AI Automation in Telecom_ Ensuring Accountability and Public Trust India AI Impact Summit 2026

20 Feb 2026 10:00h - 11:00h

Session at a glance

Summary

This discussion focused on “Building Customer Trust Through AI-Driven Operations” in the telecommunications sector, examining how to balance innovation with privacy and regulatory compliance. The panel brought together experts from telecom service providers, R&D organizations, standards bodies, and international associations to address the challenges and opportunities of implementing AI in telecom operations.

Dr. Rajkumar Upadhyay from CDOT presented several successful AI applications developed in India, including Fraud Pro, which detected fraudulent SIM connections and led to the disconnection of 7 crore mobile numbers. He highlighted the Financial Risk Indicator system that helps banks assess transaction risks, the Chakshu crowdsourcing platform for reporting fraudulent calls, and AI-driven disaster management systems that have reduced cyclone-related deaths to zero in states like Odisha. These applications demonstrate how AI can protect customers while operating at India’s massive scale.

Julian Gorman from GSMA emphasized that combating scams requires cross-sector collaboration, noting that scammers operate faster than regulations can adapt. He outlined four pillars for fighting scams: securing networks against manipulation, exposing data through APIs for ecosystem risk assessment, providing protective services to customers, and continuous digital skills development. Gorman stressed the need for global cooperation and regulatory sandboxes to enable innovation in data sharing while maintaining privacy.

Mathan Babu Kasilingam from the service provider perspective discussed the evolution from quick AI wins to consolidated platforms, addressing challenges of siloed data and infrastructure costs. Syed Tausif Abbas from TEC introduced a world-first voluntary standard for AI incident reporting in telecom networks, providing a structured framework for documenting and learning from AI-related incidents. The discussion concluded with calls for enhanced global collaboration among regulators and industry players to effectively combat cross-border fraud and scams.

Keypoints

Major Discussion Points:

– AI-Driven Fraud and Scam Prevention: Extensive discussion on using AI to combat telecommunications fraud, including disconnecting millions of fraudulent connections, detecting SIM card factories, and implementing systems like Fraud Pro, Financial Risk Indicator, and Chakshu for real-time fraud detection and prevention.

– Cross-Border Collaboration and Global Standards: Emphasis on the need for international cooperation to combat scams effectively, as fraudsters operate across borders without regulatory constraints. Discussion of GSMA’s Cross-Sector Anti-Scam Task Force involving 39 organizations from 17 countries and the importance of data sharing through standardized interfaces.

– AI Infrastructure Consolidation and Enterprise Adoption: Challenges with siloed AI implementations in enterprises, leading to duplicated data repositories and infrastructure. The evolution from quick-win, piecemeal AI deployments toward consolidated platforms with centralized data and comprehensive LLMs.

– AI Incident Reporting Standards: Introduction of world’s first AI incident reporting standard by TEC (Telecom Engineering Centre), providing a voluntary framework for telecom service providers to report and analyze AI-related incidents to improve system reliability and inform policy decisions.

– Customer Trust and Privacy in AI Operations: Discussion of balancing AI innovation with privacy protection, implementing privacy-by-design principles, and ensuring human oversight in automated systems to maintain customer trust while leveraging AI for network optimization and service delivery.

Overall Purpose:

The discussion aimed to explore how telecommunications companies can build and maintain customer trust while implementing AI-driven operations, focusing on practical applications, regulatory frameworks, and collaborative approaches to address challenges like fraud prevention, network security, and service quality.

Overall Tone:

The discussion maintained a professional and collaborative tone throughout, with industry experts sharing practical experiences and solutions. The tone was optimistic about AI’s potential while acknowledging serious challenges, particularly around fraud and security. There was a strong emphasis on cooperation between industry players, regulators, and international bodies, with speakers demonstrating mutual respect and building upon each other’s insights rather than presenting conflicting viewpoints.

Speakers

Speakers from the provided list:

– Moderator: Event moderator facilitating the discussion and managing the session flow

– Dr. M P Tangirala: Chairman and session moderator for the panel on “Building Customer Trust Through AI-Driven Operations”

– Mr. Julian Gorman: Representative from GSMA, expert in telecom industry collaboration and anti-scam initiatives across Asia Pacific

– Dr. Rajkumar Upadhyay: CEO of CDOT (Centre for Development of Telematics), expert in AI applications for telecom, fraud detection, cybersecurity, and disaster management systems

– Mathan Babu Kasilingam: Representative from telecom service provider, expert in AI adoption, privacy compliance (PIMS ISO 27701 certified), and enterprise AI implementation

– Syed Tausif Abbas: Senior DDG and Head TEC, also holding additional charge CMDTCIL, expert in telecom standards, certifications, spectrum management, network regulation, and AI incident reporting standards

– Anil Kumar Jha: Principal Advisor, TRAI (Telecom Regulatory Authority of India)

Additional speakers:

