NEW DELHI — Senior technology executives and policy experts have urged the Indian government to develop a distinct Artificial Intelligence (AI) safety framework tailored to the nation’s specific socio-economic context, rather than replicating the regulatory structures currently being formulated in the United States. The recommendations emerged from recent industry consultations, where stakeholders emphasized that a one-size-fits-all approach derived from Western models would fail to address the unique challenges posed by India’s linguistic diversity and vast digital population.
Rejection of US-centric models
The debate centers on the recent global push for AI governance, particularly the frameworks being discussed in Washington. While the United States has moved toward voluntary industry commitments and executive orders focused on frontier model safety, Indian industry leaders argue that these mechanisms do not account for the decentralized nature of India’s tech ecosystem. Unlike the US, where a handful of hyperscale corporations dominate model development, India’s AI landscape is characterized by a mix of large enterprises, agile startups, and government-led initiatives. Experts noted that a regulatory copy-paste from the US would impose compliance burdens that are disproportionate for smaller Indian innovators while failing to protect the broader public interest.
According to reports from LiveMint and The Economic Times, the core argument for an indigenous framework rests on the principle of digital sovereignty. Industry representatives stated that AI safety in India must address risks specific to the local context, such as the potential for AI-generated content to manipulate public opinion in multilingual environments or to exacerbate existing social biases. A framework designed primarily for English-centric data sets and Western legal standards is ill-equipped to handle the nuances of 22 scheduled languages and the diverse cultural norms across the country.
Focus on linguistic diversity and data sovereignty
A critical component of the proposed Indian framework is the integration of linguistic safety protocols. Experts highlighted that current global safety benchmarks often lack robust testing for Indic languages. Without specific safeguards, AI models deployed in India could generate harmful or misleading content in regional languages, a risk that is less prominent in US-centric discussions. The industry has called for the establishment of a national AI safety testing regime that includes rigorous evaluation of models for bias, toxicity, and factual accuracy across multiple Indian languages.
Furthermore, the discussion has extended to data sovereignty and infrastructure. India’s AI strategy relies heavily on the development of domestic compute infrastructure and the creation of high-quality, representative datasets. Industry experts argued that the safety framework must be aligned with these infrastructure goals, ensuring that safety standards do not inadvertently hinder the development of local AI capabilities. They suggested that the framework should encourage the creation of open-source safety tools and datasets that are representative of the Indian demographic, thereby fostering a robust and secure domestic AI ecosystem.
Policy implications and next steps
The call for a distinct Indian AI safety framework aligns with the government’s broader vision of positioning India as a global leader in AI. The Ministry of Electronics and Information Technology (MeitY) has been engaged in discussions with industry stakeholders to shape the national AI governance policy. While no final regulatory document has been released, the consensus among experts is that India must move beyond reactive compliance and proactively design a framework that balances innovation with public safety.
Industry leaders have proposed a multi-stakeholder approach, involving government bodies, academic institutions, and private sector players in the development of safety standards. This collaborative model is seen as essential for building trust in AI technologies among the Indian public. The experts emphasized that a transparent and inclusive process would not only enhance the credibility of the framework but also ensure that it remains adaptable to the rapid evolution of AI technologies.
The push for an indigenous framework also has implications for international cooperation. While India is committed to engaging with global AI governance bodies, experts argued that the country must assert its own regulatory identity. This approach would allow India to contribute unique perspectives to global AI safety discussions, particularly regarding the needs of the Global South. By developing a robust domestic framework, India can serve as a model for other developing nations seeking to harness the benefits of AI while mitigating its risks.
As the government continues to refine its AI policy, the industry’s call for a sovereign safety framework is likely to influence the final regulatory structure. The focus will remain on creating a balanced approach that supports innovation, protects citizens, and upholds India’s digital sovereignty. The next phase of policy formulation is expected to involve detailed consultations with technical experts and civil society groups to ensure that the framework is comprehensive and effective.
