Russia Expands AI Use Beyond the Battlefield
On June 6, 1972, just over a week after Richard Nixon’s visit to Moscow ended, the film Residence Permit premiered in the Soviet Union. On its surface, it was an ordinary drama. In reality, it was a finely engineered piece of state propaganda—a story about a doctor who remains in Western Europe only to discover the stories of prosperity there are a myth. His decision to leave, the film concludes, is the greatest mistake of his life. To spread that message, Moscow needed an entire machinery of studios, censors, and distribution networks. The infrastructure was vast, expensive, and slow. More than 50 years later, the Kremlin’s goals remain largely unchanged. It still seeks to project similar messages domestically and abroad. What has shifted, however, is the cost, speed, and scale of pursuing those ambitions. The reason is artificial intelligence, but not in the way the term is usually understood.
The Hardware Gap
When analysts speak of an AI race, they typically mean a contest over who builds the most powerful models and the fastest chips. By that measure, Russia occupies a complicated position. It faces a significant structural constraint: hardware. Samuel Bendett, an adviser to the Russia Studies Programme at CNA in Washington, D.C., says that Russia has a strong pool of talent—STEM-educated specialists and mathematicians capable of developing advanced software. “But hardware has always been the weakness, and this goes back to the early days of the Cold War,” he adds.
This weakness matters enormously in the modern AI setting. Cutting-edge machine learning systems depend on specialised chips—graphics processing units and AI accelerators—capable of performing vast numbers of mathematical operations simultaneously. Currently, Russia cannot produce the advanced chips needed for frontier AI. Western sanctions following the invasion of Ukraine have created even more problems. As a result, Moscow relies on smuggled or Chinese-sourced components for more sophisticated systems. “Russia loves NVIDIA microchips and depends on them for military-related AI applications, apparently. The same can be said about hardware such as Raspberry Pi and Orange Pi. That hardware is not produced in Russia or, if its equivalents are actually manufactured domestically, they are already outdated compared to global standards,” Bendett says.
Automated Disinformation and Cyber Warfare
But hardware constraints have done little to curb the Kremlin’s broader ambitions. Instead, it has shifted focus to a different kind of battlefield—one where semiconductor shortages matter far less. In this space, the priority is not building the most advanced systems, but shaping the environment in which they operate: influencing what Western AI models retrieve, controlling what its own citizens see at home, and dictating what people beyond the borders believe.
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Sopo Gelava has been researching disinformation for more than a decade and has worked with the Atlantic Council’s Digital Forensic Research Lab since 2020. She notes that in recent years, the use of AI in the creation and dissemination of Russian disinformation campaigns has significantly intensified. “Actors who once created such content manually now show much less direct human involvement,” she notes.
Currently, Russia cannot produce the advanced chips needed for frontier AI (this needs to be removed if I used it, it’s in source). Gelava explains that even a single operation, originating from a Russian website and then spreading across platforms in multiple languages, can show clear signs of AI use throughout the process. “Either automation is being used, or AI is involved in generating the content. This hasn’t caused a revolutionary shift in disinformation, but it has made it far more scalable indeed. It gives creators much greater capacity to spread content at unprecedented speed and reach very large audiences. Overall, AI enables them to achieve significantly greater impact,” she argues.
These campaigns are often most active in countries where Moscow has political interests. They tend to intensify before elections, but they do not stop once voting ends. The narratives continue, adapting to new events and audiences. In one recent case, the Digital Forensic Research Lab identified a network of TikTok accounts. They appeared to coordinate the spread of AI-generated content targeting Moldova’s ruling Party of Action and Solidarity and President Maia Sandu, while also encouraging people to join protests.
AI is deployed in multiple ways within these operations. It can automate the synchronized spread of narratives across platforms, or enhance visual content to heighten emotional impact and increase engagement. The objective remains consistent: to reach as many people as possible, as efficiently as possible. In Moldova’s case, the analyzed TikTok accounts had a combined following of 158,556 users, with total engagement exceeding 26.3 million across all interaction types.