– Mr. S.T. Abbas: Senior DDG and Head TEC, also holding additional charge CMDTCIL, with over 35 years of experience in telecom standards, certifications, spectrum management and network regulation (Note: This appears to be the same person as Syed Tausif Abbas, likely a name variation)

– Mr. Shantigram Jagannath: Mentioned as having spoken about AI through telecom networks in a previous session

– Mr. Lahoti: Mentioned in acknowledgments, role/title not specified

– Mr. Mittal: Mentioned in acknowledgments, role/title not specified

Full session report

This comprehensive panel discussion on “Building Customer Trust Through AI-Driven Operations” brought together leading experts from across the telecommunications ecosystem to examine the critical balance between innovation and trust in AI implementation. The session, moderated by Dr. M P Tangirala, featured perspectives from service providers, research and development organisations, standards bodies, and international associations.

Opening Context and Fraud Prevention Scale

Dr. Tangirala opened the session by highlighting the massive scale of AI-driven fraud prevention efforts, noting that 2.1 million numbers have been disconnected using AI-based tracking systems. The discussion immediately focused on the telecommunications industry’s role as national critical infrastructure and the unprecedented challenges posed by modern fraud operations.

The Global Scam Challenge

Julian Gorman of GSMA articulated the fundamental asymmetry facing the telecommunications industry: “In the scam economy, regulation cannot move as fast as scammers. Scammers are not bound by geography. They’re not bound by laws. They’re very technically capable and they’re very well funded.” This observation highlighted how criminal networks possess agility and resources that traditional regulatory frameworks struggle to match.

Gorman outlined GSMA’s response through the Cross-Sector Anti-Scam Task Force, which has brought together more than 39 organisations from 17 countries, including major platforms like Meta, Google, TikTok, and AWS. This collaborative approach has documented over 40 successful anti-scam strategies implemented by operators across Asia Pacific without regulatory mandates.

The scale of international fraud is staggering, with Gorman noting that India receives approximately 15 million calls per day from outside the country using spoofed Indian numbers. This cross-border nature of fraud necessitates international cooperation and standardised approaches to combat criminal networks effectively.

India’s AI-Driven Success Stories

Dr. Rajkumar Upadhyay from CDOT presented concrete evidence of India’s achievements in AI-driven fraud prevention and disaster management. The Fraud Pro system, using sophisticated image recognition and demographic matching algorithms, has led to the disconnection of 86.7 crore mobile numbers by identifying fraudulent connections created using the same identity documents. This system proved instrumental in dismantling SIM card factories in notorious fraud centres like Jamtara and Mewat.

The Financial Risk Indicator (FRI) system, mandated by the Reserve Bank of India for banking institutions, evaluates the risk profile of money transfer recipients in real-time, preventing transactions to high-risk numbers. The Sanchar Sati application has achieved remarkable adoption with 18 million downloads and 25 crore website hits, enabling customers to identify all connections associated with their identity using fuzzy logic algorithms.

Dr. Upadhyay also presented India’s AI-driven disaster management system, which federates inputs from multiple agencies including the India Meteorological Department, Central Water Commission, and Forest Research Institute. The system’s effectiveness is demonstrated by the dramatic reduction in cyclone-related casualties – Odisha, which experienced thousands of deaths during the 1999 cyclone, has achieved zero casualties in recent cyclones due to AI-driven early warning systems.

The integration of cell broadcast technology proved particularly valuable during Cyclone Montha, enabling instant alerts to everyone in affected geographic areas. Dr. Upadhyay noted that this system was “presented in parliament” and aligns with UN requirements for early warning systems by 2027.

Enterprise AI Evolution and Consolidation

Mathan Babu Kasilingam from the service provider perspective described the evolution of enterprise AI adoption. He observed that organisations initially pursue AI implementations for quick wins but often create siloed data repositories and duplicated infrastructure. The economic reality is significant, with 80-90% of AI costs stemming from infrastructure requirements, particularly storage and compute resources.

This has driven enterprises toward consolidated platforms with centralised data repositories and comprehensive Large Language Models that can serve multiple business functions. Kasilingam’s organisation has embraced privacy-by-design principles, achieving PIMS ISO 27701 certification and becoming “the only TSP in the country” certified for privacy by design.

AI Incident Reporting Standards

Syed Tausif Abbas from the Telecom Engineering Centre presented what Dr. Tangirala described as “arguably the world’s first AI incident reporting standard” for telecommunications networks. Abbas clarified that this voluntary framework “is not mandatory” and “will not give any mitigation mechanism” but provides a structured approach for documenting AI-related incidents.

The standard includes 30 key fields covering application details, technology used, impact assessment, and cause analysis. The taxonomy classifies incidents across multiple dimensions including incident type, affected systems, and severity levels. Abbas drew parallels with the evolution of Computer Emergency Response Teams, noting that incident reporting mechanisms become crucial as AI deployment accelerates.

