India Proposes Techno-Legal AI Governance Framework
India’s Office of the Principal Scientific Adviser (PSA) has released a white paper outlining a comprehensive approach to govern artificial intelligence (AI) through a “techno-legal” framework. Rather than establishing a standalone or omnibus AI law, the framework emphasizes that governance should operate within AI systems themselves, utilizing technical controls throughout the system’s lifecycle and anchored in existing legal and regulatory instruments.
Law-Plus Model
The paper introduces a “law-plus” model that incorporates sector-specific rules, subordinate legislation, regulatory guidance, standards, and technical enforcement mechanisms. This approach aims to fill gaps in AI governance while maintaining alignment with existing laws. This model distinguishes India from jurisdictions pursuing comprehensive AI Acts, reframing AI governance as a responsibility that begins at the design and deployment stages, rather than as a post hoc compliance exercise.
Rationale for the Framework
In the foreword, Principal Scientific Adviser Ajay K. Sood highlights the significant transformative potential of AI, while also cautioning that unmanaged risks could undermine trust and slow adoption. The white paper positions India’s approach within a global context of governments experimenting with risk-based regulation and principles-led frameworks.
Building upon the India AI Governance Guidelines, the framework outlines a pro-innovation approach that integrates baseline legal safeguards, sectoral regulation, technical measures, and institutional mechanisms to foster “safe and trusted AI.” Importantly, it prioritizes proportionality and flexibility, rejecting uniform obligations across all AI systems in favor of context-sensitive regulation.
Defining Techno-Legal AI Governance
The core of the white paper centers around a shift in conceptualizing governance. The techno-legal approach integrates legal instruments, regulatory oversight, and technical enforcement mechanisms directly into AI systems by design. This governance model functions as an intrinsic feature of AI systems capable of responding to evolving risks, rather than an external constraint imposed post-deployment.
Key characteristics of this approach include transparency, explainability, and provability, all aligned with India’s legal, technical, and ethical norms. The framework employs a layered structure that ranges from laws to rules, regulatory guidance, standards, and technical tools, fostering distributed governance rather than centralized control.
Lifecycle-Based Governance
The paper asserts that AI risks accrue across the lifecycle of an AI system, emphasizing that harms tied to data use and model training become difficult to rectify once systems are deployed. It distinguishes between “AI users”, who interact directly with AI systems, and “AI subjects”, who may experience the impacts of AI decisions without awareness or consent.
The governance framework is organized around five stages of the AI lifecycle:
- Data Collection
This initial stage identifies risks related to privacy, safety, intellectual property, and fairness. The paper proposes data governance frameworks, risk assessments, and consent verification to mitigate these risks.
- Data-in-Use Protection
During model training, risks such as unauthorized access and security breaches are particularly sensitive. The framework advocates for privacy-enhancing technologies and audit controls to safeguard data during this crucial phase.
- AI Training and Model Assessment
At this stage, the framework emphasizes the importance of impact-aware decision-making and stakeholder consultation, proposing controls such as model risk assessments and transparency mechanisms.
- Safe AI Inference
Governance does not cease post-deployment; the inference stage poses risks including unintended disclosures and fairness concerns. Proposed measures include runtime monitoring and adversarial attack detection to manage these risks effectively.
- Trusted and Agentic AI Systems
As AI systems gain autonomy, the need for enhanced governance increases. The framework suggests controls such as continuous monitoring and behavior logging to ensure accountability.
Operationalizing the Framework
The white paper proposes a multi-layered institutional architecture centered around an AI Governance Group, which will coordinate across ministries and regulators. It also recommends establishing a Technology and Policy Expert Committee and an AI Safety Institute to facilitate evaluation and testing of AI systems.
Additionally, a national AI incident database is proposed to allow stakeholders to report AI-related failures, fostering institutional learning and informing future standards.
Digital Public Infrastructure as a Governance Enabler
The framework leverages India’s Digital Public Infrastructure (DPI) as a governance tool, enabling consent-based access and identity verification while embedding techno-legal controls into workflows.
Limitations of the Framework
The white paper does not clarify enforcement boundaries in cases of AI failures, leaving open questions about accountability for AI subjects. It also lacks definitions for enforcement thresholds and assumes substantial technical capacity for compliance, raising concerns about feasibility for smaller entities.
Overall, while the techno-legal framework does not resolve all challenges, it presents an incremental approach to embed safeguards into AI systems while maintaining regulatory flexibility as AI adoption progresses.
