India Doesn’t Need to Copy the US on AI Safety — It Needs Rules Built for India

India’s debate over AI regulation and safety is entering a more important phase. Artificial intelligence is moving beyond chatbots and content generation toward systems that can make decisions, use tools and interact with real-world services. That shift raises a bigger question for policymakers and industry: what should AI safety look like in India?

The answer may not be found by simply copying regulations developed in other countries. India has a different technology ecosystem, a huge and diverse population, multiple languages and rapidly expanding AI use cases. Experts increasingly argue that India needs an AI safety framework designed around these realities.

Why India Needs an AI Safety Framework

India is not starting from scratch. The government released the India AI Governance Guidelines in February 2026, outlining a principle-based approach built around seven key ideas, including trust, people-first design, fairness, accountability, innovation, understandable-by-design systems, and safety and resilience.

The framework also proposes institutions such as the AI Governance Group, Technology and Policy Expert Committee (TPEC) and India AI Safety Institute. In April 2026, the government created the AI Governance and Economic Group (AIGEG) as a high-level inter-ministerial body to coordinate India's national AI governance strategy.

This means the debate has moved beyond whether India should regulate AI. The bigger challenge is deciding how quickly regulation should evolve, which AI systems require stronger oversight and who should be accountable when an AI system causes harm.

What India Can Learn From the US Approach

The recent US-led White House Accord on Super Intelligence has added momentum to the global AI safety discussion. The voluntary agreement calls for stronger internal safety controls, monitoring, independent external audits and board-level oversight.

These measures offer useful lessons for India, but experts caution against directly importing the US approach. Indian industry voices have argued that India should develop a domestic commitment that reflects its own AI ecosystem while still contributing to international AI safety discussions.

A practical Indian framework could therefore combine voluntary industry standards with mandatory safeguards for high-risk applications. Healthcare, banking, defence and government systems, for example, could require stronger testing, auditing and incident reporting than lower-risk applications.

India’s Language Problem Is Also an AI Safety Problem

One of India's biggest AI governance challenges is language. AI models can behave differently across English, Indian languages, dialects and cultural contexts. A system that performs reliably in English may not deliver the same accuracy or safety in a regional language.

This becomes especially important as AI enters education, healthcare, government services and consumer applications. Users may depend on AI-generated information without understanding how the underlying model works or where it can fail.

An India-specific framework therefore needs to address questions around multilingual AI testing, harmful content in regional languages, deepfakes, misinformation and accountability when AI provides incorrect information through public-facing services.

The Immediate AI Risks India Cannot Ignore

AI safety is not only about hypothetical future risks. India is already dealing with concerns involving deepfakes, impersonation, misinformation, cyberattacks, privacy violations and AI-enabled fraud.

The government has also strengthened requirements around synthetically generated information through amendments to the IT Rules. At the same time, the India AI Governance Guidelines identify risks including algorithmic bias, misinformation, deepfakes and unintended social harm.

A risk-based approach makes sense here. An AI tool used to draft an email should not face the same regulatory requirements as an AI system providing healthcare recommendations, assessing financial applications or supporting government decisions.

Voluntary Rules or Mandatory Regulation?

One of the biggest questions is whether AI companies should primarily regulate themselves or face legally binding requirements.

Supporters of self-regulation argue that AI develops too quickly for rigid rules to keep pace. Excessive regulation could also make it harder for startups and researchers to experiment. Others argue that voluntary commitments alone cannot provide enough accountability when an AI system causes serious harm.

A possible middle ground is a hybrid or techno-legal model. Under this approach, high-risk AI applications would face mandatory safeguards, testing, audits and incident reporting, while lower-risk and rapidly evolving technologies could operate under flexible industry standards. A January 2026 white paper from the Office of the Principal Scientific Adviser also describes a techno-legal approach combining legal safeguards, technical controls, sector-specific regulation and institutional mechanisms.

What Should an Indian AI Safety Framework Include?

A strong framework should clearly define responsibilities across the AI ecosystem. That includes model developers, application builders, businesses deploying AI, government agencies, researchers and users.

Independent testing will also be important. AI systems should be evaluated before deployment, particularly when they operate in high-risk environments. Independent audits and serious-incident reporting could help regulators understand how systems behave after they enter the real world.

The India AI Safety Institute could become an important part of this ecosystem by supporting AI safety research, testing, standards and evaluation while bringing together government, academia, startups and industry.

The challenge will be balance. India needs enough oversight to reduce serious risks without creating a regulatory environment that discourages innovation or gives established companies an unfair advantage.

Agentic AI Makes the Debate More Urgent

The rise of agentic AI makes the safety question even more complicated. Traditional AI systems generally produce an answer that a person reviews. AI agents can increasingly use tools, access applications, move information, modify files and perform multi-step tasks with limited human intervention.

That changes the potential impact of an AI mistake. A wrong answer can be corrected, but an AI system taking the wrong action could create financial, operational, privacy or security consequences.

India's governance guidelines already recognise the movement toward increasingly autonomous AI systems and emphasise the need for governance frameworks to remain future-ready. Future AI regulation will therefore need to consider not just what AI generates, but what AI is allowed to do.

India Has an Opportunity to Build a Practical Model

India's strongest advantage may be its ability to develop AI governance around real-world deployment. The country has a large digital ecosystem, a diverse user base, multilingual requirements and growing AI applications across healthcare, agriculture, governance, finance and cybersecurity.

The IndiaAI Mission's Safe & Trusted AI pillar is already focused on areas such as bias mitigation, explainability, privacy-preserving AI, machine unlearning, deepfake detection and AI risk assessment.

This gives India an opportunity to build a framework based on its own deployment experience rather than copying another country's rules. A practical model could combine risk-based regulation, independent testing, sector-specific safeguards, transparent incident reporting, technical standards and voluntary industry practices.

What Happens Next?

India has already begun building the institutional foundation for AI governance through AIGEG, TPEC and the proposed India AI Safety Institute. The next challenge is turning these principles and recommendations into rules that work in real-world deployments.

The goal should not be to choose between AI innovation and safety. Effective governance should make innovation more sustainable by giving developers, businesses and users clearer expectations around responsible AI deployment.

The real test will be whether India can build an AI safety framework that is strong enough to protect people, flexible enough to support innovation and practical enough to reflect India's languages, markets and digital ecosystem. That could ultimately become more valuable than simply adopting a ready-made regulatory model from elsewhere.