AI Legislation in Russia – Current Status

As artificial intelligence (AI) technology continues to advance and integrate into various sectors, countries worldwide are grappling with the need to establish robust legal frameworks to govern its use. Russia is no exception, with recent legislation reflecting an effort to balance innovation with ethical and legal considerations.

Key Legislative Frameworks

Federal Law No. 258-FZ: Digital Sandboxes

Enacted in January 2021, Federal Law No. 258-FZ establishes “digital sandboxes” in Russia. These sandboxes are designated areas where AI technologies can be developed and tested under relaxed regulatory conditions. This experimental regime is designed to monitor and address the legal and ethical issues that arise from real-life applications of AI. Key concerns include liability for AI malfunctions and the protection of personal data privacy​ (Gowling WLG)​.

Federal Law No. 123-FZ: Initial Steps Towards AI Regulation

Effective since July 2020, Federal Law No. 123-FZ marked one of the first significant legislative steps towards AI regulation in Russia. This law provides a framework for digital innovation within Moscow, setting the stage for broader regulatory efforts. Crucially, it introduces key definitions for artificial intelligence and related technologies, laying the groundwork for future regulations​ (Gowling WLG)​.

AI Training Data and Copyright Concerns

The use of copyrighted works for training AI models has sparked significant debate globally, and Russia is no exception. The main concern revolves around balancing the need for innovation with the protection of intellectual property rights.

Global Perspectives and Comparisons

In the European Union, the AI Act mandates transparency regarding the use of training data. This regulation requires AI developers to disclose the content used for training their models, giving copyright holders the opportunity to opt out and demand authorization for the use of their works. This approach aims to protect the rights of content creators while fostering technological innovation​ (Mintz)​.

The United States has seen numerous legal challenges highlighting the tension between fair use and copyright infringement. High-profile cases, such as the New York Times’ lawsuit against AI companies, underscore the complexities involved. These cases argue that using copyrighted content without permission to train AI models constitutes copyright infringement, reflecting a broader concern about how AI technologies utilize existing creative works​ (Ropes & Gray LLP)​​ (JD Supra)​.

Russia’s Broader Strategy and Future Directions

Russia is actively addressing these issues as part of its broader strategy to integrate AI into its economy. The country’s legislative efforts aim to ensure that technological advancements do not outpace the necessary legal and ethical frameworks. By creating experimental regimes and establishing clear definitions and guidelines, Russia seeks to foster innovation while safeguarding the rights and privacy of its citizens​ (Gowling WLG)​​ (Mintz)​.

As AI continues to evolve, the legal landscape will undoubtedly need to adapt. Russia’s proactive approach, mirrored by similar efforts globally, highlights the importance of a balanced and forward-thinking regulatory framework. This balance is essential to support technological progress while ensuring that ethical considerations and intellectual property rights are adequately protected.

There haven’t been any widely publicized legal complaints specifically against Yandex regarding the use of AI and copyright. However, the issue of AI and copyright infringement is a significant and evolving area of law globally, including in Russia.

Dan Taylor is the founder and editor of International Search Insights (ISI), the publication behind ReadIsi, and a Partner and Head of Innovation (Search & AI) at SALT.agency. He has worked in search for more than a decade, in-house and agency-side, and is regarded as one of the West’s leading voices on search engines beyond Google, including Yandex, Baidu, Naver and Cốc Cốc. On ReadIsi, Dan covers the engines and AI answer platforms that English-language coverage tends to miss: Baidu and ERNIE, Yandex and Alice AI, Naver, Cốc Cốc, Shenma, Sogou, WeChat and Weibo search, plus DeepSeek and Alibaba’s Qwen. He also keeps dated records of documented changes, such as the Yandex, Baidu and Naver algorithm update histories and the DeepSeek, ERNIE and Qwen model update histories. His approach is to take learnings from global search engines and apply logic with a high evidence threshold. A claim needs a source, patterns seen in one market are treated as a hypothesis until they are checked in another, and where the evidence is thin he says so. For brands, that means advice built on what can be shown, with measurement that separates being cited from being chosen. Dan is credited with coining the term “edge SEO” and won the inaugural TechSEO Boost research competition in 2018 for his work on CDN and edge changes for SEO. In 2026 he was credited with coining “Infinite Tail” to describe how keyword research is changing with AI search. He is the author of AI Search Marketing For The Travel Sector, writes for Search Engine Journal, Search Engine Land and Travel Marketing Guy, and has advised technology, SaaS, travel and enterprise ecommerce brands, including Cloudflare, GitLab and Airbnb. He has given multiple talks and panels at the International Search Summit in Barcelona, and has also spoken at Optimization Moscow, brightonSEO, Google Search Central Live Deep Dive Asia in Bangkok, Athens SEO and the Asia SEO Virtual Summit 2026, where his talk looked at how Baidu, Naver, DeepSeek, Tencent, Cốc Cốc and Yandex built their own AI answers.