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The Invisible Glitch: Why the Global Future of Connectivity Depends on a New Playbook for AI

wakalatnama.co.in

Mr. Anant Hooda

Research Scholar

Chaudhary Devi Lal University (CDLU)

Artificial Intelligence is no longer just a futuristic concept in the telecommunications industry; it has become the vital engine operating under the hood. From managing massive 5G network traffic and adaptive spectrum reuse to predictive maintenance that prevents outages before they even occur, AI has brought transformative efficiency, scalability, and automation to how we connect. However, as we lean more heavily on these autonomous systems, we face a new breed of “invisible” risks—failures that don’t look like traditional hacks or data leaks but can be just as damaging to the infrastructure we rely on. To ensure our digital infrastructure remains resilient, the world needs a dedicated framework for identifying, documenting, and reporting these specific AI incidents before they compromise the stability and trust of our global networks.

Defining the “AI Incident”: A New Category of Risk

Digital threats are typically viewed through two well-established lenses: Cyberattacks (external bad actors breaking in) or Data Breaches (private personal information being leaked). But AI introduces a third, more subtle category: the functional failure. Unlike traditional cybersecurity incidents, these AI-specific failures present unique challenges because they often arise from the system’s own internal logic, training data, and autonomous learning rather than an external malicious attack.

A Telecommunications AI Incident is defined as any event, circumstance, or malfunction involving AI systems in telecom networks that leads to service disruption, the introduction of bias, or harm to individuals and the environment. These risks are born from the very nature of how AI operates—through scale, opacity, and autonomy. Because AI operates at a velocity that can amplify a minor flaw into a massive, cascading failure far faster than human-monitored systems, we must categorize these risks separately. Unlike traditional cybersecurity or data protection incidents, these AI-specific failures present unique challenges for which broader regulations and use cases are yet to mature.

Real-World Scenarios of AI Failure

• Spatial Bias in 5G: An AI model managing “beamforming” (the process of directing signals to users) might be trained on data collected unevenly from its operational area. If the training data primarily covers users along a specific path, the system may systematically provide suboptimal service to users in other areas. This is a flaw that would be invisible to standard operator testing procedures.
• Model Drift: AI systems learn and adapt autonomously. Over time, an AI’s performance can “drift,” where its decision-making logic degrades due to changing real-world demand patterns. This unintended model behavior erodes service quality gradually, making it much harder to detect than a sudden, discrete network outage.
• Algorithmic Discrimination: AI models trained on skewed historical investment data may learn to operate networks that “work well” only in specific regions. This can lead to a lower quality of service for less represented, often lower-income or rural user groups, effectively automating and amplifying existing inequities.
• Operational Inefficiency: AI-driven traffic management systems could misinterpret regional demand trends. This can result in under-provisioning in high-traffic areas or prioritizing low-value traffic over critical services, jeopardizing the resilience of telecommunications infrastructure.
• The Black Box Problem: The complexity of many models makes root cause analysis exceptionally difficult. Unlike a traditional software bug, it may be impossible to determine precisely why an AI made a specific biased or incorrect decision.

The Regulatory Gap: Why Current Laws Fall Short

Many nations currently lack a “horizontal” AI law—a single set of rules governing AI across every sector of the economy. Instead, they rely on digital governance pillars designed for a pre-AI era. Using India as a prime archetype, we can see how even the most advanced telecom markets operate with significant regulatory blind spots. The country is undergoing one of the world’s fastest 5G rollouts, meaning advanced AI-driven network management systems are being deployed at an unprecedented pace, yet the laws remain focused on traditional threats.

• The Cybersecurity Focus: Rules like India’s CERT-In Rules or the Telecommunications (Telecom Cyber Security) Rules, 2024, define “incidents” through the lens of security policy violations, unauthorized access, or the impairment of availability. An AI system that is technically “up” but is making biased, unfair, or inefficient decisions does not technically violate these security policies.
• The Data Protection Lens: Legislation like the Digital Personal Data Protection (DPDP) Act, 2023, is triggered only when personal data is compromised. If an AI algorithm provides slower speeds to a certain demographic or mismanages network restoration tasks, it falls entirely outside the purview of data protection laws because no personal data was “breached” in the legal sense.
• The Telecom Framework: The recently enacted Telecommunications Act, 2023 represents a significant modernization of governance, but it does not define what constitutes a reportable incident regarding AI, nor does it explicitly address how algorithmic failures unrelated to security could affect network services.
• Reactive vs. Systemic Learning: Most existing frameworks are reactive, focusing on immediate response and mitigation of a specific threat. They lack mechanisms to systematically document and analyze AI failures to improve future model design. Unlike aviation or fire safety, AI systems often lack a structured approach to learning from past failures.

The Barriers to Transparency

If AI is failing, why wouldn’t a company simply report it? There are significant systemic, technical, and psychological barriers preventing organizations from being open about these glitches.

