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Incorporating AI incident reporting into telecommunications law and policy: Insights from India

arXiv 2509.09508 ↗First posted 11 Sep 2025cs.CY cs.AI cs.HCcited by 1

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Abstract (arXiv)

The integration of artificial intelligence (AI) into telecommunications infrastructure introduces novel risks, such as algorithmic bias and unpredictable system behavior, that fall outside the scope of traditional cybersecurity and data protection frameworks. This paper introduces a precise definition and a detailed typology of telecommunications AI incidents, establishing them as a distinct category of risk that extends beyond conventional cybersecurity and data protection breaches. It argues for their recognition as a distinct regulatory concern. Using India as a case study for jurisdictions that lack a horizontal AI law, the paper analyzes the country's key digital regulations. The analysis reveals that India's existing legal instruments, including the Telecommunications Act, 2023, the CERT-In Rules, and the Digital Personal Data Protection Act, 2023, focus on cybersecurity and data breaches, creating a significant regulatory gap for AI-specific operational incidents, such as performance degradation and algorithmic bias. The paper also examines structural barriers to disclosure and the limitations of existing AI incident repositories. Based on these findings, the paper proposes targeted policy recommendations centered on integrating AI incident reporting into India's existing telecom governance. Key proposals include mandating reporting for high-risk AI failures, designating an existing government body as a nodal agency to manage incident data, and developing standardized reporting frameworks. These recommendations aim to enhance regulatory clarity and strengthen long-term resilience, offering a pragmatic and replicable blueprint for other nations seeking to govern AI risks within their existing sectoral frameworks.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 6 of its 101 references that are in the map. First posted on arXiv 11 Sep 2025.

Builds on

6 of 101 references.

References · 6

  1. 2020Preventing Repeated Real World AI Failures by Cataloging Incidents: The AI Incident Databasecited by 95
  2. 2021Filling gaps in trustworthy development of AIcited by 14
  3. 2025Standardised schema and taxonomy for AI incident databases in critical digital infrastructurecited by 5
  4. 2025Advancing Trustworthy AI for Sustainable Development: Recommendations for Standardising AI Incident Reportingcited by 4
  5. 2024Business and Regulatory Responses to Artificial Intelligence: Dynamic Regulation, Innovation Ecosystems and the Strategic Management of Disruptive Technologycited by 3
  6. 2025Enhancements for Developing a Comprehensive AI Fairness Assessment Standardcited by 3

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