Customer Trust and Human Oversight

Throughout the discussion, speakers emphasised the fundamental importance of maintaining customer trust in AI-driven operations. Dr. Tangirala noted that while customers may not interact directly with AI models, they are significantly affected by AI-driven decisions in outage management, service continuity, and grievance handling.

The concept of “human in the loop” emerged as a critical principle for maintaining customer trust, ensuring that automated systems retain elements of human oversight to prevent autonomous decision-making without appropriate control.

Cross-Border Collaboration Challenges

A recurring theme was the necessity of international cooperation in combating fraud while navigating privacy and regulatory constraints. Gorman emphasised that effective scam prevention requires sharing information across multiple parties through standardised interfaces, but noted this operates “at the borders of regulatory compliance” when dealing with personal information.

GSMA’s proof of concept work in Southeast Asia aims to demonstrate that data can be shared both domestically and across borders in a safe and secure manner while having measurable impact on scam prevention.

Global Leadership and Knowledge Sharing

The discussion highlighted India’s emerging role as a global telecommunications leader. Dr. Upadhyay’s willingness to share India’s disaster management technology with other countries demonstrates how domestic innovation can contribute to global resilience. Gorman noted that India, as a rising telecom superpower, cannot exist in isolation and must embrace a statesman role in global cybersecurity efforts.

Interactive Discussion and Future Directions

During the Q&A session, Mr. Jha raised questions about regulatory frameworks and industry collaboration. The discussion revealed ongoing challenges including the tension between data sharing requirements for fraud prevention and privacy protection, the skills gap in AI expertise, and cost optimisation concerns for smaller operators.

Conclusion

Dr. Tangirala concluded the session by emphasising the need for enhanced collaboration among regulators across different sectors. Gorman’s final remarks highlighted the importance of global cooperation through initiatives like the “United Against Scams” program, reinforcing that the challenges of AI governance in telecommunications transcend national boundaries.

The discussion demonstrated a telecommunications industry actively embracing AI while carefully managing implementation challenges. The success stories presented, from fraud prevention to disaster management, provide concrete evidence that AI can deliver significant benefits when implemented with appropriate oversight and international cooperation. The emphasis on voluntary standards, cross-sector partnerships, and knowledge sharing suggests a mature approach to AI governance that prioritises customer trust while enabling innovation.

Session transcript

Moderator

Technology Security and Data Privacy Officer at Vodafone India Limited, with over 20 years of experience in cyber security domain and governance structure. Rounding off the panel, we welcome Mr. S.T. Abbas, Senior DDG and Head TEC, also holding additional charge CMDTCIL, with over 35 years of experience in telecom standards, certifications, spectrum management and network regulation. I would request all the panelists to please come forward for a quick photograph Thank you, sirs. Please take your seats. Let’s engage deeply on how to balance information. Innovation with privacy and trust. I now hand over to Dr. Tangirala Ji to begin the session. Thank you.

Dr. M P Tangirala

Chairman, member, Mr. Mitter, distinguished delegates, my fellow panelists, I welcome everyone to this second session. The clock is already ticking, so I will be brief in my opening remarks because I come between the audience and the distinguished panelists, which I don’t intend to do. The session title is Building Customer Trust Through AI -Driven Operations. The importance of trust was highlighted, among others, by Mr. Shantigram Jagannath as well, when he was speaking about AI through telecom networks and the at -scale problems that we could try and solve. Thank you. Now, while customers may not interact with AI models directly, they are affected by the outcomes of the decisions. And therefore, you know, whether it’s outage management, service continuity, grievance handling, you know, while efficiencies may improve, the responsibility for decision integrity ultimately remains with the telecom service providers.

And clear and proactive communication with the customers would become very important. And that is where, you know, there are impactful applications of AI in telecoms, in spam and fraud prevention, which a person had mentioned in his opening remarks about how 2 .1 million numbers were disconnected using AI -based tracking. But the challenge is also that we need to reduce this spam. while minimizing false positives, avoiding customer inconvenience, and fully respecting privacy and regulatory requirements. So that is always a big concern. Then, of course, this whole issue of the human in the loop or human in the mix. We need this automation to have an element of human control that is so that the system does not run away with its own decisions.

So we have, for all these issues and more, we have eminent speakers here, both from the service providers, from the R &D, and as well as from the standard -setting body of DOT. I will request each of them to give their thoughts, and then maybe a few of you… Both of them have presentations to make. I’ll request them to keep it to about five minutes or so, so that we have time for further discussion. Thank you.

Mr. Julian Gorman

And the reason for it is in the scam economy, regulation cannot move as fast as scammers. Scammers are not bound by geography. They’re not bound by laws. They’re very technically capable and they’re very well funded. They have all the things that mobile operators would like to have. I think it’s important to understand that we have to focus on stimulating innovation. At GSMA, about 12 months ago, we formed a coalition called Cross -Sector Any Scam Task Force. It involves more than 39 organisations from 17 countries, including the social media platforms, so Meta, Google, TikTok, AWS. And the aim was to drive or identify and prioritise initiatives and activities that we could do as an industry to help combat.

