Roskomnadzor (RKN), Russia’s federal internet regulator, plans to deploy machine learning technologies to enhance its ability to detect and block VPN traffic at the national level.
The initiative, outlined in the agency’s 2026 digital transformation plan, signals a strategic pivot toward algorithmic censorship aimed at overcoming the growing use of encryption and anonymization tools in the Russian internet space.
According to internal documents reviewed by Forbes Russia, RKN has earmarked ₽2.27 billion (approximately $25 million) to develop a machine learning–driven traffic filtering system this year. The project is part of a broader federal mandate requiring agencies to report on AI adoption across operational domains. The system will be integrated into Roskomnadzor’s existing infrastructure known as TSPU (Technical Means of Countering Threats), which is already deployed across major Russian telecom networks under the 2019 “sovereign internet” law.
These TSPU devices currently rely on Deep Packet Inspection (DPI) technology to enforce state-imposed content blocks. However, traditional DPI methods are limited when it comes to detecting encrypted or obfuscated traffic, such as that generated by VPNs. The integration of machine learning will allow Roskomnadzor to move beyond static filtering and begin dynamically identifying VPN protocols, encrypted tunnels, and other circumvention techniques based on traffic behavior and metadata.
Founded in 2008, Roskomnadzor plays a central role in regulating media, telecommunications, and personal data in Russia. It operates the national blacklist of prohibited websites, targeting content ranging from child exploitation and suicide instructions to online gambling and extremist material. Over the past five years, RKN has significantly ramped up its technical capabilities, using automated systems to monitor, analyze, and restrict online content at scale.
With VPN usage on the rise following increased censorship and platform bans, RKN appears to be escalating efforts to counter digital anonymity tools directly. Machine learning models integrated into TSPU could support filtering based not just on known signatures or IPs but also on traffic shape, packet timing, and flow dynamics. Once flagged, this traffic could be throttled, blocked outright, or logged for further analysis.
Roskomnadzor has already implemented AI across other parts of its enforcement apparatus. Neural networks currently assist in scanning text, video, and audio for prohibited content, with reported success in reducing content detection time from 48 hours to six hours. Automated systems like Oculus and Vepr are used to monitor social media and news sources for “points of information tension” and illegal materials.
Still, the move to apply AI in the context of VPN blocking represents a significant escalation in Russia’s internet control strategy. It also introduces new challenges for VPN providers and developers of censorship circumvention tools, many of whom already rely on techniques like domain fronting, randomized packet sizes, and pluggable transports to evade DPI systems.
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