Spy catcher rivals – a story of artificial intelligence

Spy catchers. An intelligence analyst competes against a researcher armed with artificial intelligence. Who will catch the spy first?

The 4-page story is followed by a commentary on the differences between human intelligence and artificial intelligence, and the different roles of intelligence researchers and analysts. See commentary.

Artificial intelligence plays a big role for spy catchers (counter intelligence)
Artificial intelligence plays a big role for spy catchers (counter intelligence)

“A quick catch” – spy catcher story

Bhanuprakash  studied his opponent. Athanasia had sharp features, a brash character, and bright clothes. Her fingers moved so fast on her keyboard and mouse that she’d been supplied with a gaming console, and in the moments when she was reading she flexed her fingers as if squeezing imaginary stress balls.

A race, they called it. For his part, Bhanu sat rigidly in his chair with his hands poised above his keyboard like a piano player. His inputs were mostly limited to scrolling through the pages in front of him. Bhanu was immaculately dressed in neutral colours, with his facial hair trimmed so precisely that he stood out in the office. His weakness was his waistline, which had surrendered to his love for his wife and her cooking.

“Are you actually trying?” Athanasia called out from her console opposite him. “I’ve already let two of my cats out, there more to go. They’re sniffing out our suspects.”

Cats. Athanasia had given each of her artificial intelligence “engines” the name of a cat. She personalised them, and Bhanu had even heard her talking to them.

“It’s all action over here,” Athanasia said. She moved her arms like she was peddling a bicycle. “I’m starting by trawling through all our suspects and their contacts, and creating profiles. Next, I’m going for our suspects comms.”

Bhanu watched her animation. His face was neutral – he was born that way. He used his voice to project his thoughts. “I am reading, and I am thinking about the suspects.”

There were ten suspects, any one of which could have passed the secrets to the Chinese. On paper it was “trade secrets” but the 5G mobile technology on which they worked was treated as a national security secret.

“You do thinking? That’s prehistoric. There are quicker ways of achieving results.”

Bhanu considered the intelligence researchers he had known. She was on the hyper end of the scale. Perhaps it was her time working with artificial intelligence engineers that did it. Athanasia could coax results out of huge datasets using cleverly designed models of behaviour. Spy Catcher, they called her for her ability to find patterns in people’s behaviour that marked them as likely spies. In the last year she’d found a spy in a company building fighter aircraft, and another in the nuclear industry.

“Yes,” he retorted without taking his eyes from his computer monitor, “your method is quicker.” Though perhaps not so good for global warming, he pondered. The banks of computers became so hot that they were located in one of the coldest and windiest parts of the country.

“Hey, Bhanu, I’m not saying you’re redundant. You’re a great guy. You just need to get into machine thinking.”

Bhanu tilted his head gently to one side. His knowledge of artificial intelligence was not as great as Athanasia’s, but he knows its strengths and weaknesses.

By the end of the day he was still reading about their suspects. Unless he saw something special, he figured he would still be reading and checking in two days time. It takes time to understand people.

Athanasia was finished next morning. “Hey, Bhanu,” she announced. “I’ve got our suspect. It’s their systems architect who’s been leaking secrets to our enemies. His numbers are off the scale compared to the other people around him. It’s a definite match. I’m passing the name up. As they say in tennis: game, set and match.”

Bhanu paused. Her interruption was a distraction to his concentration. “Congratulations,” he said. He contemplated what he’d read about the systems architect. “An interesting find,” he said. “I wondered if you’d say that.”

“You had suspicions? Well done. But it’s not the same as hard facts. Sorry about that. You didn’t have a chance against my vast data lakes and machine learning.” She raised her arms into a power salute. “The Spy Catcher wins again!”

“Congratulations,” he said. “I think I’ll spend another day or so building up detail about the systems architect. It will be useful for the surveillance team.”

“You do that, Bhanu. I’ll write up the report for the Big Wigs upstairs.”

Bhanu went home and sat in the little room he had used as a home office during the Covid-19 lockdowns. The walls were white with no decorations, and a single window with shutters that he used to avoid distractions. It was his concentration room.

The next two days at work were difficult. There were increasing demands for him to give up and move to the next item on his worklist. He pretended to give in, to placate their managers, but he was still reading and thinking. And at the opposite desk, Athanasia had completed her report and was getting excited about some technology improvements.

“The surveillance teams are onto our guy,” Athanasia said suddenly. “They’re even watching his wife and eldest son.”

Bhanu considered the situation. “This may not be good,” he said.

“What? Of course it is. We need evidence to convict him, and to find his handler.”

“Athanasia, it’s time you and I had a talk with the people upstairs.”

It required a meeting, of course. Nothing important happened without a meeting, preferably with a lot of status posturing. This time they had a room with a view, of kinds. Bhanu listened to Athanasia reiterating her case. And at last they called on him to talk.

“We should consider the possibility that the systems architect is a decoy,” he said slowly.

