Formation of Legal Regimes for Artificial Intelligence in BRICS Countries: From National Strategies to Potential Integration

Aim and tasks. The article provides a comparative analysis of the formation of artificial intelligence (AI) legal regimes in the BRICS states. Methods. The methodological basis of the study is the civilizational approach, which allows taking into account the specifics of legal mentality, the genesis of national legal orders, and the variability of attitudes towards technological regulators in each of the member countries. Results. The authors substantiate the thesis that deepening cooperation within the association objectively requires a synthesis of two levels of legal formalization: international law (conclusion of treaties) and domestic (harmonization of legislations). Based on the analysis of national strategies, sustainable regulatory patterns are identified: the Chinese model of “sovereign AI” and the export of normative meanings, Indian an approach combining strategic planning, flexible adaptation of existing norms, and a reliance on ethics in the absence of a single comprehensive AI law, the Brazilian model of precedent-based regulation through data protection, the Russian approach to standardization as an integration tool, as well as the specifics of South Africa and new members (UAE, Saudi Arabia, Iran, Ethiopia, Egypt). Particular attention is paid to the institutional architecture of cooperation, including the activities of the Joint Working Group on Digital Technologies and the prospects for establishing a BRICS Artificial Intelligence Alliance. The article systematizes key problems: heterogeneity of technical standards, imbalance of technological influence (86% of generative AI is accounted for by China), fragmentation of the institutional structure, and extraterritorial pressure from Western regulators. Based on the analysis of the BRICS Leaders Declaration on AI Global Governance (Rio de Janeiro, July 2025) and current dynamics, a forecast for the development of the association’s legal architecture until 2030 is proposed, including the formation of a common data market and the creation of a World Organization for AI Cooperation. Conclusions. The authors conclude that the Russian-Indian precedent of mutual certification of AI solutions in 2026 demonstrates the economic efficiency of harmonization and creates the basis for the formation of a macro-region of technological sovereignty on its own regulatory framework.

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