India stands at a critical juncture with the rapid adoption of artificial intelligence. Various industrial sectors are outpacing the evolution of the institutional frameworks around AI. Regulating and standardizing their ethical deployment needs to battle key gaps in enforcement, auditability, and transparency. A structured, adaptive, and risk-based governance approach, that is anchored in data protection, will help the country with its social welfare goals for AI.  

In this blog, while understanding the emerging applications of AI, we will understand AI Governance in the Indian ecosystem.  

AI on the Ground 

From public safety to financial security and grassroots entrepreneurship, AI breakthroughs are addressing India-specific challenges with context-aware AI solutions. Context-driven, scalable, and inclusive solutions are redefining the country’s position in the global AI market. The following use cases would illustrate the turning point that India’s AI economy is headed toward.  

• AI-Powered Public Safety Monitoring: During the famous Ganeshotsav immersions, Pune Police deployed an AI-driven surveillance system linked to citywide CCTVs, generating 30,000+ real-time safety alerts for crowd surges, abandoned objects, and suspicious movements, leading to 100+ preventive interventions. 
• AI in Drug Regulatory Approvals: The Central Drugs Standard Control Organisation (CDSCO) is piloting AI to streamline drug test license approvals, cutting processing times from 3+ months to 45 days and reducing export certification delays to under a week. 
• Multilingual Conversational AI: Companies across banking, railways, and telecom are deploying multilingual voice AI bots capable of free-flowing, unscripted conversations in multiple Indian languages, improving customer engagement and accessibility. 
• AI for SMB Enablement: Haptik (Reliance Jio) launched “AI for All”, offering enterprise-grade AI agents for customer support, WhatsApp commerce, and marketing automation, priced affordably for small and medium businesses (SMBs). 
• RBI’s MuleHunter AI: The Reserve Bank of India (RBI) launched MuleHunter AI, an advanced machine learning system to identify mule accounts and suspicious transaction patterns, aiming to curb digital payment fraud and protect financial consumers. 

India’s National Strategy for Artificial Intelligence (NSAI) emphasizes deploying AI across as many sectors in India as required to tailor to the country’s socio-economic diversity. Consequently, India’s AI governance model has leaned toward flexible, market-friendly guidance rather than rigid prescriptive controls. This is in contrast to neighboring China’s state-centric, command-driven AI governance approach. 

AI Governance in Motion 

India’s demographic, linguistic, and developmental diversity makes a one-size-fits-all approach untenable. A governance framework must ensure AI systems are tailored to local languages, mitigate regional data biases, and support last-mile delivery of education, health, and welfare services. Here are some legal and regulatory roadblocks in India’s AI governance model. 

Digital Personal Data Protection (DPDP) Act, 2023 

• Passed in August 2023, the DPDP Act establishes a baseline for personal data rights, fiduciary duties, and consent mechanisms. However, without notified rules, its enforcement remains pending. 
• Significant Data Fiduciaries (SDFs): By classifying certain entities as Significant Data Fiduciaries, the DPDP enforces stricter compliance, including mandatory Data Protection Impact Assessments (DPIAs) and audits. For AI-driven organizations, this means that any automated decision-making, profiling, or algorithmic bias risks must be systematically identified, assessed, and mitigated before deployment.  
• Consent & Transparency in AI Models: AI models often depend on personal data ingestion for training, personalization, and recommendations. The DPDP mandates explicit, informed consent from data principals, alongside clear disclosures on how personal data will be used, stored, and shared. For generative AI and machine learning companies, this elevates the importance of consent-driven datasets and enforces stronger user rights. This also includes the ability to revoke consent and request data deletion, which is a direct check on opaque AI systems. 
• Data Sovereignty & Cross-Border AI Regulation: DPDP governs digital personal data processed within India and abroad when it involves Indian citizens. Hence, AI companies handling cross-border datasets will need to comply with data localization norms and government-led whitelists/blacklists for data transfers. This ensures that sensitive personal data stays within India’s regulatory purview. Policymakers get more control over AI model governance, training data provenance, and cross-border accountability. 
• Once in force, the Act will influence how AI systems access, process, and manage personal data for both model training and deployment 

MeitY’s Advisory & Subcommittee on AI Governance (2024–25)

• In November 2023, the Ministry of Electronics and Information Technology (MeitY) set up a Subcommittee on AI Governance & Guidelines Development, under the PSA-led advisory group, to produce actionable governance recommendations. 
• The resulting report, published for public consultation in early 2025, emphasized a whole-of-government coordination model, digital-by-design oversight, and cross-sector principles including transparency, accountability, fairness, safety, and inclusive innovation. 

Sectoral Initiatives & Fragmentation Risk

• The RBI’s FREEAI committee (2025) is working on an AI governance framework tailored to financial systems. 
• On the state level, Odisha recently approved an ambitious AI Policy-2025, launching the “Odisha AI Mission” emphasizing governance, skills, infrastructure, and ethical standards. 
• Meanwhile, pilot AI deployments in health and education at various jurisdictional levels risk producing a patchwork regulatory environment, making centralized coordination essential. 

Preparing for India’s AI Regulations 

India’s evolving AI governance model is being perceived as pragmatic but maturing. The recent developments hint at a flexible, activity-based, and sector-driven approach that is anchored in principles like transparency, accountability, and innovation enablement. With the DPDP Act, MeitY’s upcoming AI governance guidelines, and sectoral frameworks from regulators like the RBI, India is moving toward a multi-layered regulatory architecture. Companies will, therefore, need to balance innovation speed with compliance readiness. 

• Prioritize Data Governance & DPDP Compliance: Align AI data practices with the Digital Personal Data Protection Act, ensuring lawful data collection, consent, and storage. Build early readiness for cross-border data transfer restrictions. 
• Assess Risk Levels of AI Deployments: Expect activity-based classifications under MeitY’s draft framework that categorize AI use cases as minimal, medium, or high risk. High-risk deployments (e.g., credit scoring, healthcare diagnostics, surveillance) will likely require mandatory audits and pre-deployment assessments. 
• Implement Explainability & Transparency Mechanisms: Be prepared to provide model documentation, impact assessments, and disclosure statements, particularly for customer-facing AI like chatbots, voice assistants, and recommendation engines. 
• Stay Prepared for Sector-Specific Compliance: Monitor emerging regulator-led AI frameworks, such as RBI’s FREEAI committee recommendations for financial institutions or CDSCO’s AI protocols for healthcare approvals, to avoid fragmented compliance risks. 
• Build Localized AI Solutions: Focus on regional language capabilities, bias testing, and last-mile usability to align with India’s inclusion-first AI vision under NITI Aayog’s NSAI strategy. 
• Invest in AI Auditability & Assurance: Prepare for independent third-party audits and certification regimes likely to emerge under MeitY’s proposed framework. Establish internal mechanisms for logging, traceability, and provenance tracking from day one. 
• Engage in Policy Consultations: Participate in public consultations for upcoming AI regulations, industry whitepapers, and standards-setting processes to shape compliance frameworks and stay ahead of regulatory changes. 

India’s Path to Responsible AI Governance 

India stands at the cusp of becoming one of the world’s most dynamic AI-driven economies. But the pace of innovation demands an equally agile governance framework. The country’s current flexible, activity-based, and sector-specific approach reflects its ambition to foster innovation while safeguarding societal interests. However, as AI penetrates critical domains like finance, healthcare, and public safety, the cost of regulatory fragmentation and weak enforcement could become significant. By aligning innovation with accountability, India has the opportunity to set a benchmark for inclusive and context-driven AI governance.