Ride the Lightning

When the Russian Soldiers in Ukraine Talk, AI is Listening

April 6, 2022

Wired reported on April 4 that radio transmissions between members of the Russian army in Ukraine, captured from an unencrypted channel, revealed panic and confusion after coming under artillery fire.

Unsurprisingly, as the soldiers spoke, AI was listening. Their words were automatically captured, transcribed, translated, and analyzed using several artificial intelligence algorithms developed by Primer, a US company providing AI services for intelligence analysts. While it isn’t clear whether Ukrainian troops also intercepted the communication, the use of AI systems to intercept communications underscores the growing importance of sophisticated open source intelligence in military conflicts.

Unsecured Russian transmissions have been posted online often, translated, and analyzed on social media. Other sources of data, including smartphone video clips and social media posts, have received similar treatment. But the use of natural language processing technology to analyze Russian military communications is especially new and striking. For the Ukrainian army, making sense of intercepted communications generally involves human analysts translating messages and interpreting commands.

The tool developed by Primer shows how valuable machine learning could become for parsing intelligence information. We’ve seen significant advances in AI’s capabilities around image recognition, speech transcription, translation, and language processing thanks to large neural network algorithms that learn from vast amounts of training data. Off-the-shelf code and APIs that use AI can now transcribe speech, identify faces, and perform other tasks, often with high accuracy. In the face of Russia’s numerical and artillery advantages, intercepting communications is likely making a difference for Ukrainian troops.

As Wired reported, “Primer already sells AI algorithms trained to transcribe and translate phone calls, as well as ones that can pull out key terms or phrases. Sean Gourley, Primer’s CEO, says the company’s engineers modified these tools to carry out four new tasks: To gather audio captured from web feeds that broadcast communications captured using software that emulates radio receiver hardware; to remove noise, including background chatter and music; to transcribe and translate Russian speech; and to highlight key statements relevant to the battlefield situation. In some cases this involved retraining machine learning models to recognize colloquial terms for military vehicles or weapons.”

 The surprising part to me – and many military analysts – is that some Russian troops are using unsecured radio channels. It suggests an under-resourced and under-prepared operation.

Calder Walton, a historian of espionage at Harvard, says the invasion of Ukraine shows how valuable open source information has become for intelligence operatives. Facial recognition software has been used to identify some individuals in videos of the conflict. “We are at an absolute watershed in terms of the nature of intelligence collection and what’s available,” Walton says.

The conflict has highlighted the importance of mining different sources of intelligence. For instance, Ukrainian troops may have successfully targeted Russian generals by looking for gray-haired people near antennas in satellite, drone, or other imagery. Russian troops have also been using cellphones, sometimes revealing their location and details of missions, as well as their frustrations and low morale.

Walton says the NSA, the primary US signals intelligence agency, as well as GCHQ, the British equivalent, most likely have versions of the kinds of tools that Primer is using. But Primer is one of a growing number of companies that could make these technologies more accessible to those in the defense world and in private industry. The involvement of private companies in the war in Ukraine, such as those that provide satellite communications and imaging, raises questions about the power this gives those companies, and how they may become embroiled in an international conflict.

There will still be problems, from things such as algorithmic bias caused by poor quality or unrepresentative training data. Because machine learning algorithms often work in opaque ways, intelligence operatives will need to find ways to build trust in the conclusions these programs draw. An incorrectly transcribed communication could have deadly consequences on a battlefield, such as sending soldiers into an enemy position or misdirecting a missile strike.

Gathering and analyzing data using AI could become central to battlefield operations. The US military is investing millions to develop AI software capable of ingesting and analyzing different signals in the field. A US Army program called Tactical Intelligence Targeting Access Node proposes creating a ground station capable of ingesting and drawing insights from many different battlefield sensors and data sources.

If Russia’s invasion of Ukraine has relied on older tactics such as tank maneuvers and artillery bombardments, future wars that the US and other countries are preparing for may rely heavily on new technologies, including AI.

Battlefield use of AI may also become a game, experts say, with efforts to deceive or mislead algorithms becoming just as important. We’ve seen this gambit in cybersecurity – bad AI vs. good AI. We almost certainly will see it again here.

Sharon D. Nelson, Esq., President, Sensei Enterprises, Inc.
3975 University Drive, Suite 225, Fairfax, VA 22030
Email:   Phone: 703-359-0700
Digital Forensics/Cybersecurity/Information Technology
https://senseient.com
https://twitter.com/sharonnelsonesq
https://www.linkedin.com/in/sharondnelson
https://amazon.com/author/sharonnelson