Now, one of those activities was let’s gather what the industry is doing. Now, in just the last couple of months, across Asia Pacific, we’ve gathered case studies of more than 40 instances where operators without regulation have developed, implemented, and used successfully some sort of strategy or service to combat scam. And I think that’s an indication, along with GSMA’s globally working with people like Virginia Tech and with our foundry, with our proof of concept around data sharing, is the industry is focused on this. And the danger, of course, of implementing service -based rules is they restrict innovation in the future. And so we really need to focus on outcomes when it comes to regulation. And I think we all universally subscribe to the fact we need to combat scam.

We need to work together. And it’s not just the people in this room. We need to collaborate and work across the ecosystem. to make that possible. I think those principles actually also apply in the broader sort of sense of the term is how do we grow 5G, how do we make 5G meaningful to the whole economy, to all users. It’s about stimulating that ecosystem and making sure that they are using 5G and 4G and mobile broadband into meaningful solutions for the population. And the important thing also for India is India is rising not just economically but also in its position in the telecom world and the GSMA sort of global ecosystem is India is a real telecom superpower and it’s on the rise.

And that means actually it cannot just be worried about its domestic situation. actually it has to embrace that statesman role to be a global leader. And so actually considering cross -border, how does India play its role in a global ecosystem are critical to actually the sustainability and growth of the global ecosystem of which India’s vision is dependent on. It cannot exist alone. And I think it’s important that when we focus on innovation and solving things like scam, it is as part of a global community. It’s not just a national community. And so the actions we take, the innovations we look to stimulate have to be part of that global solution. Thank you.

Dr. M P Tangirala

That was thought -provoking, some of the things that you said about collaborative innovation or innovation through collaboration. We will come to that in a bit when we go for the questions. So, with that now may I request Dr. Rajkumar Upadhyay, CEO of CDOT for his presentation and opening remarks.

Dr. Rajkumar Upadhyay

Respected Chairman, Mr. Lahoti, Mr. Mittal, Mr. Tanglura fellow panelists, industry leaders, policy makers experts, ladies and gentlemen thank you for inviting me here I think in the previous session there was talk about how do you optimize your network how do you self heal your network how do you make correction in the network so I’m not going to talk about that even though we also as India we have developed our own 4G and 5G we used because we were the late comers so we used quite a bit of AI in terms of predicting the faults because a lot of logs are generated by various systems so I’m not going to talk about that I’m going to talk about where is the PPT?

where is the PPT? So I’m going to talk about some use cases which we have developed during last few years. We are CDOT. We were established in 1984, and we had the legacy of developing the rural telecommunication. We work in primarily three to four areas, the mobile wireless, cyber security, information security that is done by quantum. Quantum AI is a horizontal thing and advanced telecom applications. But I will focus on these are our product line, and all of these products actually use AI because AI is so pervasive. Without AI, you cannot function. So all these product lines, whether it is mobile, whether it is cyber security, information security, disaster management applications, are using AI in a big way.

So one of the key application, key product what we have developed is Fraud Pro. What it does, it actually detects the fraudulent connections in the system. I think you may be aware with the cases of Jamtara, Mewat and all these sim factories running. And these sim factories were destroyed by this particular software. What it does, it groups all the images of the same person because if you go and buy 500 sims using same Aadhaar card or same driving license, this is what was happening. So it detects that and it not only matches the images, it also matches the demographics, name, father, name. And sees that whether names and photos are same but names are different.

So using, I will come to the number. I think some number was described in the beginning that how many connections were disconnected using this software. So this is deduplication and finding the, and in fact we developed for telecom, it is being used now, going to be used in driving license, passports, income tax, deduplication, Manrega deduplications. The second one, I think this, it mentions the AI. AI analysis, 86%. 7 crore mobile numbers. and it was very well used even to, you know, find out dead bodies in the Balasore train accident. The first use case of this particular platform was to identify the dead bodies. The second one is financial risk indicator. I think you would have seen in newspapers RBI has mandated the banks to use financial risk indicator.

What it does, if A is transferring money to B, so B, the credentials of B are checked with the platform which we have developed, which we call digital intelligence platform. And the platform returns a figure that this is risky number, is medium risk or low risk. If it is a high risk number, the bank will not happen, let that transition happen. And it has saved a lot of fraud cases currently. And all the banks actually are using this FRI, which is able to tell that the B number where the money is going is a, you know, dangerous number or a well -identified fraudulent number, the money is stopped. The next is the Chakshu. Chakshu is again crowdsourcing platform.

wherein if you get a fraudulent call or a promotional call or any kind of call or fake KYC or faking as police, you can report and using crowdsourcing, we are able to disconnect the number and we are able to take. And this is again using our Sanchar Sati app. Just to bring to the notice of the audience, rarely a government app has a download of 18 million plus. This is 18 million plus downloads are there. The hits in the website of Sanchar Sati are 25 crore. Very rarely you see that. So this shows the popularity of how customers are protected using the AI -based platform. This is again AI -based platform. This Tapcoff and CIR here, I don’t know how many of you have used.

