Securing Data Privacy in the Era of Generative AI | 10 Nov 2025

Source: IE 

Why in News? 

The rapid adoption of generative AI (GenAI) in India has raised government concerns over data privacy, inference risks, and national security, especially with foreign AI in official tasks, prompting an evaluation of its use in sensitive governance. 

• Inference Risk is the possibility that AI models may reveal sensitive information or deduce user roles, priorities, and strategic intent from prompts, even when the data is meant to be confidential.

What is Generative AI (GenAI)? 

• About: Generative AI (GenAI) is a type of artificial intelligence (AI) that creates original content such as text, images, audio, or code based on the data it has been trained on.  
  • Unlike traditional AI, which primarily analyzes, classifies, or predicts from existing data, GenAI produces new, human-like outputs, demonstrating creative generation beyond mere data processing. 
• Working: GenAI learns from large datasets (text, images, code, music) to identify patterns and relationships. Given a prompt, it generates new content based on these patterns.  
  • Modern GenAI mainly uses Transformer architecture, powering Large Language Models (LLMs) like ChatGPT, Gemini, and Claude, which excel at understanding context in sequential data. 
• Key Examples: Common examples include ChatGPT and Gemini for text, DALL-E and Midjourney for images, GitHub Copilot for code, Suno and ElevenLabs for audio, and Runway Gen-2 for video.

What Key Concerns are Associated with the Adoption of Generative AI? 

• Inference Risk: The core concern is that AI systems can infer sensitive strategic insights from user prompts and behavior, raising issues beyond basic data privacy. For instance, queries from senior bureaucrats and policy advisers can reveal national priorities, policy timelines, and system vulnerabilities. 
• Data Privacy Concerns: There is limited visibility into how foreign AI platforms store, track, or repurpose Indian user data — especially when such tools are integrated with telecom subscriptions and linked to verified phone numbers. 
• Mass Data Aggregation by Foreign Firms: Global AI firms could collect and analyze data from millions of Indian users to monitor societal trends and train advanced models, potentially putting emerging Indian AI initiatives at a disadvantage. 
• Dependence on Foreign AI Ecosystems: The widespread use of foreign GenAI services increases dependency on external technologies, potentially undermining India’s efforts toward data localisation, technological self-reliance, and digital sovereignty. 
• Policy Ambiguity: The absence of a uniform government policy on the official use of GenAI tools has led to fragmented approaches across departments — with some issuing bans and others experimenting without clear safeguards.  

What Measures is the Indian Government Taking to Mitigate the Risks of Generative AI? 

• Restrictions on AI Use in Government Work: The Indian government has started to restrict the use of foreign GenAI tools on official devices. E.g., the Ministry of Finance directed employees to avoid using ChatGPT and DeepSeek on office devices to protect confidential government data and documents. 
• Internal Monitoring and Policy Discussions: Various ministries are debating GenAI use in official functions, focusing on data privacy, inference risks, and whether to restrict or air-gap (keeping them disconnected from foreign AI platforms) official systems until domestic alternatives are ready. 
• Indigenous AI Development: The government is investing heavily in homegrown AI through the Rs 10,372-crore India AI Mission.  
  • It aims to develop at least 12 Indian LLMs and smaller domain-specific models, with startup Sarvam expected to launch an LLM by end-2026 targeting governance and public sector applications. 
• Subcommittee for AI Governance Guidelines: A subcommittee under the Ministry of Electronics and Information Technology (MeitY), under the IndiaAI Mission, recommended creating an India-specific AI Risk Assessment Framework to address local challenges and harms.  
  • It also proposed a whole-of-government approach to ensure coordinated and consistent AI governance across all sectors. 
• Push for Digital Sovereignty: The government urged the use of Indian-built digital platforms in sensitive areas like communication and governance, with senior officials shifting to Zoho Office Suite and Zoho Mail to enhance digital sovereignty and limit foreign data exposure.  
• Strengthening Security Frameworks: The government is developing standards for secure AI deployment, covering data protection, ethical use, and model transparency, with the IndiaAI Mission framework guiding safe adoption in sensitive areas. 
  • AI usage discussions are aligned with national security and technological sovereignty, echoing past measures like the 2020 Chinese app ban and promotion of domestic platforms like Koo and UPI.

How can India Build Sovereign AI Capabilities and Mitigate Foreign Platform Risks? 

• Strengthen Indigenous AI Capabilities: The top priority must be the fast-tracking of 12 indigenous LLMs for governance, alongside supporting domestic AI through grants, incubation, collaborations, and adoption incentives to reduce reliance on foreign platforms. 
• AI Prompt Sanitization Gateways: All official AI prompts must go through a secure gateway that removes metadata, context, and identifiers, while every query is logged in a secure internal registry for audit and pattern analysis, ensuring sovereign AI governance. 
• Creation a National Synthetic Data Fabric for AI Training: A key challenge for Indian AI is the lack of high-quality, culturally relevant datasets; the government can address this through a National Synthetic Data Generation Project using Indian LLMs to create diverse, privacy-preserving datasets for startups and researchers 
• Incentivize 'Niche Domain LLMs': Rather than competing with giants like GPT-4, India should focus on specialized, domain-specific LLMs, such as Nyaya-Shastra trained on Indian law, to deliver high-impact, strategic utility. 
• Foster Strategic Collaboration: Collaborate with friendly nations on secure AI, set global AI governance standards, and prioritize domestic innovation to ensure technology sovereignty while managing foreign AI risks. 
• Enforce Data Localization: Mandate all AI platforms in India to store, process, and train data domestically with strict encryption and privacy, and establish a National AI Security Framework for data handling, inference protection, and model transparency, aligned with the Digital Personal Data Protection (DPDP) Act, 2023. 
• Restrict AI Use in Sensitive Domains: Issue unified Standard Operating Procedures (SOPs) across ministries banning foreign GenAI tools for official tasks and implement secure, air-gapped AI systems to protect sensitive government data

Conclusion 

India’s adoption of Generative AI presents immense opportunities for innovation, governance efficiency, and digital services, but also raises data privacy, inference, and national security concerns. Strengthening indigenous LLMs, fostering swadeshi digital tools, and implementing structured AI governance frameworks are crucial to ensure sovereign, secure, and strategic AI capabilities.