A Federated Architecture for Sector-Led AI Governance: Lessons from India
Summary: arXiv:2603.26865v1 Announce Type: cross
Abstract: India has adopted a vertical, sector-led AI governance strategy. While promoting innovation, such a light-touch approach risks policy fragmentation. This paper aims to propose a cohesive “whole-of-government” architecture to mitigate these risks and connect policy goals with a practical implementation plan.
Introduction
The advent of artificial intelligence (AI) technologies has prompted nations to develop governance frameworks that promote innovation while managing associated risks. India’s approach to AI governance is characterized by a vertical, sector-led strategy, which, although encouraging innovation, presents challenges in terms of policy coherence and integration across various sectors.
Purpose of the Study
This study aims to propose a comprehensive governance architecture that aligns national policy goals with practical implementation strategies. By doing so, it seeks to address the potential fragmentation arising from sector-specific regulations and enhance the overall efficacy of AI governance in India.
Methodology
The paper employs a five-layer conceptual framework tailored to the Indian context, consisting of:
- National governance architecture
- Sector-specific governance frameworks
- Operational systems for incident management
- Data management protocols
- Public engagement strategies
Findings
Two actionable architectures have been developed through this research:
- The primary model delineates clear governance roles among key institutions in India, ensuring accountability and delineation of responsibilities.
- The second model offers a federated architecture specifically for national AI Incident Management, addressing data silos through a standardized framework that allows for sector-specific data collection while promoting cross-sectoral collaboration.
Practical Implications
The proposed architectures provide a structured roadmap for policymakers, regulators, and industry stakeholders in India. By clarifying roles and responsibilities, these frameworks aim to accelerate the national AI governance agenda, making it more predictable and actionable.
Social Implications
By establishing a systematic approach from policy formulation to practical execution, the proposed architecture fosters public trust. This structured methodology not only ensures accountability but also aligns AI development with societal values, thereby addressing ethical concerns inherent in AI deployment.
Originality and Value
This paper introduces a detailed operational architecture for India’s “whole-of-government” approach to AI governance. The insights and frameworks presented provide a globally relevant template for other nations adopting a similar sector-led governance model. Additionally, the federated architecture illustrates how common standards can facilitate cross-border data aggregation and global sectoral risk analysis without centralizing control, paving the way for international collaboration in AI governance.