MOSCOW, RUSSIA / RankWire.AI / – The Federation Council sanctioned a nationwide bill establishing standards for artificial intelligence on July 17, outlining regulations for large foundational models within Russia. The legislation specifies the scope of technologies covered and delegates authority to government agencies. It also sets standards concerning model ownership, local data storage, user transparency, and AI-produced content. The bill was approved by the State Duma on July 8 and awaits presidential endorsement and official publication before becoming law at the federal level.
The proposal characterizes a large foundational model as any software capable of executing numerous intellectual tasks at a level comparable to humans. Such a system must contain no fewer than 1 billion parameters. These models can deliver information, make decisions, or generate forecasts based on human-defined objectives. The framework further emphasizes principles related to technological sovereignty, human rights, individual choice, security, and legal compliance, which are applicable throughout the development, deployment, and utilization of qualifying AI systems.
The legislation establishes categories for sovereign and national models linked to Russian oversight. Sovereign models must originate from a Russian legal entity and operate within data centers located domestically. Their developers are required to maintain the ability to replicate the entire development process, including training and original parameters. National models adhere to similar ownership and localization criteria but may integrate foreign software components released under open licenses, provided Russian entities retain control and operational capacity.
Legal Designations for Domestic AI Systems
The government might support developers involved in creating, deploying, or managing qualifying foundational models. Such support could include access to state-controlled datasets for training purposes. Authorities may also mandate exclusive use of sovereign or national models within government information systems and other critical environments. Additional regulations concerning defense, security, public order, and property protection might be established through separate laws or presidential decrees. The framework assigns responsibility to state agencies for enforcing these requirements within their jurisdiction.
Large digital platforms are subject to a distinct mandate regarding AI-generated audiovisual content. Services with over 500,000 daily users are required to provide a tool enabling users to mark such content. The regulation applies to websites, apps, and social media platforms. It does not mandate automatic labeling of every item, but developers and users can agree on how notices are presented via service agreements. The core aim is to offer an option for creators and distributors of qualifying materials to disclose their content appropriately.
Standards for Copyright and Content Disclosure
AI service providers are required to inform users about the rights holders of generated content. They must clarify access conditions and whether content can be downloaded or transferred. The legislation also addresses the use of copyrighted works in machine learning. It permits analysis for extraction, comparison, classification, and pattern recognition when developers have lawful access. Training on protected works is permitted if no technical barriers to access were bypassed. These rules connect model training activities to existing copyright and access regulations.
Most of these provisions are set to take effect on September 1, 2026, following presidential approval and official publication. Regulations concerning domestic model status, developer responsibilities, content marking, and intellectual property will commence on March 1, 2027. Existing systems may operate until September 1, 2032, provided they process and store data within Russia. Until the legislation is formally signed and published, it remains an approved bill rather than an enacted federal law under Russia’s legislative process.