AI Governance Frameworks in India: Balancing Innovation and Regulation

• Post by: Arjun Kumar
• July 15, 2026
• No Comment

Rohit Mehta ^[1]

---

^[1]  Visiting Researcher and Assistant Manager (IT & Digital), Impact and Policy Research Institute (IMPRI)

---

Title:	AI Governance Frameworks in India: Balancing Innovation and Regulation
Author(s):	Rohit Mehta
Keywords:	AI Governance, India AI Policy, Digital Regulation, Ethical AI, Data Protection, AI Ethics, Public Policy, AI Regulation India
Issue Date:	15 July 2026
Publisher:	IMPRI Impact and Policy Research Institute
Abstract:	Artificial Intelligence (AI) is rapidly reshaping governance, economic landscapes, and public service delivery across the globe. India, leveraging its extensive digital infrastructure and initiatives such as the India Stack, stands at the forefront of AI adoption in the developing world. However, this exponential growth presents a complex challenge: how to foster an environment of innovation while establishing robust regulatory safeguards to protect privacy, ensure fairness, and maintain public trust. This paper examines India’s evolving AI governance ecosystem through a qualitative policy analysis and comparative study. It analyzes key regulatory instruments including the Digital Personal Data Protection Act, 2023, and the National Strategy for AI against global frameworks such as the EU AI Act and the U.S. approach to AI regulation. The study identifies critical challenges, including algorithmic bias, regulatory gaps, and infrastructure deficits. It further proposes an original “Balanced AI Governance Framework” tailored for developing economies, emphasizing ethical AI principles, regulatory sandboxes, data protection mechanisms, and institutional oversight. The findings suggest that India must adopt a middle path combining the EU’s risk-based rigor with the U.S.’s innovation-centric approach to position itself as a global leader in inclusive and responsible AI.
Page(s):	15-24
URL:
ISSN:	2583-3464 (Online)
Appears in Collections:	IPRR Vol. 5 (1) [January – June 2026]
PDF Link:	https://iprr.impriindia.com/wp-content/uploads/2026/07/Insights-AI-Governance-Frameworks-in-India_-Balancing-Innovation-and-Regulation.pdf

---

1 Introduction

The dawn of the twenty-first century has been marked by the relentless advance of Artificial Intelligence (AI), a technology that promises to redefine human civilization. From predictive diagnostics in healthcare to algorithmic decision-making in welfare distribution, AI systems are increasingly embedded in the fabric of public and private life. For India, a nation with over 800 million internet users and a vibrant startup ecosystem, AI represents a unique opportunity to leapfrog traditional developmental barriers (NITI Aayog, 2021). Initiatives like the Unified Payments Interface (UPI), Aadhaar, and the CoWIN platform have demonstrated India’s capacity to deploy technology at scale, creating a fertile ground for AI-driven innovation.

However, the rapid proliferation of AI-driven systems brings with it profound concerns regarding data privacy, algorithmic discrimination, and the erosion of individual rights. The sheer volume of data generated by Indian citizens often collected without explicit informed consent poses significant risks to personal privacy (Mehra, 2023). Furthermore, AI algorithms, trained on historical data, risk perpetuating and amplifying existing social inequalities, particularly in a diverse society like India (Crawford, 2023). The lack of a comprehensive, cross-sectoral legal framework for AI specifically has left a regulatory vacuum, creating uncertainty for innovators and exposing citizens to potential harms.

This paper seeks to address this critical juncture. It asks: How can India craft an AI governance framework that balances the imperatives of technological innovation with the necessity of ethical regulation? To answer this, the paper adopts a qualitative methodology, employing a comparative policy analysis to juxtapose India’s regulatory trajectory with global models. It reviews existing literature, analyzes current policy instruments, and examines specific case studies of AI deployment in India. The ultimate aim is to propose a governance model that is not only theoretically sound but practically applicable to the unique socio-economic context of a developing nation.

