Industry leaders advocate for a bespoke regulatory approach that prioritizes domestic linguistic needs and local economic conditions over international voluntary pacts.

India must develop a bespoke artificial intelligence safety framework that accounts for its unique linguistic diversity, massive scale, and application-centric ecosystem rather than adopting the voluntary standards recently established in the United States. This call for a sovereign approach follows the September 29 White House Accord on Super Intelligence, a pact signed by President Donald Trump and executives from major firms including Google, OpenAI, Anthropic, Meta, Nvidia, and xAI.

Srikanth Velamakanni, chairman of Nasscom and CEO of Fractal Group, argues that India should leverage its position within the Global Partnership on AI to establish a domestic commitment that serves as a foundation for global safety standards. He emphasizes that as AI models gain greater autonomy to make decisions and access critical systems, safety protocols must evolve faster than the underlying capabilities to mitigate catastrophic risks.

Soumen Mandal, principal analyst at Counterpoint Research, suggests that India can draw inspiration from international models while expediting the creation of a trusted local ecosystem. His proposed framework includes mandatory pre-deployment testing, independent third-party audits, and clear accountability structures for AI-related risks.

Amit Khanna, partner and automation ecosystem leader at Grant Thornton Bharat, highlights that India’s primary economic focus lies in building AI applications rather than foundational models. He maintains that any national commitment must ensure reliability across vernacular languages and unique cultural contexts to prevent the exploitation of knowledge asymmetry among the population.

Ganesh Gopalan, co-founder and CEO of Gnani.ai, advocates for a level playing field where global frontier models are subject to the same regulatory scrutiny as domestic offerings. He warns that overly rigid mandatory regulations could stifle competition and disadvantage local startups in a market that requires a wide array of domestic AI companies to prevent technological dependency.

The Indian government is currently evaluating its regulatory path, with IT Secretary S Krishnan noting that existing legal provisions may require supplementation to address the rapid emergence of synthetic content. IT Minister Ashwini Vaishnaw has previously indicated that the current information technology law, framed before the proliferation of generative AI tools, necessitates a new legal framework to manage the rapid evolution of machine learning capabilities.

Experts remain divided on the efficacy of voluntary self-regulation, with some arguing that binding rules are essential for transparency and incident reporting. Velamakanni notes that industries such as aviation and pharmaceuticals demonstrate how technically competent regulators can foster public trust without impeding innovation.

A hybrid approach appears to be gaining traction among analysts, who suggest applying mandatory rules to high-stakes sectors like defense, healthcare, and banking while maintaining voluntary standards for fast-moving technical fields. This strategy aims to provide companies with the flexibility needed for innovation while ensuring that fundamental safety and privacy protections remain intact.

The broader consensus among industry stakeholders is that any effective framework must extend beyond AI developers to include government agencies, public sector bodies, academia, and civil society. This multi-stakeholder model is intended to provide formal channels for reporting harms and ensuring that the deployment of autonomous systems remains under human oversight. The immediate priority for many remains the mitigation of existential threats to the social fabric, specifically deepfakes and the potential for political manipulation.

Future policy developments will hinge on the strengthening of the IndiaAI Safety Institute, which is expected to serve as the primary technical body for testing newly released models. Stakeholders are watching for clear guidelines on liability when AI systems fail, particularly in consequential areas such as medical diagnostics and welfare distribution, where the margin for error is minimal. The institute must balance its mandate to ensure safety with the need to avoid creating bureaucratic bottlenecks that could slow the deployment of beneficial AI applications across the Indian economy.

At Global Business & Economics Journal, we contribute to the  future of business and technology with news,  innovative research, expert insights, and thought-provoking analysis.