Shaping the AI Sandbox Ecosystem for the Intelligent Age 2025

The AI sandbox framework embodies the principle of “responsible innovation by design” as it is structured to support both innovation and regulation. Each layer within the framework plays a distinct role in enabling responsible, scalable AI innovation, as outlined below, starting with infrastructure at the base and working up: Infrastructure: The infrastructure layer forms the technical backbone of the sandbox ecosystem. It ensures that start-ups and researchers can access the secure, affordable compute and development environments needed to build and test AI solutions. At the same time, it embeds safeguards such as secure access protocols and resilience standards to ensure that infrastructure is reliable, protected and ready to scale. The EU Act mandates the provision of all services free to SMEs and start-ups. The Government of India may take an appropriate view on what concessions can be extended to the SMEs and start-ups participating in the AI sandboxes. Data: The data layer ensures access to high-quality, multilingual and AI-ready datasets that reflect India’s diverse contexts. It enables experimentation through well-governed data access, including anonymized, labelled and federated sources. To build trust, this layer also enforces strict data-privacy standards, consent frameworks and compliance with the Digital Personal Data Protection (DPDP) Act16 and emerging data protection regulations. Models: The models layer supports the development of localized, domain-specific AI models that are representative of India’s linguistic and cultural diversity. It enables access to pretrained models and tools for fine-tuning, allowing innovators to build contextually relevant solutions. Guardrails in this layer include evaluation standards for fairness, transparency and robustness to ensure that models are safe and reliable before deployment. A required precaution is the flagging of datasets/ content that are translated from English and originated outside India. This enables the models to avoid compounding the bias inherent in such datasets/content. Innovation: The innovation layer focuses on translating AI capabilities into real-world use cases across sectors such as public safety, public health, healthcare, agriculture, education, finance, environment, climate action, sustainable energy, transportation, and public administration and public services. It enables innovators to pilot and refine solutions in controlled environments with access to curated data for training, validation and testing, and domain expertise for mentoring and early users. In parallel, India can also emerge as a leader in science-led innovation, where AI is accelerating discovery itself. Sandboxes can support this frontier through controlled experimentation in fields such as lab automation, simulation and autonomous scientific analysis. For instance, emerging systems such as Robin – a multi-agent AI platform capable of autonomously designing, executing and analysing scientific experiments – illustrate the kind of deep-tech applications that could benefit from sandbox support. Guardrails ensure that deployed applications meet sectoral standards, minimize risk and uphold public trust through validation and responsible deployment protocols. Governance: The governance layer provides the overarching structure for coordination, accountability and risk management across the sandbox ecosystem. It enables multistakeholder participation, transparent decision-making and alignment with regulatory and ethical standards. Guardrails at this level ensure responsible oversight through structured governance boards, grievance redressal mechanisms and the application of risk management frameworks such as NIST RMF. This layered and modular architecture ensures that AI innovation is pursued in a systemically safe, contextually relevant and future-ready manner. For instance, access to public compute infrastructure must be matched with enforceable security protocols, just as multilingual datasets must be governed by strong privacy and consent mechanisms. By embedding both innovation drivers and ethical boundaries into every layer, the framework positions AI sandboxes not merely as experimental spaces but as trusted national platforms for safe, inclusive and scalable AI adoption. The following caveats are in order: a. Not every instance of an AI sandbox needs to have all five layers, depending on the sector and the implementation model. For instance, for an AI sandbox dealing only in use cases not involving any personal or sensitive data, the governance layer need not be present. b. Likewise, not every layer requires equal emphasis. Each instance of an AI sandbox needs to be designed on a case-to-case basis involving a multidisciplinary team, consisting of domain experts, IT and AI professionals, and security, privacy and legal experts, besides public administrators. Shaping the AI Sandbox Ecosystem for the Intelligent Age 16