“A decoy? No!” It was Athanasia, interrupting. “His profile is right. He has contacts, he has opportunities for dead letter drops, he’s got communications, he has unexplained finance, and he’s had holidays in places where he could have been recruited.”

She hadn’t been listening, Bhanu decided. “His profile is too good to be true – it’s as if it’s been artificially improved to please the machines.”

“So? Just supposing your hunch is right, we still need surveillance to eliminate the suspect.”

“By watching him, we’ll alert the real spy and his or her handler. They will go silent, and we’ll have no evidence against the traitor.”

“Then what do you suggest?” Athanasia was arguing directly at Bhanu as if there was no-one else present.

“We should look for the handler.”

“Bhanu, you’re talking nonsense: if we don’t have the spy, we don’t have the handler.”

“Not exactly,” he stood and moved to the whiteboard. “A decoy is only useful if the handler knows it’s come under surveillance. There has to be someone close to the decoy who is trained to watched for anything suspicious.” Bhanu wrote a name on the board. “Try this woman from the list of our decoy’s contact. She’s their child carer. I’ll show you her profile, it’s very indicative of a cut-out between a spy and their handler.”

“A cut-out? Someone who would know little if we arrested her. How do you know it’s her?”

“She also has contacts with another person on our team.” He wrote another name on the board. “To find the handler, follow the cut-out. Then we also have the traitor.”

Commentary on artificial intelligence, human intelligence and spy catchers

“Counter-intelligence is an art. We start with an empty canvas and use the CI tools on our palette to paint our picture.”

James, M. Olson, former chief of CIA Counter Intelligence
The spy catcher's: craft is an art, not a science.
The spy catcher’s: craft is an art, not a science.

On artificial intelligence vs human intelligence

The original purpose for this story was to explore how artificial intelligence can be tricked into giving false results. It’s a weakness that can be exploited by people with mischief on their mind. It can be used to defraud insurance and financial systems, and to confuse security and defence systems, and to queue jump.

In adding a twist to the end of the story, I found myself illustrating another important difference between humans and machine learning: most artificial intelligence relies on having a single measurable purpose. It has to be, because it’s calculations are based on comparing against the current case to large numbers of previous ones. Humans see the bigger picture, and some can make a jump in logic with seemingly random connections.

(I’ve also got other short stories on artificial intelligence – see all.)

On intelligence research vs intelligence analysis

I was surprised by the way the story illustrated the different approaches of intelligence research and intelligence analysis. Athanasia and Bhanuprakash are example of the two mindsets. They are, of course, just two fictional characters that represent snapshots of disciplines that attract a diverse range of personalities.

As background to intelligence researchers, they are trained to find relevant information for a particular purpose, or answer a simple question. In large intelligence agencies, researchers tend to be very specialist, working within narrow areas with highly specialised tools, and without visibility of the bigger picture – Athanasia is an example of that. Another characteristic of researchers is that they are trained to avoid making personal judgements.

Analysts make judgements, and they handle the “big” questions and, like Bhanu, they’re trained to see context and connections. Analysts bring together research from many areas, in order to explain why things are happening, and to explore possibilities of what may happen in the future. Within national intelligence agencies, there is a preoccupation with estimating the probability of different outcomes, so that politicians and leaders can make informed decisions. (I’ve got a separate story coming on that.)

Analysis takes time, even when it begins at the same time as the research work. The “customers” also have access to many of the research reports the analysts are using. (The customers include politicians, civil servants, military, other intelligence agencies, and more.) Many of the customers make up their minds before the analysis is complete. Using the story above as an example, the surveillance had started before Bhanu’s analysis was completed.

On this site there is are stories and articles on intelligence research (see more) and on intelligence analysis (see more). And a caveat: there are notable differences for intelligence research in small units and the public sector – that’s covered in “Intelligence research specialist jobs – 14 tips for survival.

On spy catchers and counter-intelligence

The counter-intelligence (spy catcher) theme was inspired by James, M. Olson’s book, “To Catch a Spy”, Georgetown University Press, 2019. http://press.georgetown.edu/book/georgetown/catch-spy The author had a successful spell as chief of CIA counter-intelligence, and then trained others entering the same profession.

This is an extraordinary book that distils his experience of catching spies, including his “ten commandments” for practitioners. I found it a good read, with 12 real cases and anecdotes from many others.

The book is also an eye-opener to the almost obsessive mindset of people who hunt for spies and protect against them. Perhaps they have to be like that to avoid the feelings of guilt others would have, and to have the persistence to keep going against so many obstacles. I was intrigued by Olson’s repeated reiterations of his anger against people who spy against the US. Good writing practice for textbooks is to avoid displaying emotions, but it was so deep inside him that he wrote in his own way.

If you want a study of the morality of counter-intelligence, this is the wrong book. The author’s mind is decided. Try something like “Principled Spying: The Ethics of Secret Intelligence”, by David Omand and Mark Phythian, Oxford University Press. https://global.oup.com/academic/product/principled-spying-9780198785590