Russia cooperates with countries of global majority on AI regulation ...

The absence of a formal multilateral AI treaty between Russia and the bloc of global majority nations—BRICS, G77/LAI, ASEAN, AU, and their allies—has become a clear signal to policymakers and corporate strategists. While Russia participates in UN‑initiated “AI for Good” working groups and has signed isolated bilateral data‑exchange agreements with India and Brazil, it remains outside the mainstream of inclusive AI governance that is shaping the emerging global regulatory landscape.

• No multilateral treaty: As of December 19 2025, Russia has not entered into a binding framework with global majority countries on AI regulation.

• Domestic focus: The Ministry of Digital Development’s 2025‑2030 policy is inward‑oriented and prioritizes state control over data and security.

• Business friction: Multinationals face higher compliance costs and strategic uncertainty when operating across Russia and emerging markets that adhere to unified frameworks such as the EU AI Act or US NIST guidelines.

• Opportunity for alignment: Companies can leverage bilateral agreements (e.g., Russia‑India, Russia‑Brazil) to pilot joint ventures while preparing for future multilateral standards.

• Recommendation: Develop a dual compliance strategy that addresses both Russian domestic regulations and the evolving norms of global majority blocs, investing in legal expertise and standard‑setting participation where feasible.

The divergence between Russia’s internal AI policy and the inclusive governance models championed by global majority nations creates a fragmented regulatory environment. For enterprises with cross‑border operations, this translates into several concrete challenges:

• Compliance Overlap: Russian data‑protection rules—rooted in state surveillance priorities—do not align with the GDPR‑inspired standards adopted by many emerging economies. A single AI system deployed in both regions must be engineered to satisfy two distinct sets of requirements, inflating development and audit costs.

• Supply‑Chain Fragmentation: International suppliers may need to segregate data pipelines to avoid cross‑border transfers that violate Russian laws. This isolation can increase latency and reduce the effectiveness of distributed AI models.

• Investment Risk: The emphasis on state control over AI assets raises concerns about intellectual property protection for foreign firms. Companies may face forced technology transfer or limited recourse in disputes, deterring investment in Russia’s nascent AI ecosystem.

• Talent Mobility Constraints: Russian regulations restrict the export of certain AI expertise and technologies. Multinationals must navigate visa restrictions and data‑transfer limitations when deploying talent across borders.

Russia’s inward focus limits its attractiveness as a partner for global majority economies that are actively building inclusive AI ecosystems. The economic ramifications can be quantified through several indicators:

• In 2024, Russian AI R&D expenditure reached ₽35 billion (~$450 M USD), yet only 12% of this was earmarked for international collaboration projects. Compared to India’s $2.5 B investment in its National AI Strategy (2025) aimed at global partnership, Russia’s contribution is modest.

• Russian AI hardware exports declined by 8% YoY in 2024 due to export controls on dual‑use technologies, reducing the country’s role as a supplier of AI components to emerging markets.

• The 1,200+ AI startups registered in Russia (2025) are predominantly domestically focused, with only 3% engaging in cross‑border data projects. This limits job creation linked to global supply chains.

The regulatory gap between Russia and the global majority can be dissected through three lenses:

• Russian AI systems lack participation in ISO/IEC 23053‑1 or IEEE 7000 series, which are increasingly adopted by emerging economies to ensure transparency and accountability.

• Russia’s policy prioritizes data sovereignty at the expense of interoperability. This stance clashes with the “South‑centric” model advocated by BRICS, which emphasizes shared data governance frameworks.

• The absence of a multilateral treaty means that Russian AI products are subject to ad hoc bilateral agreements. These agreements often lack clear enforcement mechanisms, creating legal uncertainty for multinational enterprises.

For firms deploying AI solutions across Russia and global majority markets, technical architecture must accommodate divergent regulatory requirements without compromising performance:

• Design AI pipelines with modular compliance layers that can be toggled based on jurisdiction. This reduces code duplication and facilitates rapid updates when regulations evolve.

• Deploy edge devices in Russia to process sensitive data locally, reducing cross‑border transfer needs while maintaining model accuracy.

• Leverage federated learning frameworks that keep raw data within national boundaries, aligning with Russian data protection mandates and the privacy norms of emerging markets.

• Implement immutable audit logs using blockchain or distributed ledger technology to satisfy both Russia’s state oversight requirements and the transparency expectations of global majority regulators.

Investing in a dual‑compliance architecture can yield measurable returns over a 3‑year horizon:

• By avoiding duplicated compliance teams, firms can reduce regulatory overhead by approximately 15% annually.

• Modular compliance layers accelerate deployment cycles by up to 20%, translating into earlier revenue capture.

• Early alignment with emerging standards reduces the probability of fines and reputational damage, estimated at a potential $12 M annual savings for large enterprises.

• Even without a formal treaty, Russian AI firms can participate in ISO and IEEE committees through national delegations. This proactive engagement signals commitment to global norms.

• Use existing Russia–India and Russia–Brazil data‑exchange agreements as pilot platforms for joint AI projects that adhere to both domestic and international standards.

• Create internal guidelines that map Russian regulations against the EU AI Act, US NIST, and G77/LAI frameworks. Assign compliance leads for each jurisdiction.

• Retain counsel familiar with both Russian law and emerging market regulatory regimes to navigate complex cross‑border data flows.

• Track the progress of the 2026 “Global AI Governance Forum” and assess Russia’s potential participation. Adjust strategy accordingly.

The trajectory of Russia’s engagement with global majority AI governance is contingent on several macro‑level dynamics:

• A shift toward greater cooperation with BRICS or G77/LAI could open pathways for a multilateral treaty, especially if Russia seeks to counterbalance Western sanctions.

• The rollout of GPT‑4o and Claude 3.5 in enterprise settings may drive demand for unified safety standards that Russia would need to adopt to remain competitive.

• Should Russia secure significant foreign investment in AI infrastructure, it may be compelled to harmonize its regulations with global norms to attract capital.

The lack of a multilateral AI treaty between Russia and the global majority presents both risks and opportunities for multinational enterprises. While compliance costs rise and strategic uncertainty looms, companies can capitalize on existing bilateral agreements and proactive standard‑setting participation to mitigate exposure.

• Map your AI product portfolio against Russian domestic regulations and emerging market standards within the next quarter.

• Allocate budget for dual compliance architecture—target a 10% investment in modular compliance layers by Q4 2025.

• Engage with industry consortia (ISO, IEEE) to secure early access to evolving AI standards and influence their development.

By anticipating regulatory divergence and embedding flexibility into technical and legal frameworks, businesses can navigate Russia’s isolated stance while positioning themselves for a more integrated global AI ecosystem in the coming decade.