IBM India Managing Director Sandip Patel has said that India is taking a 'very smart approach' to artificial intelligence (AI) regulation — one that safeguards public interest without stifling innovation or discouraging investment. Speaking on the sidelines of the IBM Think 2026 conference in Mumbai on Tuesday, Patel stressed that AI governance is 'very, very critical' at this juncture as the technology evolves rapidly across sectors.

India's balanced regulatory stance

Patel noted that India's current posture of calibrated regulation is likely to persist as AI technology continues to mature. Rather than rushing into prescriptive legislation, India is reportedly seeking a middle path — enabling innovation while embedding guardrails. The Centre is expected to release a consultation paper on AI regulations soon, with a focus on AI safety, skilling, and curbing risks from deepfakes.

Union Electronics and IT Minister Ashwini Vaishnaw has previously stated that AI 'cannot be governed through legislation alone,' advocating instead for a 'techno-legal solution' — a framework that blends regulatory oversight with technological enforcement mechanisms.

Digital sovereignty as 'Digital Swaraj'

Beyond regulation, Patel highlighted digital sovereignty as a defining challenge — not just for India but globally. He framed the concept as 'Digital Swaraj,' arguing that enterprises must maintain sovereignty aligned to the regulations they operate under and the workloads they handle.

'The bigger question that we should be asking ourselves is how do we drive digital sovereignty? And the issue of sovereignty, it's not just an India issue; it's global,' Patel said.

This comes amid growing concern worldwide about data localisation, cross-border data flows, and the dependence of critical national infrastructure on foreign-owned AI platforms — a debate that has intensified since the European Union's AI Act and similar frameworks emerging in the US and China.

Three pillars of AI governance

Patel outlined three distinct governance layers that enterprises must address. The first is data governance — ensuring data provenance, accuracy, and compliance with usage norms. The second is model governance, which involves tracking which AI models are deployed, whether they are updated, and whether they remain fit for purpose.

The third and broadest layer is overall AI governance, encompassing transparency in model usage and, critically, the explainability of outcomes. 'Can you explain your outcomes? Or are you letting your models hallucinate and do whatever they want? All of these components create governance modules for AI,' Patel observed.

IBM's WatsonX.gov gains traction in India

Patel pointed to IBM's WatsonX.gov platform as a validated governance solution now gaining adoption in the Indian market. He revealed that Infosys had initially started building its own AI governance framework but reportedly abandoned that effort after evaluating IBM's offering.

'When they saw what we had, they abandoned that, and they adopted our WatsonX.gov. And they are using it now, not only for internal work, but also for clients,' Patel said, adding that adoption is proliferating beyond a single enterprise.

What lies ahead

India's forthcoming consultation paper on AI regulation is expected to set the tone for the country's longer-term AI governance architecture. With the Centre signalling that no one should be left behind as AI advances, the focus will reportedly span safety, ethical deployment, and workforce skilling. For enterprises, the pressure to demonstrate transparent, explainable, and sovereign AI systems is only set to intensify in the months ahead.