I would request those who have not used, please use it. In Sanchar Sati, you go, you give your mobile number, it will tell you all the connections under your name. using fuzzy logic, fuzzy AI and fuzzy logic. It doesn’t ask you any other detail. And that number also we ask because we want to verify that it is you and the OTP sent. Otherwise, no other details are asked. Just by the detail, we are able to find out how many numbers are there. And just to bring to your notice, 70 lakh connections have been disconnected using this. People have themselves disconnected their numbers because it also tells you, this is not my number, disconnect it. This was a big problem for us.

When we blocked the SIMs in the country, these guys went outside. And they started pumping calls using Indian numbers. It’s spoof call. This technology is available. I can get call from my own number in using this. We were getting 15 million calls per day, 15 million. And now you see, this was a very complex system because when the call hits at the gateway, the system has to decide within millisecond that this call to be let it go through or block it. it has to be decision has to be millisecond and it has to be zero error because no actual call should be blocked and today we are able to do our because the rigorous testing happened with all the operators today we are able to we have totally neutralize this of course they have found another way they are they have taken sims in places like Cambodia Indonesia Myanmar from there they’re calling so again the AI based system is alerting that these are the numbers of that country and we are alerting the governments of that country AI based security solution because cyber is another major area for all of us and somebody was mentioning the AI will do cyber attacks and it’s true we see in our system AI attacking the systems earlier it was human and now it is fully AI so you have to use AI to counter it so the cyber security solution today what we provide is fully AI based so that it can coordinate between various particular solutions disaster management we have used AI you may be aware that India has deployed ITU CAP based disaster management system as well as 3GPP cell broadcast based disaster management system which is implemented across India we use AI here because what happens like IMD is giving me a warning on rain CWC is giving me a warning on flood weather report is coming so we federate all these inputs and using AI you have less than 2 minutes so this I won’t go through NMS of course we use AI to see that how the when the network is likely to be down and this is actually implemented in Bharatnet 1 and 2 it tells you that this is likely to fail this router is misbehaving or this node is misbehaving so that was my last slide so in nutshell what I am saying that the a lot of AI applications are needed for the customer side to protect the customers and we have made India has made a good progress in terms of reducing the frauds reducing the fraudulent connections therefore safeguarding the customers and we will be very happy to take these technologies to any part of the world given it is implemented at India scale thank you so much

Dr. M P Tangirala

thank you Dr. Rupajay that was very interesting the flavor of the kind of R &D that has been done and the apps that have been developed we now move to someone on the panel Mr. Matan Babu Kashi Lingam who is representing the service providers and you carry a lot of burden of customer expectations on your shoulders so do tell us about your thoughts on the topic of today’s thank you thank you

Mathan Babu Kasilingam

So a few things that we have done as a service provider, majority of the topics are touched upon from fraud and cyber security. I’m trying to say the role of AI in terms of establishing trust, our entire whole ecosystem, which is telecom ecosystem, relies primarily on the customer trust. So to ensure that we have given trust journey for our customers and in the means of adopting AI, there are various core secure pillars that we have followed through. Any AI adoption should have the reasonability, reliability, trust, privacy should come to deliver that. So as a TSP, when we have embarked on the journey of AI, that’s the first and foremost core element that we have taken into consideration.

We are one of the TSPs in the country who have taken the journey of privacy since past five plus years now. We are completely certified on PIMS ISO 27701. We are the only TSP in the country who have governed privacy by design and have certified ourselves against that as well. So that is to only ensure that the trust is given back to the customer. Now I will come back on the journey of AI adoption. First thing that has happened is consumerization of AI. AI has been part and parcel of our life since all of us learned about Siri, Alexa. Day to day home we have been living with AI for many, many years now. So the consumerization of AI happened many, many years back.

What happened in enterprises, there came the pressure of adoption of AI in enterprise. In that, the first and foremost thing that we did is we took as applied in consumer. Let us try and adopt it in AI. Enterprises as well. well. Obviously, it has its own benefit. The benefit being it shares quick win, right? So you get to see a first yearly win that you are able to see by deploying AI in your setup. So how enterprises embarked on that journey is you pick and choose one department, one function, one key problem that you are faced with, deploy AI in it, and you see results. So we saw all of these examples. Fraud. It’s a serious problem for the entire country as a whole.

What can we do? Can we leverage AI? And AI is capable of giving me million eyes and million hands in name of a single human operating that, right? So the power of AI came to aid. We are able to today identify fraud. Sir also briefly touched upon cyber security. So as national critical infrastructures as what we are as TSPs, today we are pressed with serious amount of attacks. So India apparently in the past one year has hosted as many mega significant events. whether it is G20, whether it is Mahakum, then there is situational geopolitical tensions that we went by and now we are hosting AI summit. So national critical infrastructures like TSPs are also faced with increased volume of cyber attacks increased volume if I tell would be not 10 times it would be in as many count that I could multiply with that is a quantum of increased cyber attacks that we are seeing now in the cyber field we are also limited with the number of professionals we have.

