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TRAI’s AI spam crackdown flags spammers but enforcement faces challenges

The Telecom Regulatory Authority of India (TRAI) has implemented an AI-driven system to detect and flag spam calls and messages, enabling telecom operators…

The Telecom Regulatory Authority of India (TRAI) has implemented an AI-driven system to detect and flag spam calls and messages, enabling telecom operators to share these flags across networks within two hours and disconnect flagged numbers. The system was discussed at a MediaNama roundtable on August 12 in New Delhi, where telecom operators, enterprises, and startups examined its impact on spam prevention.

Under TRAI’s enforcement framework, once AI flags a number as spam, operators must warn the sender and, if the warning fails, disconnect all numbers linked to that sender. This process includes re-verifying the sender’s identity through KYC, which adds operational costs. A participant at the roundtable explained that disconnecting thousands of numbers linked to one spammer creates significant challenges for operators, both financially and logistically.

While AI effectively detects spam, it does not prevent spammers from generating new spam, nor does it fully differentiate between legitimate businesses and spammers. This limitation results in legitimate businesses sometimes being caught in the crackdown. The enforcement chain’s cost and disruption raise concerns about the system’s overall effectiveness in curbing spam, highlighting the complexity of balancing spam prevention with operational feasibility.

The roundtable discussion underscored that AI’s role in spam detection is valuable but insufficient as a standalone solution. Telecom operators must navigate the costly enforcement process, including mass disconnections and KYC re-verifications, to comply with TRAI’s regulations, reflecting ongoing challenges in India’s spam prevention efforts.

Editorial standards. Reported and edited at Startupniti's news desk from the sources listed in the right rail. Every fact traces to a citation. If something looks wrong, write to corrections.
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