2 Literature Review

The discourse on AI governance has evolved from initial focus on technical safety to a holistic examination of socio-economic impacts, ethics, and rights. At the global level, the OECD AI Principles (2019) established the foundational pillars for responsible AI: inclusive growth, sustainable development, human-centered values, transparency, robustness, security, and accountability. These principles have become a benchmark for national strategies, emphasizing that AI systems must be designed to respect human rights and democratic values.

In the academic sphere, the work of researchers at the World Bank has highlighted the distinct challenges of AI governance in developing economies (World Bank, 2022). Unlike the EU or the US, where digital infrastructure is mature, developing nations often face issues of digital literacy, infrastructure deficits, and informal data markets. This context necessitates a governance model that is not merely a clone of Western regulations but is tailored to local realities.

In India, the primary policy contribution has come from NITI Aayog’s discussion papers, particularly “Responsible AI for All” (2021) and the “National Strategy for Artificial Intelligence” (2018). These documents articulate a vision of “AI for All,” emphasizing inclusive growth but often lacking in enforceable regulatory mechanisms. Scholars have critiqued this approach as being more aspirational than substantive, noting the gap between policy pronouncements and ground-level implementation (Kumar, 2022).

The literature on AI ethics underscores the importance of “fairness, accountability, and transparency” (FAT). However, implementing these abstract principles in complex socio-technical systems remains a challenge. This review identifies a gap: while global frameworks exist, there is a lack of an integrated governance model specifically designed for the Indian context, a gap this paper aims to fill.

3 India’s AI Policy Landscape

3.1 The National Strategy for AI

India’s formal entry into the AI policy arena began with the NITI Aayog’s “National Strategy for Artificial Intelligence” in 2018. The strategy identified five core sectors for AI application: Healthcare, Agriculture, Education, Smart Cities, and Transport. It championed the use of AI for economic growth and social inclusion, famously coined as “AI for All.” However, the strategy was largely a guidance document, emphasizing research and skill development rather than regulation.

3.2 Digital Personal Data Protection Act, 2023

A landmark legislative development is the Digital Personal Data Protection Act, 2023 (DPDP Act). This act represents India’s first comprehensive attempt to regulate the processing of personal data. It establishes duties for data fiduciaries and rights for data principals (citizens), including the right to consent, correction, and erasure. Crucially, the Act imposes penalties for non-compliance, creating a deterrent against data misuse. While not exclusively an “AI law,” the DPDP Act provides the essential substrate for AI governance, as most AI systems rely on large datasets for training (Mehra, 2023). It addresses the critical issue of consent, requiring that data be processed only for specified purposes.

3.3 IT Rules and Sector-Specific Policies

Beyond the DPDP Act, the Information Technology (Reasonable Security Practices and Procedures and Sensitive Personal Data or Information) Rules, 2011, remain relevant, though they are considered outdated in the context of advanced AI. Sector-specific regulators have also begun issuing guidelines. For instance, the Reserve Bank of India (RBI) has released guidelines for the use of AI and Machine Learning (ML) in the financial sector, emphasizing explainability and model risk management. Similarly, the Ministry of Health and Family Welfare is drafting regulations for AI in telemedicine and diagnostics.

3.4 Government Vision: Innovation vs. Regulation

The Indian government’s vision has largely leaned towards promoting innovation. The “Make in India” and “Digital India” campaigns reflect a desire to position India as a global AI hub. The establishment of the India AI Governance Centre is a step towards institutionalizing these efforts. However, there is a recognized tension: heavy regulation could stifle the burgeoning startup ecosystem, while under-regulation could lead to societal harm. The current approach can be described as “soft law” guidelines and principles rather than binding legislation though the DPDP Act signals a shift towards harder law.

4 Global Comparison: Finding a Middle Path

To contextualize India’s approach, it is imperative to analyze the dominant global models of AI governance.

4.1 The European Union: The Risk-Based Rigor

The EU AI Act (2024) represents the most comprehensive attempt to regulate AI globally. It adopts a risk-based classification, banning AI systems that pose “unacceptable risk” (e.g., social scoring) and imposing strict compliance requirements on “high-risk” systems (e.g., in hiring, law enforcement, and critical infrastructure). The Act mandates transparency, human oversight, and documentation. For India, the EU model offers a template for ensuring safety and rights protection. However, its prescriptive nature may be overly burdensome for a developing economy with a nascent AI sector.