So the power of AI not just for the attackers as defenders as well we have started leveraging how can we leverage the power of AI to combat them. So we have, so those are quick wins right? Network operations with the advent of 5G we wanted self operating self healing networks. So in various smaller smaller areas where AI can be embarked that we can realize a very very quick business value, enterprises started adopting That’s the first part that we wanted the quick win, we saw the quick win. The challenge that came with that is we started seeing them in piecemeal approach. The data that we were working upon was almost similar. You gather this intelligence information from the same network elements and nodes.

But you started to look upon through different lenses. All that I need to do is to look through different lens. But I started creating individual siloed repository of data. So if you look at corporates today that have embarked AI, you will see as many isolated silos of data created for them. Because each of them want their lens. And for every lens, they didn’t see the data through the lens. They created a total isolation of the data. Second thing that happened is mammoth amount of infrastructure. Anybody who touches AI today talks about GPUs, humongous. Power that is required to run, etc., etc. That again at enterprise. Thank you. it is siloed data, siloed infrastructure that has been taken into account.

So the journey that we are today in is we had the quick wins, we have taken the first few steps, but we are re -looking at from a different standpoint as we see currently. So we have stepped back. Is there data deduplication that can be done today? In lieu of 20, 30 silos that I have created, do I want to create one single repository of this data? Thereby the secure element also becomes easier. If in silo I have to secure everywhere, bring them in one area, I have the ability to secure them well. Can I leverage a common platform infrastructure, which is the AI infrastructure that is required to put the data and then do these work?

We are doing that. So you can still leverage a comprehensive LLMs, individual businesses in variety of functions, I have taken their own purpose -built LLMs, right? Because… You will have a HR function. The provider for HR is a specific, say SAP, would be primarily driven for HR. And surrounding systems which are talking AI would have built on top of it. There will be a self -healing network. The network provider builds an AI -driven system. So there we are now stepping back to see can we build a comprehensive central LLM, which will still deliver the purpose that are required for looking at. So at V, the premise is core infrastructure, put data in comprehensive, expose them through interconnected enterprise API architecture, thereby businesses and users do not have to talk to the data directly.

They talk through the enterprise model, touch the AI infrastructure, and go and reach back the data for various reasons. So it could be to service my service provider. It could be to service my customers. It could be my customer support. Thank you. bridging them. That’s a platform journey that we are doing that. So this consolidation, like I told, privacy is by design. We are able to do the DPDP compliance inclusive, which is minimize the data. Data in one area, we are able to minimize them as appropriate. That’s what I wanted to share with. Thank you.

Dr. M P Tangirala

Thank you. Fascinating. Now we come to Mr. Abbas, who is the senior DDG from TEC standard setting. He’s promised that he will make a different presentation. So over to you, Mr. Abbas.

Syed Tausif Abbas

the name of application what are the technology used what is the purpose like that then what was the impact or harm information with the what was the incident like physical harm environmental property psychological so these things also forms part of the 30 key fields in which the input is to be given for the schema and then some of the information which is to be masked later on so those related to the name of the submitter email and other things related to submitter information which will be redacted later on similarly the taxonomy as earlier I told that it will classify the incident into different categories depending upon the incident type whether it is a subcategory as network description service quality outage or it is a security beach or AI mismanagement or then affected system whether core is affected whether the radio access network is affected whether the edge is affected or IOT components or physical so which part of the network is affected or any application which is related to user is affected and then what is the incident severity whether it is critical high moderate or low so that also will be recorded and cause of failure if it is known to the user otherwise the deployer or the service provider has to enter this what was the cause of the failure so basically this database will give input to the service provider also that they themselves can examine it they can analyze it and then realign their AI related application so that these incidents don’t recur in future so it’s a gradual auto development of their own AI system which will be then error free and gives the best results output so for this is only this standard has been made but it is not going to give any mitigation mechanism or something.

This is to be decided by the deployer who has deployed those AI application and it is not mandatory. Just as a beginning, when the new computer system came, initially there was not much thing but when the incident started then computer emergency response team was proposed and it started working on collecting the data related to the computer incident. So similarly since the AI has already begun, so we should have this mechanism in place so that we can have the AI incident reporting database also available. Thank you so much.

