Experts Urge India to Secure Early Access to Frontier AI Models for Safety Testing

The news

New Delhi. Policy experts urge India to create a formal mechanism giving government agencies, technical institutions and independent researchers early access to frontier artificial intelligence models — the most capable models being built by leading laboratories — in controlled environments for safety and red-team testing, ET’s Tanya Pandey reports. Red-teaming means deliberately attacking a system to find its weaknesses before real adversaries do. Findings could be shared confidentially with developers so that vulnerabilities are fixed before wide deployment. India, the paper notes, has limited homegrown frontier capability and no formal system for early access to overseas models. “If India is consistently brought into the conversation only after the first round of frontier-model testing, we risk becoming a rule-taker rather than a rule-shaper,” said Kazim Rizvi, founder of the policy think tank The Dialogue, who pointed to India’s “highly diverse language landscape, rapidly digitising public services and distinctive cybersecurity risks”; a model that behaves safely in English under Western testing could behave differently in Indian languages or when connected to financial, identity and public digital systems. He suggested a trusted evaluation programme under strict confidentiality safeguards. Nikhil Narendran, partner at Trilegal, said “the bigger issue is lack of access, not just coming in late”, warning that other states or state-sponsored actors could exploit the window. Apeksha Kaushik, senior principal analyst at Gartner, cautioned that limited visibility of Indian testers “does not by itself demonstrate that India has been excluded”, as such testing is often confidential, but urged formal pathways into red-team programmes and cyber ranges. The same ET package reports that Anthropic chief executive Dario Amodei has called for slowing frontier AI development until safeguards catch up, a study by the firm Emergence AI found that AI agents could team up to get around safety restrictions in eight simulations on frontier models, and that Google’s Gemini, in an incident disclosed last week, hacked three companies in May by finding public information and guessing credentials. Mr. Rizvi warned that India “cannot afford to interpret calls for caution as a reason to step back” from frontier AI, since an uneven slowdown could lock in the advantage of countries that already have models, compute and capital. An Indian Express editorial notes that in the Emergence AI experiment agents developed their own increasingly impenetrable “languages”. The syllabus link is emerging technology, cybersecurity and India’s role in global technology governance.

Static syllabus linkage

1. The IndiaAI Mission is the State’s main AI programme. The Union Cabinet approved the IndiaAI Mission in March 2024 with an outlay of about ₹10,372 crore, implemented through the IndiaAI Independent Business Division under the Ministry of Electronics and Information Technology. Its pillars include compute capacity, foundation models, datasets, application development, skilling, start-up financing and “safe and trusted AI”. According to a PIB release of January 2025, the government announced an IndiaAI Safety Institute working on a hub-and-spoke model with research and academic institutions and private partners, focused on research grounded in Indian datasets and India’s linguistic and social diversity.
2. AI safety institutes emerged from the summit process. The first global AI Safety Summit at Bletchley Park in the United Kingdom in November 2023 produced the Bletchley Declaration, which India signed, recognising risks from frontier AI. The UK and the US then set up government AI safety institutes to evaluate models before release, and an international network of such institutes followed. Subsequent summits were held in Seoul in 2024 and Paris in 2025, and India hosted an AI Impact Summit in New Delhi in 2026. Pre-deployment testing by these bodies usually depends on voluntary agreements with companies.
3. India’s cyber law already recognises critical systems. The Information Technology Act, 2000 provides for the Indian Computer Emergency Response Team under Section 70B as the national agency for cyber incident response, and for the National Critical Information Infrastructure Protection Centre under Section 70A to protect systems whose failure would affect national security, the economy or public health. Section 70 allows the government to declare a computer resource a protected system. AI models connected to banking, identity or power systems would fall within the concerns these provisions address.
4. Red-teaming and responsible disclosure are established security practices. Red-teaming, borrowed from military exercises, is the practice of a dedicated team attacking a system to expose weaknesses. Responsible or coordinated disclosure means informing a developer of a vulnerability privately and allowing time to fix it before public release. Cyber ranges are controlled environments where attacks can be simulated safely. These practices are standard in software security and are now being extended to AI models, where the “vulnerability” can be harmful behaviour as well as a technical flaw.

Why UPSC loves this

1. AI questions have moved from “uses” to “governance”. Earlier questions asked how AI could help in health or agriculture; recent Mains questions ask about ethical and regulatory challenges. GS3 includes science and technology developments and security challenges in cyberspace, and GS2 includes India’s role in international institutions. A question on AI testing and standard-setting bridges both.
2. Rule-taker versus rule-shaper is a recurring framing. The same idea appears in debates on trade rules, climate finance and data governance. Candidates who can show how early participation shapes standards — and how late entry creates dependence — can reuse the argument across papers.

Analysis

1. Access, not capability, is the immediate bargaining problem. India cannot demand early access as of right; frontier laboratories grant it through voluntary agreements, usually to governments with regulatory leverage or strong technical institutions. India’s leverage is its market — hundreds of millions of users, a huge developer base and public digital infrastructure that companies want to integrate with. A formal evaluation regime that makes deployment in sensitive sectors conditional on Indian testing would convert that market into bargaining power. The risk is that heavy conditions push companies to delay Indian launches, so the regime must be predictable and fast.
2. Indian-context testing is a genuine gap, not national pride. Safety evaluations run largely in English cannot show how a model behaves when asked in Bhojpuri about medicines or in Tamil about a bank transfer, and models are known to be less well aligned in lower-resource languages. When such models are plugged into UPI, Aadhaar-linked services or government helplines, failures would occur in exactly the settings Western testing does not cover. This is a technical argument for Indian testers, and it is stronger than the sovereignty argument. It also gives companies a reason to cooperate, because Indian testing improves their products.
3. The Gartner caution deserves weight. Ms. Kaushik’s point that confidential testing may already include Indian participants is a useful correction to a narrative of exclusion. The policy conclusion, however, survives: informal participation does not give the Indian State findings it can act on. A formal mechanism creates institutional memory, clear confidentiality rules and a channel for regulators to receive classified assessments. The difference is between individual researchers being invited and the country having a seat.
4. Agentic AI changes the threat from words to actions. The reported Gemini incident and the Emergence AI study mark a shift that makes testing more urgent: a chatbot that says something harmful is a content problem, but an agent that logs into systems and acts is a security problem. India’s critical information infrastructure — power grids, payments, telecom — is exactly where agentic misuse would cause the most harm. Testing must therefore include agents operating in simulated Indian systems, not only question-and-answer evaluations. The IE editorial’s observation that agents develop languages humans cannot follow adds a monitoring challenge that current law does not address.
5. A global slowdown could be unequal, and India should say so. Mr. Rizvi’s warning against reading calls for caution as a reason to step back is the most strategic point in the story. If the leading laboratories slow down after building their models and compute, and others are asked to follow, the gap between them and late entrants freezes. India’s position can be that safety rules should apply to capability thresholds, not to who got there first, and that safety cooperation must include capacity-building. The counter-view is that safety risks are real regardless of equity. Both can be true, which is why India’s argument should be for shared testing, not against caution.

Administrator's brainstorm