4.2 The United States: Market-Driven Innovation

The United States has adopted a sectoral, market-driven approach, prioritizing innovation over top-down regulation. The “Executive Order on Safe, Secure, and Trustworthy Artificial Intelligence” (2023) represents a shift, but the U.S. relies heavily on self-regulation and industry standards. This approach fosters rapid innovation but risks leaving citizens exposed to algorithmic harms and bias.

4.3 China: State Control

China’s approach is characterized by state-centric control, with regulations focusing on content moderation, algorithmic recommendation, and deepfakes. The “Generative AI Services Management Measures” (2023) emphasize alignment with socialist values. India’s governance model cannot be a carbon copy of any of these, given its democratic setup, federal structure, and developmental priorities.

4.4 India’s Middle Path

India is uniquely positioned to chart a “middle path.” It can borrow the risk-based classification logic from the EU to protect fundamental rights while incorporating the innovation-friendly sandbox approach seen in the US and UK. India’s model must be “developmentally regulatory” meaning it regulates enough to ensure trust and safety but not so much that it impedes technological adoption for societal benefit.

5 Key Challenges in AI Governance

Despite progressive policies, several challenges impede effective AI governance in India.

5.1 Data Privacy Risks

The lack of robust data protection before the DPDP Act led to the proliferation of unregulated data collection. Even now, enforcement remains a challenge. The sheer scale of data collection by government platforms (e.g., Aadhaar) and private players creates persistent risks of surveillance and data breaches.

5.2 Algorithmic Bias

AI systems are only as good as the data they are trained on. In India, historical data often reflect caste, gender, and economic biases. AI-driven credit scoring systems may exclude marginalized communities, while algorithmic hiring tools may discriminate against women. The lack of diverse datasets in Indian languages further exacerbates this issue, leading to exclusionary technologies.

5.3 Regulatory Gaps

India lacks a dedicated “AI Act.” The DPDP Act covers data, but AI-specific issues like algorithmic accountability, automated decision-making, and synthetic content generation are not comprehensively addressed. This regulatory ambiguity creates uncertainty for businesses.

5.4 Skill and Infrastructure Deficit

A significant challenge is the lack of technical expertise within government agencies to understand, audit, and regulate complex AI systems. There is a dire need for AI literacy among policymakers and regulators.

6 Case Studies

6.1 AI in Healthcare: Predictive Diagnostics

AI is making significant inroads into Indian healthcare. Algorithms are used for early detection of diabetic retinopathy, cancer prediction, and drug discovery. For example, AI-driven platforms are being integrated into the Ayushman Bharat health scheme to optimize resource allocation. However, these systems raise concerns regarding patient data privacy and the “black box” nature of diagnostic algorithms. If an AI system misdiagnoses a patient, determining liability is complex.

6.2 AI in Governance: Welfare Targeting

The most visible application of AI in governance is the use of algorithms for targeting welfare schemes. The government uses the Socio-Economic Caste Census (SECC) data and Aadhaar to identify beneficiaries for schemes like PM-KISAN. While this has improved targeting efficiency and reduced leakages (“DBT” or Direct Benefit Transfer), it has also led to “exclusion errors” where legitimate beneficiaries are denied support due to algorithmic flags. This highlights the risk of automating high-stakes decisions without adequate human oversight.

6.3 AI in Agriculture: Crop Prediction

AI models are being deployed to advise farmers on crop selection, fertilizer usage, and weather forecasting. Initiatives like the Kisan drones and AI-powered soil health cards represent a push towards precision agriculture. The benefit is increased productivity; the risk lies in data ownership and the potential marginalization of small farmers who lack access to smartphones or digital literacy.

7 Proposed AI Governance Model

Based on the analysis of India’s challenges and global best practices, this paper proposes a “Balanced AI Governance Framework” tailored for developing economies. This model moves beyond the binary of “innovation vs. regulation” to a symbiotic relationship.