Dr. M P Tangirala

Thank you so much Mr. Abbas. Presenting arguably and congratulations arguably world’s first time that such a standard has been put out. So since we are fresh off with you, I will start with a question for you about what you have just now presented. you said it is not mandatory it is voluntary of course we will see where that journey goes as you said about certain coming in after the computers but can you tell us a little bit more about what value it offers to the telecom service providers if they voluntarily adopt this standard

Syed Tausif Abbas

so telecom service providers they have already started using the AI application in their network optimization network and services to the users orchestration of resources so many things already the AI application has started so if any incident which gives an unintended outcome if it is recorded and reported then it will be in the best interest of the service provider that those incidents are analyzed and then rectified for so that it is not occurring in the future so in this way it is a can be best utilized by the service provider and since the structure of the schema and taxonomy both are given so it will be a same structure compilation of data which every service provider is doing so that will give benefit to the regulator and policy makers to how to go about the AI policy because of those input which we get from those incidents.

Dr. M P Tangirala

So therefore, Mr. Kashi Lingam, would you think it offers a voluntary adoption of this standard offers any benefit to you from the side of a service provider?

Mathan Babu Kasilingam

I think like sir rightly mentioned about incident recording has been not a new phenomenon for at least people who have been in the IT industry. Recording cyber specific incident additionally has also started happening. However, we have tied back to the same ITIL framework that has been there historically followed. So enhanced AI is yet another tool which is landing up creating possibly an outcome. The outcome could be erroneous. It could be an event, incident, bias could be one of the situation that are arising. So as TSPs, individually while we have started doing this internally as we have adopted the journey of AI for us, these are recorded events. But one manner that it helps and supports in the framework as TEC has put across is, yes, it can be streamlined in a manner that the rest of the populace, if they have to refer by, can also be referred.

Because today there are no standalone companies, right? Every company is in the area. They are in the area of digital and IT. They are only doing. work in their own function. If you ask a bank, bank has to tell that I am an IT company in the service of doing banking. That is how it is changed. So IT plays a crucial role and AI will be a supporting arm in that. So this record keeping will make the ability for us to scale our AI and models as appropriately. With the advent that India wants to, and we have already announced three LLMs coming our way already, homegrown, home developed here, a platform like this will possibly help us manage and then refine our models well.

Dr. M P Tangirala

So you mentioned how enterprises are becoming digital first. And you also spoke in your initial remarks about AI for enterprises. So how do you look at this controlling costs of, you know, costs the infra part you did deal with, but how about the costs? Of AI for enterprises? Any thoughts on that?

Mathan Babu Kasilingam

Currently, it is still a significant amount that is being incurred upon. So the cost optimization, a larger chunk of cost optimization comes from the infrastructure as a whole. So about 80 -90 % of the cost to AI goes primarily on the infra in itself, both in store and as well in compute. The rest obviously comes in the skills. So today, while we definitely showcase the world that we have humongous talents that are getting built in the AI area, for an enterprise still to have these skilled engineers to build upon AI is still an adaptable work -in -progress area. So I think in the journey, we are now looking at AI to come in the aid of AI.

So we were in conversation with one of the AI -driven companies yesterday, and the way he highlighted back to us, telling that earlier… the total employee base was 10 ,000. Now there is a refinement and optimization by incorporating AI and thereby there is reduction in employee base. But then if we look at the people who are operating in AI, which was 30 now has gone to 3 ,000. So you cut down here and increase over there. So we were trying to tell them that the true power of AI is actually in making sure that AI is not touched with people, human. So reduction in human by upskilling that as appropriately is an important element for us to do.

Thank you.

Dr. M P Tangirala

I’ll come to you, Dr. Upadhyay. You did, you know, I know I cut you off or sort of gave you a time pause there. Could you tell us a little bit more about what you were doing, what you’re doing with respect to disaster management, the application that you spoke about?

Dr. Rajkumar Upadhyay

Disaster, yeah. Yeah. So disaster management, as you know, earlier, how did you? It used to happen. Suppose there is a cyclone in Odisha. A mail will go from IMD to chief secretary. Chief secretary will write to district collector. District collector would, in his best way, try to send the cyclone exactly to come. And we used to have thousands of lives lost, property lost. Today, using AI and the sensors, the system what we have done, this is one unified platform where all the alert generating agencies, IMD, CWC, FRI, DGSE, so all alert generating agencies are connected through APIs, auto. All the telecom operators are connected. All the alert dissemination agencies like SDMAs in the states are connected.

So it is all powerful one system. Now there is an alarm, a sensor alarm comes that there is a cyclone likely to, or rain likely. This is automatically read by the system. It prepares the message and finds out what is the geo -targeted area. Because earlier the problem was, they will put these kind of threats but nothing will happen. So people will take next time very casually. But today it is a geo -targeted system. It will alert only to the people who are in that belt. Suppose there is a cyclone hitting Gopalpur in Odisha it will only alert the people who are likely to be affected much before. And it will tell you also you need to evacuate you need to evacuate.

If you need to evacuate what is the arrangement by the government or you need to stay indoors. So all that happens and it was actually presented in parliament. The death in for example I am taking the case of Odisha where thousands of people died in 99. The death is zero. So what happened that after that we implemented because India is a large country and sometime a large population is to be alerted in some other cases. That time SMS gets delayed. You know SMS is a sequential process. SMS is sent by SMSCs. There was a new technology called cell broadcast where you don’t see the messages common. You don’t send through SMS you just broadcast it.

