From AI Policy to Proof: Why India Needs Evidence-Ready AI Governance

Artificial Intelligence has become a interesting subject of contemporary legal scholarship, regulatory design, and market governance. As AI systems increasingly shape decisions in sectors such as finance, healthcare, employment, public administration, and consumer services, the legal system faces the challenge of ensuring that these technologies operate within frameworks of accountability, transparency, and demonstrable compliance. The traditional approach of adopting broad AI policies is no longer sufficient. Regulators, courts, and market actors now require evidence‑ready governance: the ability to identify the precise obligation applicable to an AI system, the accountable owner within the organisational structure, the control mechanism used to manage the obligation, and the evidence demonstrating that compliance has been achieved. This shift reflects a deeper transformation in legal compliance, where principles must be supported by operational structures capable of withstanding regulatory and judicial scrutiny.

The evolution of AI governance has been shaped by significant international developments. The OECD Recommendation on Artificial Intelligence established foundational expectations for trustworthy and human‑centred AI. The European Union’s Artificial Intelligence Act introduced a risk‑based regulatory model that imposes stringent documentation, oversight, and auditability requirements for high‑risk AI systems. India’s National Strategy for Artificial Intelligence emphasised responsible innovation, transparency, and accountability, while the Digital Personal Data Protection Act introduced obligations relating to data processing, algorithmic fairness, and user rights. These frameworks collectively demonstrate a global movement from aspirational ethics to enforceable legal compliance. Academic literature has reinforced this trajectory, arguing for governance models that integrate legal, ethical, and technical layers and emphasise evidence‑based oversight as a core requirement of responsible AI deployment.

Despite these global developments, India currently has no dedicated legislation governing Artificial Intelligence. While sectoral regulations and constitutional principles offer partial safeguards, the absence of a comprehensive statutory framework leaves significant gaps in accountability, risk classification, liability, and oversight. Given the scale at which AI systems are being deployed in India—particularly in financial services, public administration, and digital platforms—there is a growing consensus among legal scholars that Parliament must seriously consider enacting a specialised AI law. The constitutional architecture also provides a pathway for judicial intervention. Under Articles 32 and 226, the Supreme Court and High Courts possess the authority to entertain public interest litigation on matters affecting fundamental rights. It is legally conceivable that the judiciary may, suo motu, initiate proceedings to examine the risks posed by unregulated AI systems and call upon Parliament to frame an appropriate legislative framework, as it has done in other emerging domains such as environmental protection, privacy, and sexual harassment at the workplace. Such judicial action would not constitute legislation but would reflect the Court’s constitutional role in prompting democratic institutions to address urgent regulatory voids.

The integrity of AI governance depends on the connection between four essential elements: the obligation that defines what must be done under law or policy; the owner who is accountable for fulfilling the obligation; the control that describes how the obligation is managed; and the evidence that demonstrates review, monitoring, and compliance. When these elements are disconnected, organisations face fragmented accountability, inconsistent oversight, and manual evidence‑generation processes that are vulnerable to regulatory challenge. Legal research increasingly shows that governance failures arise not from the absence of policy but from the absence of structured accountability mechanisms capable of demonstrating compliance across complex organisational and jurisdictional landscapes.

Legal entity structure plays a decisive role in determining accountability for AI systems. Multinational organisations operate through subsidiaries and affiliates that are subject to different regulatory regimes, business functions, and AI use cases. A European subsidiary deploying a high‑risk AI system must comply with the EU AI Act’s documentation and oversight requirements, while an Indian entity may be governed by the Digital Personal Data Protection Act and emerging algorithmic transparency obligations. Mapping obligations to the correct legal entity ensures that compliance is not only documented but legally defensible. Market analysis shows that governance failures frequently occur when obligations are assigned at the group level without clarity on entity‑specific responsibilities, creating gaps in oversight and increasing regulatory exposure. Courts and regulators increasingly expect organisations to demonstrate entity‑level accountability rather than relying on generalised corporate policies.

The future of AI governance will rely on continuous monitoring, automated evidence generation, and cross‑entity accountability. Organisations will need to embed ethical review mechanisms into operational workflows and adopt interoperable compliance technologies capable of supporting multi‑jurisdictional regulatory requirements. As AI systems evolve, governance frameworks must adapt to ensure transparency, accountability, and resilience. The convergence of legal, technological, and ethical oversight will define the next stage of governance maturity, particularly as markets demand demonstrable compliance rather than policy declarations. Legal scholarship suggests that future regulatory models may incorporate dynamic obligations, real‑time auditability, and cross‑border harmonisation of AI standards.

The shift from AI policy to evidence‑ready governance represents a fundamental transformation in corporate accountability and legal compliance. Organisations capable of tracing obligations, ownership, controls, and evidence across legal entities will not only meet regulatory expectations but also lead in responsible and ethical AI adoption. The defining question for modern governance is no longer whether an organisation has an AI policy, but whether it can prove the integrity of its governance. This requirement will shape the legal landscape of AI for the coming decade, influencing regulatory enforcement, judicial interpretation, and market expectations.

Vishal Kale

Advocate and Litigation Counsel

References

Cath, C. (2022). Governing Artificial Intelligence: Ethical, Legal, and Technical Perspectives. Oxford Internet Institute. European Commission. (2024). Artificial Intelligence Act (Regulation (EU) 2024/1689). Official Journal of the European Union. Floridi, L., & Cowls, J. (2021). A Unified Framework of Five Principles for AI Governance. Harvard Data Science Review, 3(1). Gasser, U., & Almeida, V. (2022). A Layered Model for AI Governance. IEEE Internet Computing, 26(2), 4–9. Kuner, C. (2022). Data Protection and Artificial Intelligence: The Need for Evidence‑Based Compliance. International Data Privacy Law, 12(3), 145–158. NITI Aayog. (2020). National Strategy for Artificial Intelligence: #AIforAll. Government of India. OECD. (2019). Recommendation of the Council on Artificial Intelligence. OECD Legal Instruments No. 105.