Shaping the AI Sandbox Ecosystem for the Intelligent Age 2025

To be effective, an AI sandbox must begin with a clearly defined objective – whether the focus is enabling innovation, regulatory experimentation or a hybrid approach. –Clearly articulate the primary purpose: innovation, regulation or hybrid – along with specific end goals. –Identify the scope, based on target sectors and anchor use cases: for example, healthcare diagnostics, MSME export compliance agents, agri-input optimization tools, voice-first AI agents for rural service delivery, autonomous agri-drones, Sewa agents (AI-based virtual assistants for accessing public schemes and entitlements) in rural India or AI-powered warehouse robots. –Map key stakeholders: government nodal bodies (government agencies with primary responsibility for sectoral implementation and coordination), AI start-ups, domain experts and infrastructure providers and potential adopters (for example, public agencies). Example: A sandbox for AI-enabled diagnostics would involve India’s Central Drugs Standard Control Organization (CDSCO),17 health departments, AI health-tech start-ups and medical institutions, enabling sector-aligned validation and deployment.Define objectives and scope 4.3.1 A strong governance model builds credibility and ensures trust in experimentation environments, which is essential for secure, transparent and inclusive AI sandbox environments. For research- intensive sectors, participation also depends on robust IP protection and clear data-ownership norms. Sandboxes must support sector-specific agreements, enable confidential computing environments and maintain audit trails to safeguard proprietary research and sensitive workflows. This can be enabled by the following: –Define access and participation/eligibility criteria based on alignment with sectoral needs or problem statements. Ensure selection criteria are transparent and accessible, minimizing bureaucratic hurdles such as excessive paperwork. A single-window entry system with modules linked to relevant government schemes (e.g. Startup India, India AI Mission) can further ease participation. –Set up inclusive governance boards with representation from ministries, legal and policy experts in AI policy and AI risk management, start-ups, industry, academia and civil society. –Embed a localized responsible AI risk- management framework, adapted from global standards such as the US government’s National Institute of Standards and Technology (NIST) AI Risk Management Framework (RMF),18 to systematically identify, document and manage AI risks. –Enable clear coordination protocols for onboarding, stakeholder management and conflict resolution. Inclusion of consumer consent mechanisms, where applicable, can improve trust, scale and innovation. –Incorporate robust data-sharing and privacy policies, modelled on frameworks such as India’s Data Empowerment and Protection Architecture (DEPA).19 –To enable secure multi-tenant data access, adopt security approaches such as Zero Trust architecture and confidential computing. Include protocols for secure data destruction post- usage, especially for sensitive sectors such as healthcare. Example: In an education sandbox, the inclusion of edtech firms, relevant state departments and the State Councils of Educational Research and Training (SCERTs) would ensure that solutions are aligned with ground-level educational needs and policy pathways.Establish multistakeholder governance and access policies 4.3.2 Shaping the AI Sandbox Ecosystem for the Intelligent Age 18