India’s
artificial intelligence (AI) ambitions are moving beyond building models and attracting investment to a harder question: Can the country build trust while scaling AI?
The debate comes as India lacks a standalone AI law and instead relies on existing laws, sectoral oversight, and emerging governance frameworks. At the same time, rising deepfakes, synthetic content and public-sector AI use are increasing pressure for stronger safeguards.
Can India scale AI without a dedicated AI law?
Unlike some global peers pursuing dedicated AI legislation, India has chosen a more flexible route. Existing mechanisms such as data protection rules, IT regulations, sector-specific oversight and voluntary governance frameworks are becoming the foundation of India’s AI approach.
Experts say that may not necessarily slow adoption.
Rishi Agrawal, chief executive officer and co-founder of Teamlease Regtech, said that India has consciously avoided creating a standalone AI law and is instead building on existing legislation and governance mechanisms. However, he warned that sovereign AI expansion cannot rely only on broad policy intent.
"The real challenge is not whether India has an AI Act. It is whether every AI deployment has clear accountability, documented decision-making, continuous monitoring, audit trails and mechanisms to demonstrate compliance throughout the AI lifecycle," he told Business Standard.
Bruce Keith, chief executive officer and co-founder of AI-powered wealth-tech platform InvestorAi, drew parallels with India’s earlier digital growth model. "India has an incredible track record in scaling digital infrastructure without a single dedicated law—UPI and Aadhaar grew under a patchwork of RBI circulars, IT Act provisions, and sector-specific rules," he told Business Standard.
But he added that adoption and safe deployment are different questions. "Scaling adoption can definitely happen without a law. However, scaling it responsibly and accountably is where the gap begins to appear", Keith said.
Who takes responsibility when AI gets it wrong?
As AI moves into decision-making systems, another question is emerging: who becomes accountable when outcomes fail?
This becomes especially sensitive in public-sector deployment where decisions can affect welfare delivery, finance, healthcare and citizen rights.
Experts argue that responsibility cannot be outsourced to algorithms.
Rishi Agrawal said, "AI should never become a mechanism for diffusing accountability. Responsibility must continue to rest with the public authority deploying the system and the officials making the final decisions, not with the algorithm itself."
He said vendors remain responsible for model quality and documentation, while deploying institutions remain responsible for oversight and outcomes. According to him, governance systems similar to financial controls may become necessary.
"This is why public-sector AI requires governance mechanisms similar to financial controls. Defined ownership, independent validation, continuous monitoring, explainability, incident reporting and periodic audits are a must," he said.
Keith said this accountability gap is becoming visible globally. "This is a real accountability gap across the world, e.g. vendors usually try to limit liability or disclaim responsibility for 'model behaviour'. This is unacceptable."
Deepfakes may become India’s first real AI governance test
The governance debate is becoming more urgent as deepfakes move from isolated incidents to a broader challenge affecting fraud prevention, information integrity and public trust.
Experts say restrictive regulation alone may not solve the problem.
Rishi Agrawal said India can curb deepfakes and synthetic content but the answer lies in trusted governance rather than restrictive regulation. "Deepfakes are fundamentally a trust problem. Banning technology is rarely effective because AI capabilities evolve faster than legal prohibitions," he said.
He suggested focusing on technical safeguards. "India should focus on technical safeguards such as provenance, watermarking, content labelling, traceability standards and platform accountability."
He added that stronger governance may actually accelerate adoption rather than weaken it.
Rahul Agarwalla cautioned against overregulation. "Flagrant violations are already covered under multiple regulations. The challenge lies in the grey areas, where finding the right balance in regulation is tough."
Is sovereign AI reducing dependence?
The idea of sovereign AI is often linked with technological independence. But experts say complete self-reliance may not be realistic. Even countries investing heavily in domestic AI still rely on global supply chains for chips, cloud infrastructure and research ecosystems.
Rishi Agrawal said, "India's objective should, therefore, be to reduce critical dependencies where they matter most: sensitive public-sector data, critical government workloads, indigenous language models, trusted infrastructure and governance capability."
Keith said sovereignty often shifts dependence across different parts of the AI stack. "From an India perspective, the biggest opportunity is creating local language models, but if they are built on an open-weight foundation model released by a foreign lab, the base architecture, training methodology, and safety behaviour are still externally determined."
Will procurement become India’s real AI regulator?
Experts believe the next phase of AI governance may not come through legislation alone -- but through procurement standards.
Government purchasing decisions could determine how AI systems are designed, tested and monitored.
Rishi Agrawal said, "A dedicated public-sector AI governance framework should establish common standards across government for procurement, risk assessment, model documentation, independent audits, explainability, human oversight and continuous monitoring."
"Importantly, governance should not end when an AI system is procured. It should extend throughout the system's lifecycle from vendor evaluation and deployment to ongoing performance monitoring, incident management and retirement."
According to him, automated compliance systems and real-time audit mechanisms will become essential.
Keith said procurement is the most practical place to begin, while warning that audit capacity still remains limited.
Rahul Agarwalla added, "Procurement should be the key focus, as this will drive sovereign AI adoption. Explainability and audits can come later, once AI reaches a higher level of maturity."
India’s sovereign AI journey may ultimately be judged less by how quickly it deploys AI and more by whether citizens trust the systems being built. As AI moves deeper into public services, finance and everyday decision-making, governance may become the real infrastructure challenge. The next phase of India’s AI story may depend not on building larger models, but on proving that innovation, accountability and democratic oversight can scale together.
 "Sovereign AI's next challenge: Can India balance innovation and trust?")
