Artificial Intelligence sovereignty has become a central question in global politics. The issue extends far beyond the question of technological self-sufficiency. It concerns the power to shape knowledge systems, regulate digital markets, protect national security, and preserve policy autonomy in an era when AI increasingly structures economic, political, and social life. A country that depends on foreign AI models, cloud infrastructure and data architecture will eventually find that critical decisions are being mediated by external assumptions, commercial priorities, and geopolitical interests. The danger is already visible. Contemporary AI systems are largely built in Western institutional and commercial settings, trained on datasets that reflect Western linguistic dominance, legal reasoning, and cultural frameworks. Their outputs therefore often present Western perspectives as neutral default positions. In fields such as international law, public policy, policing, security analysis, and ethics, this bias can produce serious distortions — for instance, AI-assisted, data-driven policing tools are more likely to target marginalised groups like Dalits and Muslims as these communities are over-represented in prejudiced, historical criminal databases by Western writers. Interpretive traditions from India and the wider Global South are frequently sidelined, diluted, or recast as secondary viewpoints. This is not a marginal, technical flaw. It is a structural problem that can shape public discourse and statecraft.
India must recognise AI as a core national infrastructure as dependence on externally controlled foundational models carries serious strategic consequences. It embeds foreign cultural and normative assumptions within domestic institutions and exposes digital systems to sanctions, supply restrictions, and extra-territorial legal authority, constraining India’s regulatory autonomy. Dependence on
foreign computing also affects the country’s ability to capture the economic value generated by Indian data, Indian labour, and the scale of India’s digital market. But AI sovereignty does not mean technological isolation. India must remain integrated with global innovation networks while retaining authority over the critical layers of compute, data, models, and governance. Achieving this balance demands a deliberate industrial and regulatory strategy.
Investment in domestic compute infrastructure is the first priority. Sovereign data centres, high-performance computing clusters, and semiconductor capability are essential foundations. India must also build large multilingual datasets rooted in its linguistic diversity and social realities so that Indian languages are fully represented in AI systems used across education, governance, and commerce. Public support for the development of domestic foundational models is equally important through research partnerships, procurement commitments, and open innovation frameworks. Regulatory autonomy must be protected and trade agreements should not restrict access to source code, algorithmic auditing powers, or control over public data resources.
The question is straightforward: will India develop AI systems that reflect its own realities or operate within systems designed elsewhere? The answer to this will determine sovereignty, economic resilience, regulatory independence, and strategic autonomy.