7.1 The Framework Diagram

The proposed framework rests on four interdependent pillars:

• Ethical AI (The Foundation):
  • Principles: Fairness, Accountability, Transparency, and Privacy (FATP).
  • Mechanism: Mandatory “AI Ethics Impact Assessments” for high-risk AI systems, similar to Environmental Impact Assessments.
• Regulatory Sandbox (The Engine):
  • Purpose: To allow innovators to test AI solutions in a controlled environment with relaxed regulations, under strict monitoring.
  • Application: Specifically designed for startups and research institutions in sectors like fintech, health, and agriculture.
• Data Protection Mechanism (The Shield):
  • Implementation: Full enforcement of the DPDP Act, 2023.
  • Extension: Creation of “Data Trusts” or common data facilities for sensitive sectors (health, finance) to enable AI training without compromising individual privacy.
• Institutional Oversight Body (The Watchdog):
  • Structure: An independent “AI Regulatory Authority of India” (AIRAI).
  • Functions: Auditing algorithms, setting technical standards, handling citizen grievances, and ensuring compliance.
  • Features: It must include a multi-stakeholder panel (technologists, ethicists, lawyers, and civil society) to ensure diverse perspectives.

7.2 Core Additions for the Model

To ensure the framework is actionable, the following are integrated:

• Public Audit Systems: Mandatory algorithmic audits for public sector AI.
• Explainable AI (XAI) Requirements: Developers of high-risk AI must provide explainable outputs for decisions affecting individuals.
• Redressal Mechanisms: A clear legal pathway for citizens to challenge automated decisions.

8 Policy Recommendations

To operationalize the proposed framework, the following policy actions are recommended:

• Legislative Action: Enact a dedicated “Artificial Intelligence Act” that builds upon the DPDP Act, specifically addressing algorithmic accountability and sectoral risks.
• Capacity Building: Invest in training judges, bureaucrats, and law enforcement in AI literacy.
• Promote R&D in Ethical AI: Fund research into explainable AI, bias detection, and privacy-enhancing technologies.
• Foster Public-Private Collaboration: Use regulatory sandboxes to bridge the gap between tech startups and government regulators.
• International Cooperation: Align with global standards (OECD, ISO) while protecting India’s strategic interests.

9 Conclusion

India stands at a critical juncture in its AI journey. The nation has the potential to become a global AI powerhouse, driven by its talent pool, digital infrastructure, and massive data resource. However, realizing this potential sustainably requires a governance ecosystem that earns public trust. The “Balanced AI Governance Framework” proposed in this paper offers a roadmap for achieving this balance. It advocates for a risk-based, flexible regulatory approach that safeguards fundamental rights while unleashing innovation.

By learning from the EU’s rigor, the US’s dynamism, and addressing the unique challenges of the Global South, India can define its own path. This path must be paved with ethical principles, robust data protection, institutional oversight, and a commitment to inclusive growth. Only then can India truly harness AI “for All.”

References

European Commission. (2024). Regulation (EU) 2024/1689 laying down harmonised rules on artificial intelligence (Artificial Intelligence Act). Official Journal of the European Union. https://eur-lex.europa.eu/eli/reg/2024/1689/oj

Kumar, A. (2022). AI governance in India: Policy challenges and opportunities. Journal of Information Policy, 12(4), 45–67.

Mehra, S. (2023). Data protection in the digital age: The Indian DPDP Act. Legal Press.

NITI Aayog. (2018). National strategy for artificial intelligence: #AIforAll. Government of India. https://www.niti.gov.in/sites/default/files/2019-01/NationalStrategy-for-AI-Discussion-Paper.pdf

NITI Aayog. (2021). Responsible AI for all: Approach document for India. Government of India. https://www.niti.gov.in/sites/default/files/2021-02/Responsible-AI-22022021.pdf

Organisation for Economic Co-operation and Development. (2019). OECD AI principles. OECD Publishing. https://oecd.ai/en/ai-principles

The Digital Personal Data Protection Act, 2023, No. 22 of 2023, Gazette of India. https://egazette.nic.in/WriteReadData/2023/248045.pdf

World Bank. (2022). Digital development and AI governance in emerging economies. World Bank.