Between the US innovation model, the EU rights model, and China's state-control model lies a vast terrain of countries seeking a fourth path. India, Brazil, Indonesia, Nigeria, and dozens of other nations are developing AI governance strategies that reject both unregulated market dominance and authoritarian state direction. These strategies, collectively, represent an emerging model of digital sovereignty — the assertion that nations have the right and capacity to govern their own digital futures on their own terms.
AI Regulatory Strategies for Sovereignty
Papyshev and Chan (2026), in Electronic Markets, examine how countries are developing AI regulatory strategies as expressions of digital sovereignty. Their analysis reveals that digital sovereignty is not merely about data localization or technology self-sufficiency — it encompasses the capacity to set rules, develop domestic capabilities, and ensure that AI systems deployed within national borders serve national interests.
The strategies they document share common elements: investment in domestic AI research capacity, development of national AI datasets (particularly in local languages and cultural contexts), creation of regulatory frameworks that balance innovation incentives with rights protections, and strategic engagement with international governance forums to ensure that global AI standards reflect non-Western perspectives. But the specific configurations vary dramatically based on economic structure, institutional capacity, and geopolitical positioning.
The BRICS Alternative
Ilyinsky (2026), in the HSS Bulletin of Financial University, analyzes the AI strategies of Russia, India, and Brazil — three major economies pursuing digital sovereignty from very different starting positions. Russia's approach emphasizes technological independence through domestic AI development and import substitution, driven partly by sanctions that limit access to Western technology. India's approach leverages its massive IT workforce and domestic market to build AI capabilities that serve both development goals and global competitiveness. Brazil's approach focuses on AI governance frameworks that address the country's specific challenges: inequality, environmental protection, and digital inclusion for marginalized communities.
The common thread is the refusal to accept either American or Chinese technological dependency. Each country recognizes that adopting foreign AI systems wholesale means accepting the values, biases, and strategic interests embedded in those systems. Digital sovereignty requires not just regulating foreign AI but building domestic alternatives — a challenge that requires sustained investment in research, education, and infrastructure that many developing countries struggle to afford.
Digital Neocolonialism
Kubantseva (2026), in Izvestiya of Saratov University, introduces a sharper framing: AI as a resource of digital neocolonialism. The argument is that countries that depend on foreign AI systems for critical functions — healthcare, education, agriculture, governance — cede cognitive and decisional autonomy to the nations and corporations that control those systems. When a country's hospitals use American diagnostic AI, its schools use Chinese educational platforms, and its farms use European precision agriculture systems, the country's development trajectory is shaped by foreign technological choices rather than domestic priorities.
This is not merely a theoretical concern. AI systems trained on data from wealthy countries perform poorly on problems specific to developing contexts — tropical diseases, informal economies, non-Western languages, diverse agricultural systems. Deploying these systems without adaptation reproduces the development patterns of previous technological waves, where technologies designed for wealthy contexts were transferred to developing countries with limited consideration of local needs.
The digital sovereignty response involves building AI capacity that addresses locally relevant problems with locally relevant data — a project that requires not just technology investment but fundamental shifts in how AI research priorities are set and funded. The countries that succeed in this project will have genuine digital sovereignty; those that do not will find themselves in a new form of technological dependency, more subtle than previous forms but no less consequential.
Building Local AI Capacity
The practical challenge for non-aligned nations seeking digital sovereignty is building AI capacity without the resources — massive datasets, expensive compute infrastructure, deep talent pools — that characterize AI development in the US, EU, and China. Several strategies are emerging.
Collaborative sovereignty involves groups of countries pooling resources for shared AI infrastructure. The African Union's AI strategy, ASEAN's approach to regional AI governance, and Latin American cooperation on AI ethics all represent attempts to achieve through collaboration what individual countries cannot achieve alone. These coalitions provide shared compute resources, training datasets reflecting regional languages and contexts, and collective bargaining power in international governance forums.
Open-source sovereignty leverages the global open-source AI ecosystem to build domestic capabilities without depending on proprietary foreign systems. Countries that develop expertise in fine-tuning, deploying, and maintaining open-source models can build AI capacity at a fraction of the cost of training models from scratch. This approach is not free — it requires investment in technical talent and deployment infrastructure — but it dramatically reduces the capital requirements for AI capability development.
Data sovereignty focuses on ensuring that the data generated within national borders is available for domestic AI development. Many developing countries generate vast amounts of data through mobile banking, agricultural monitoring, and government services, but this data flows to foreign platforms where it is used to train models that may not serve the data-generating country's interests. Data sovereignty policies — including data localization requirements, data sharing mandates, and data trusts — aim to ensure that domestic data contributes to domestic AI capability.