalition and drove him out of Kuwait. So he got that really wrong. But that's an example of complexity. So if you're in the US Intelligence community trying to predict will Saddam cross the border, you're actually trying, you have to say, what does he think the United States would do? And you don't really know what your own government would do in those circumstances because it hasn't happened yet. So that's the acme that if you can get those kind of complexities when there's an interaction between the adversary you're examining and your own reactions, that's when it gets really difficult. But get that right, of course. And that's of enormous value to the policymakers.

Right. Well, let me ask you another question related to that. Because at a strategic level, governments and in some big businesses, intelligence is a separate function because you need to be able to create something and speak truth to power and not be influenced by the biases of your customer. And there's a lot of important reasons for that to be that way. There's a lot of weaknesses in that model. And that you don't get the full picture of what your customer is doing and thinking and how they're interacting in the world is one. And at a strategic level, that first Gulf War may have turned out different if intelligence were really embedded with Department of State, for example. In the business world, it's rare to see that because you can't afford a totally separate operation. And I think it's also, maybe it's more productive when intelligence is joined at the hip with the operational decision makers. In many military units, there's a two an intelligence and a three in operations. And the best, those guys are working just right together. Intelligence and operations is blended. You move up to the strategic level and it's so far apart it seems inefficient. And now as a citizen, you are one individual and you have to do all these functions in your brain yourself. Intelligence, analysis, operations, decision making, it's all mixed in together. So that's why I wanted to ask your thoughts. What's the right model when it comes to intelligence and operations? Why is it so separate like this in the countries? And what should a business do? Should a business integrate intelligence and operations?

I think the best answer was given by a former chair of the British Joint Intelligence Committee, Percy Cradock, very experienced diplomat and ambassador in Moscow. And he said the best solution is you have the policy people and the intelligence people in adjoining rooms with thin partition walls like cheap hotels. So it is important to keep the analysts from accusations of being politicized, just giving the answer that they know the policymakers want. And you know, you recall all of us about the Iraq run up to the Iraq war and those accusations. So it would be a mistake to integrate them completely because then you would lose. And there are times when the intelligence analyst, or in business, the business strategist, has got to be able to say quite straightforwardly to the Boss, you know, it's not the way you think it is. We have a different analysis and be taken seriously. So that kind of distance has its value. There's a lovely story that the head of the British Secret Service a few years back known as sea. So the sea of the day went to call on a new British Prime Minister. And the new Prime Minister said, well, great to meet you. Tell me, I've always wondered, what exactly do you do as the chief of the Secret Service? To which the reply came back quick as a flash. Prime Minister, my job is to tell you what you don't want to know. And that sense of you have these people who will say to the boss, however powerful the bosses, no, that's not the conclusion we've come to now. In the end, you know, the President is the President, they can disregard the analysis and the Prime Minister in the UK likewise. But they've got to hear it the way. And it's got to be told the way the analyst sees it. So the other consideration which I talk about a bit in the book is why do we have. What is the survival value of intelligence to the human race? It enables better decisions by reducing the ignorance of the people have to take decisions of what they face. Daniel Kahneman wrote about thinking fast and slow. So when you're thinking fast and you're a hunter in the savannah, is that rustling in the bush my lunch or am I going to be its lunch? In those circumstances, you have to think fast. Not much use saying, well, it's 65% probability that this is likely to be a gazelle. But we also have the capacity to think slow. And that's what we are having this conversation about, the careful analytic thought. And if you use all information, open sources, secret sources, if you're a government, then you can reduce the ignorance of the people have to take the final decision about what they face. You never reduce it entirely, but you're making it easier for them to make sound decisions. So they've got to get the information honestly. It mustn't be spun at them. It mustn't be distorted because everybody knows that the President doesn't want to hear this particular line of, you had that in the United States recently over Russia under the last president. So there's got to be that ability to speak truth to power. On the other hand, if the analysts don't even know what the policymakers are up to, how do they know what kind of decisions are coming up? How can they help them take better decisions? So this is why we come up with this British model of adjoining rooms but thin partition walls. You've got to know enough about what is coming up on the policy agenda to be able to say, well, actually with our intelligence capability or in a business with our planning capability, we could deliver data on this and this and this. And we think that would really help you. And the policy makers are busy. They might not even be aware of how they might be helped by the analysts. So that's the kind of interaction which I think makes most sense.

Well, Sir David, I want to end with a question that I hope is hard, but I know you can hit it out of the park based on your SEAS model. It's extremely important to think about not just the situational awareness, not just your estimate of it, but what's coming next. The strategic notice. And that's what I wanted to ask you. What's next? What do you see in the future of humanity when it comes to all this information overload we're all being subjected to? Are we going to make it?

We?

What's your strategic view?

I'm an optimist, always have been. I'm a glass half full rather than glass half empty. But you know, if this year of pandemic and then extreme weather events should have taught us anything, it is that we need to really study. Look ahead. What are going to be the technological changes? What are going to be the changes in our planet and the climate, species loss of species diversity and all of those things, some of which are going to come and have very direct national security implications. We're going to see global population movements as the sea level rises. We're going to see desertification and wars breaking out over access to resources, the warming of the planet. We're going to see a flow of shipping passing from China to the west, through the northern routes, around Russia, around Canada. We're going to see struggles for resources and some things don't change in the world. One of those is human greed. So we are still going to see criminal exploitation of all of these things and at the moment, particularly of the technology. So watch out, because we see nothing yet when it comes to the malicious use of that wonderful invention, the Internet. So, you know, there are lots and lots of things out there that we need to keep a careful eye on. But we've survived worse over the centuries. I'm sure we will find ways of working through these. Right.

You are an optimist and that also comes through in your writing. And speaking of your writing, I just again, really appreciate the book.

I'm.

It's I'm glad you did this. I'm going to most strongly recommend it to any business out there that wants to better understand the world that they're in and how to act in it. And we'll definitely be putting links to that in our show notes. So thanks again. Really appreciate it.

It's been a great pleasure talking. Thanks Bob.

Thanks for listening to this OODA Loop production. For the latest analysis on cybersecurity technology and global risks, the please visit www.oodaloop.com.