AI is making it easier not only to scale disinformation, but also to intensify cyberwarfare. In April, Dutch military intelligence warned that Russia is using AI to accelerate cyberattacks, with the threat expected to grow. What is changing is not just the speed of these operations, but their structure. AI is shifting cyberattacks from labor-intensive efforts to highly automated processes. This allows multiple targets to be identified and hit simultaneously. Tasks that once required sustained human effort can now be executed in seconds, significantly expanding both the scale and reach of these operations.
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The Domestic Surveillance State
The same logic behind Russia’s use of AI abroad—based on automation, scale, and efficiency—is increasingly being applied at home. “Internal security has always been at the forefront of Russian high-tech development in general,” Bendett says. “A key priority has been how to insulate the country from external influence and limit its impact on the domestic population.”
AI is now making that approach dramatically more powerful. Where state propaganda once depended on extensive physical infrastructure—studios, printing presses, distribution networks—it can now be managed digitally. This allows the Kremlin to monitor, filter, and shape its information environment with far fewer people and at far greater scale. In January, Forbes reported that Roskomnadzor—the federal body for regulating and censoring telecommunications—plans to deploy a machine learning-based system for filtering internet traffic within a year. According to the agency’s digitalization plan submitted to the government, 2.27 billion rubles ($30 million) has been allocated to the initiative. The system aims to identify and block prohibited content more efficiently and restrict access to VPN services that Russian citizens use to circumvent censorship.
Bendett believes that what is happening in Russia now, with restrictions on Telegram, broader internet blocking, and limits on VPNs, runs counter to long-term logic. He argues that if most Russians are cut off from international IT applications and global messaging platforms, it will hinder development over time, because Russia’s IT and high-tech sector is small. “Russia’s government policies, which are currently aimed at limiting the population’s access to some of these international components, are probably shooting themselves in the foot,” Bendett says. “This is delaying many projects and developments that would have unfolded if Russian developers and users had access to Western applications, databases, and algorithms.”
The surveillance architecture extends into physical space as well. Across Russian cities, street cameras embedded with AI-powered recognition systems are being used to monitor public spaces and identify individuals in real time. In Yekaterinburg alone, around 1,000 additional cameras are expected to be installed by the end of June, covering streets and public areas. The systems analyze video feeds continuously, significantly expanding the state’s capacity to monitor its population without requiring a proportional expansion of human personnel.
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Shaping Global AI Models
While these operations are visible, if difficult to counter, there is another dimension that is far harder to detect. Recent studies suggest that one of Russia’s most consequential AI strategies is aimed not directly at populations, but at the models they increasingly rely on to interpret and understand the world. This strategy targets Western AI models indirectly by shaping the data they are trained on and the sources they retrieve information from.
A network of pro-Kremlin websites has reportedly used AI tools to flood the internet with millions of pieces of Russian propaganda. Much of this content is designed to be picked up by search engines and scraped into large datasets used to train AI systems. Researchers describe this approach as a form of data poisoning by scale, where the aim is not a single piece of misinformation, but a sustained saturation of the information ecosystem. The concern is that, over time, this could subtly shape how AI systems interpret, prioritize, and reproduce information.
Sopo Gelava notes that Russia’s AI-driven tactics are becoming more sophisticated over time. AI-generated content used in disinformation campaigns was once relatively easy to spot. But that is changing quickly. “There used to be frequent grammatical errors, and in the past we could often tell from this that the operation had been created by AI. Today, however, it gives more opportunities to creators of disinformation because the translation is much more refined and significantly better adapted to the local context,” Gelava says.
Researchers studying Russia’s use of AI believe its parallel efforts in the global AI race are becoming harder to detect, more scalable, and more targeted. They not only reach wider audiences but also risk shaping how information is interpreted and reproduced across digital systems. The Soviet strategy relied on physical infrastructure like studios and censors to control the narrative, a strategy that was expensive and visible. Today, Moscow is employing a parallel logic but with a different method: digital saturation. By flooding datasets rather than building studios, Russia is attempting to achieve the same political outcomes at a fraction of the cost, shifting the battleground from the physical world to the digital training data of foreign systems.
