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

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.

💡 Research Summary

The paper addresses a critical gap that emerges as artificial intelligence (AI) becomes embedded in telecommunications infrastructure. While traditional cybersecurity and data‑protection regimes focus on network intrusions, malware, and personal‑data breaches, they do not cover the novel risks introduced by AI‑driven services—namely algorithmic bias, unpredictable autonomous decision‑making, and performance degradation caused by model errors or flawed training data. To fill this gap, the authors propose a precise definition and a typology of “telecommunications AI incidents,” distinguishing four primary categories: (1) service performance loss or outage caused by AI components, (2) algorithmic bias or unfair outcomes, (3) unpredictable autonomous decisions that affect users or network operations, and (4) security vulnerabilities that arise specifically during model training, deployment, or inference. By treating these as a distinct class of risk, the paper argues that they merit dedicated regulatory attention separate from conventional cyber‑incident frameworks.

Using India as a case study—representative of jurisdictions that lack a comprehensive horizontal AI law—the authors conduct a detailed analysis of the country’s recent digital statutes: the Telecommunications Act, 2023; the CERT‑In Rules; and the Digital Personal Data Protection Act, 2023. The Telecommunications Act mandates network security and service continuity but does not address AI model integrity or algorithmic fairness. The CERT‑In Rules prescribe incident‑response procedures and a mandatory reporting timeline for cyber‑incidents, yet they explicitly exclude AI‑specific failures. The Data Protection Act focuses on personal‑data handling and breach notification, leaving AI‑generated or AI‑derived data largely unregulated. Consequently, high‑risk AI failures—such as biased routing decisions, automated throttling, or model‑driven service disruptions—fall through the regulatory cracks, exposing both operators and consumers to unmitigated harm.

The paper further highlights structural impediments to effective AI‑incident disclosure. India lacks a centralized AI‑incident repository, and existing international databases (e.g., the AI Incident Database) suffer from language barriers, jurisdictional inconsistencies, and limited adoption by Indian firms. Without a legal duty to report, many operators keep AI‑incident logs internally but do not share them publicly, hampering transparency, academic research, and policy formulation.

To bridge these gaps, the authors propose a set of targeted policy measures:

1. Mandatory Reporting for High‑Risk AI Failures – Define “high‑risk AI” (e.g., systems affecting large user bases, critical infrastructure, or those with a high potential for discriminatory outcomes) and require operators to report such incidents within 24 hours of detection.
2. Designation of a Nodal Agency – Expand the mandate of an existing body—such as the Telecom Regulatory Authority of India (TRAI) or CERT‑In—to include an AI‑incident unit responsible for collection, analysis, and public dissemination of AI‑incident data.
3. Standardized Reporting Framework – Develop a template that captures incident type, impact scope, root‑cause analysis, remediation steps, and preventive actions, aligned with emerging international standards (e.g., ISO/IEC 42001 for AI risk management).
4. Incentives and Sanctions – Offer tax incentives or regulatory relief for timely, accurate reporting while imposing fines for delayed, incomplete, or false disclosures, thereby creating a “carrot‑and‑stick” environment.
5. Data Sharing and Anonymization Protocols – Implement mechanisms that allow aggregated incident data to be shared with academia and industry while preserving confidentiality and privacy of affected entities.

The anticipated outcomes include clearer regulatory expectations for telecom operators, enabling them to budget for AI‑risk management proactively; a growing corpus of incident data that can be mined for patterns, leading to pre‑emptive safeguards and more robust AI models; and a replicable blueprint for other jurisdictions facing similar regulatory vacuums. By integrating AI‑incident reporting into existing sectoral frameworks, the approach promises to enhance long‑term resilience of telecommunications networks without requiring a brand‑new, overarching AI law.

The authors acknowledge limitations and outline avenues for future research. Defining “high‑risk AI” objectively will require quantitative metrics and sector‑specific thresholds (e.g., for 5G, IoT, cloud‑based AI services). International cooperation is needed to harmonize incident‑sharing protocols and to develop cross‑border standards for anonymized reporting. Moreover, the paper calls for empirical studies on the effectiveness of the proposed incentives, the impact of reporting latency on mitigation outcomes, and the development of automated tools for AI‑incident detection and reporting.

In sum, the paper makes three core contributions: (1) a novel taxonomy that isolates AI‑specific incidents within telecommunications; (2) a gap analysis of India’s current digital legislation, exposing the regulatory blind spots for AI‑driven risks; and (3) a concrete, implementable policy package that embeds AI‑incident reporting into existing telecom governance structures. This work not only advances scholarly understanding of AI risk in critical infrastructure but also offers a pragmatic pathway for policymakers worldwide to safeguard the next generation of AI‑enabled communication services.