Shaping the AI Sandbox Ecosystem for the Intelligent Age 2025

To effectively support AI experimentation and deployment, sandboxes must be built around core components that address real-world constraints faced by Indian start-ups. These components fall under three main categories: 1. Data and infrastructure enablers –Provide AI-ready localized and multilingual datasets through secure data pipelines and data-sharing protocols, including federated systems or data clean rooms, enabling trusted, multi-tenant collaboration. –Support vernacular speech datasets and automatic speech recognition (ASR) models in vernacular languages to enable inclusive, voice- first AI agents for rural and semi-urban contexts. Ensure data governance and discipline aligned with India’s emerging data economy principles. –Offer affordable access to compute infrastructure, including shared access to GPU clusters and subsidized infrastructure models via public–private partnerships. Enable compute access to also support hybrid cloud and edge environments, enabling scalable testing of agentic and physical AI solutions in decentralized or bandwidth-constrained settings. –Provide access to foundational or pretrained AI/machine learning (ML) models, including vernacular models developed through platforms such as Bhashini or the recently released Sarvam’s model. –Offer preconfigured integrated development environments for streamlined coding, model development and testing. 2. Trust and validation enablers –Embed compliance and evaluation frameworks, such as the NIST RMF, adapted to Indian sectoral and regional requirements for fairness, safety and explicability. –Enable certification or precertification support aligned to sectoral regulatory standards to facilitate deployment, such as the Insurance Regulation and Development Authority of India (IRDAI)20 for insurance or the Reserve Bank of India (RBI)21 for banking and finance. –Provide automated tools for benchmarking, model validation and performance testing to ensure fairness, robustness and safety before full-scale deployment. All sandbox participants may be encouraged to publish reports on bias mitigation, citizen protection and safety frameworks. Differential privacy techniques, where appropriate, should also be considered in sandbox data-handling protocols. 3. Ecosystem enablers –Offer incentive programmes such as milestone- based funding, innovation grants and pilot challenges to incentivize high-impact solutions. –Create structured mentorship networks involving policy-makers, tech original equipment manufacturers (OEMs), domain experts, industry stakeholders, start-ups, researchers, technical experts and adopters to support solution readiness and scaling. A pool of experts comprising industry professionals and academics may be curated to guide participants throughout the sandbox life cycle and aid in transitioning viable projects to scale. –Facilitate market access through integration with public platforms (such as AI-based Sewa agents for citizen services in vernacular languages) and establish clear commercialization pathways linking validated solutions to government procurement, market integrations or seed-stage investor showcases. Example: A sandbox hosted in collaboration with IRDAI could enable start-ups to test agentic AI for insurance underwriting, evaluate bias and performance, and enable emerging models such as AI-based risk insurance.22Design and develop core AI sandbox components 4.3.3 AI sandboxes should be implemented in an agile, phased manner – starting small, learning from early experiments and scaling up with robust measurement and feedback loops. This approach helps mitigate risk, optimize resources and enable more targeted interventions. The main steps for operational execution are as follows: –Begin with a minimal viable pilot – focused either on one sector or on cross-sector use cases where dependencies exist (e.g. open-source finance in BFSI) – or a defined geographical region, to test feasibility, stakeholder engagement and operational workflows.Execute in phases, scale and monitor outcomes 4.3.4 Shaping the AI Sandbox Ecosystem for the Intelligent Age 19