1. Technical Challenges: The “black box” nature of complex models makes the technical challenge of root cause analysis exceptionally difficult. Determining accuracy and validating quality in a timely manner is a hurdle for even the most well-resourced operators.
2. Reputation and Trust: In a hyper-competitive market, disclosing an “AI failure” can lead to a loss of client confidence and brand damage. Given the intense public scrutiny and hype surrounding AI, the concern over being perceived as incompetent is often heightened.
3. Legal and Policy Barriers: Companies fear that sharing data about their failures will expose them to unintended legal liability or regulatory reprisals. There is also a fear of violating antitrust laws when sharing incident data with competitors.
4. Operational Constraints: Smaller organizations often lack the trained personnel with expertise in AI incident assessment. Internal resistance to change and low motivation among personnel to actively participate in reporting can stifle a transparency-based culture.

A Blueprint for Resilience: Key Recommendations

To bridge these gaps, we must move away from general-purpose AI databases—which often focus on social media controversies—and integrate AI incident management directly into the technical frameworks of the telecom sector. This approach is more effective and pragmatic for countries where overarching AI governance is absent.

1. Mandating High-Risk Reporting

The scope of telecommunications law should be expanded to mandate reporting for high-risk AI incidents. This expansion must begin with the formal adoption of a broader definition of a reportable “incident,” so that the full spectrum of AI-specific risks is brought under regulatory oversight. This would leave no ambiguity for operators regarding their reporting obligations.

2. Designating a Nodal Agency

Instead of a new government department, countries should empower existing technical bodies with sectoral expertise to act as a “nodal agency.” In the Indian context, this could be the Telecommunication Engineering Centre (TEC) or the Telecom Regulatory Authority of India (TRAI). This agency would maintain a secure, anonymized repository of AI failures, issue guidelines, and ensure compliance with reporting protocols.

3. Implementing Tiered Risk Assessments

Not all AI is equal. We need a tiered approach that categorizes AI applications as low, limited, high, or unacceptable risk. This approach aligns with recommendations to move toward proportionate oversight. Service providers should be required to conduct periodic risk grading to identify vulnerabilities, potential failures, and unintended consequences before deployment.

4. Incentivizing “Good-Faith” Reporting

To build transparency, regulators should offer “safe harbor” protections. If a company reports an incident in good faith to improve industry standards, they should receive certain liability protections. Regulatory incentives may include compliance benefits for proactive risk management and access to anonymized industry-wide insights for improved risk mitigation.

5. Standardizing Taxonomy and Process

Currently, “failure” means different things to different engineers. A standardized taxonomy is needed to classify AI incidents by root cause, severity, and network function. This allows for meaningful trend analysis and ensures that data can be aggregated and compared across platforms and jurisdictions.

6. Modernizing Equipment Certification

Telecom gear is already tested for hardware safety and electromagnetic compatibility. This “conformity assessment” should be expanded to include AI-specific criteria. We need to stress-test parameters like fairness, robustness, and security risks of AI-powered network components before they are ever deployed in a live network.

Conclusion: Building Trust in the Machine

The goal of these measures isn’t to slow down innovation with red tape. On the contrary, by creating a clear, structured way to report and learn from AI failures, we build a more resilient foundation for the digital economy. Resilience is the ability to anticipate, withstand, and recover from disruptions. In an AI-driven world, this requires proactive data collection and systemic learning rather than just reactive firefighting.

As we move toward a future defined by 5G and eventually 6G, the networks underpinning our society must be more than just fast—they must be accountable and trustworthy. By acknowledging and addressing the unique risks of AI today, we can ensure that the connectivity of tomorrow is stable and fair for everyone. This strategy serves as a replicable blueprint for any nation governed by domain-specific laws seeking to manage the risks of AI without a comprehensive, overarching legal framework.

 
 

Citations and Full References

The information provided in this blog is based on the research and analysis presented in the following sources:

• Agarwal, A., & Nene, M. J. (2026). Incorporating AI incident reporting into telecommunications law and policy: Insights from India. Computer Law & Security Review, 60, 106263.
• Scope of Risks: AI introduces novel risks such as algorithmic bias, unpredictable behavior, and model drift that fall outside traditional cybersecurity and data protection frameworks.
• Example Case Study: AI in 5G Radio Access Network (RAN) management can introduce “spatial bias” if models are trained on uneven data, causing service degradation in specific areas.
• India’s Regulatory Framework: Current laws such as the Telecommunications Act (2023), the CERT-In Rules (2013), and the Digital Personal Data Protection Act (2023) focus narrowly on security breaches and personal data protection.
• Regulatory Gaps: Four critical gaps identified include the limited scope of telecom laws, narrow coverage of broader laws, misalignment with AI technological realities, and a focus on response over systemic learning.
• Reporting Barriers: Key hurdles include operational/resource constraints, technical “black box” challenges, trust and reputation concerns, and legal/policy uncertainties.
• Database Limitations: Existing repositories like the AI Incident Database (AIID) and AIAAIC lack sectoral granularity and rely on voluntary reporting, leading to significant underreporting in critical infrastructure.
• Policy Recommendations: Proposals include integrating AI reporting into telecom regulations, designating a nodal agency (like TEC or TRAI), mandating risk assessments, and standardizing taxonomies.
• Global Context: India serves as an archetype for non-EU jurisdictions (like Australia, Nigeria, and Singapore) seeking to manage AI risks within existing sectoral frameworks.

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