So we developed a technology called Cell Broadcast And it was recently used in Cyclone Montha And how do you use AI? Because now I am getting Inputs from various agencies I federate it My system federates all these information Using AI, builds one Particular message, finds out what is The right area where it is likely to hit And sends only to those people And the beauty of this system is, earlier there was a system Of group SMS, they will find people who are staying there Now even if you are a foreigner You are available at that particular time there It will pick your number and it will Give you the message. So tsunami Is coming, so We don’t know, people may be from here And there at the beach.

So this has A very good, and in fact we have Published a paper in ITU, ITU has taken This as a report So this is going Forward, we feel that this particular System will meet The requirement of early warning for all By UN by 2027 And we are already talking to many countries And soon this solution will be Deployed in few countries which is Thank you. Thank you.

Dr. M P Tangirala

In fact, in your presentation, you also spoke about fraud pro and so on. But I will, in the interest of time, I’ll move to Mr. Gorman about this fraud and scam. Now, you did in your opening remarks talk about the importance of collaboration across sectors. Also, the opportunities of, you know, of engendering innovation through collaboration for controlling or combating scams. Could you elaborate a bit upon that?

Mr. Julian Gorman

Sure. Thanks. Thanks for your question. I think this builds on actually the last couple of comments. I mean, what we’re talking about here is sharing data between multi -parties through standardized interfaces and then using AI or something or other to produce a good outcome. And all these things are innovations. They’re on the leading edge of something. If I start with the first thing, data sharing through standardized interfaces. standard interfaces, you know, GSMA has open gateway APIs program and that is contributing to providing data points which can be used in assessing risk for transactions. There’s other data that can be shared that could help address scams earlier in the cycle. Example, there’s lots of other data points to do and that’s the proof of concept that GSMA is working on in Southeast Asia, sharing data.

The challenge with doing that is you’re at the borders of regulatory compliance. You’re talking about private information or personal information or maybe not. There’s sometimes debate. But to be effective, you’re talking about being able to measure the risk on a particular individual user by sharing information across multiple parties. That requires some regulatory support, sandboxes or other activities to help find, to develop the innovation that finds the solutions that help combat scams. I think one of the things we need to focus on in industry is how do we create that nurturing environment that permits exploration of data sharing in a privacy -enhanced way. There’s lots of nice new technologies that have the impact while complying with the regulations and the privacy we want to maintain.

But ultimately, from a mobile operator point of view, I would say there’s four pillars in this combating scam thing. The one is the network, making sure the network cannot be manipulated in favor of the scammers. And that’s by CLI spoofing, all that sort of stuff. Let’s cut that out. If you introduce AI, there’s other things you can do on top. The second is what can mobile operators expose to the ecosystem so that the ecosystem can measure and respond to risk? Open gateway APIs is one thing. The POC I talked about before is another. And there may be other things. The third is what can mobile operators provide as services to their customers? in the same way the physical environment you can provide hard hats and things like that there’s things you can help customers and they can choose to acquire or choose to use them of their own choice to help protect them online and the fourth thing is digital skills digital skills historically we’ve considered is a destination in actual fact we now know we’re never going to hit that final point skills are going to continue to adopt and to adapt and it’s critical that we focus on all four pillars and that from a regulatory point of view and ecosystem point of view we’re collaborating so that the data can flow we can try and test things and we overcome the prejudice that may be stopping innovation because there’s an expectation that you can’t do these things so it requires policy makers regulatory to sponsor to nurture these things I mean I can guarantee I work into 90 % of mobile operators in Asia Pacific and I start a sentence with I want to suggest we use consumer data for I won’t get past halfway through the sentence they’ll say nope you can’t do that But in actual fact, if we want to be successful, no single entity, especially no mobile operator, has all the information.

I mean, if a mobile operator arbitrarily starts turning off SIM cards because they think maybe that traffic looks a bit dubious, I mean, you’ve only got to look at the Optus outage in Australia where three or four people died because they couldn’t call emergency services. You don’t want to be taking that action. It requires collaboration, regulatory support and policy support.

Dr. M P Tangirala

Yeah, thank you. Thank you. Network, ecosystem, hard hats and upskilling. I think that’s a good way to end the discussion here on the panel. But we have time for one question. Yes, Mr. Jha. We have less than two minutes.

Anil Kumar Jha

Thank you. Very quick, very brief. The question from Mr. Julian Gorman. as we have said we are under attack may we attack anytime. We have also said that we should align with the global trends in order to combat these fraud and all those things. You have heard our panelists who are the icons in their field of manufacturing and standardization and PSPs. Could you suggest two steps that global leaders should take to align the world with themselves and two steps that India should take to align with the world. Thank you.

Mr. Julian Gorman

I mean two steps globally. So the proof of concept we are trying to do in Southeast Asia is actually prove that data can be shared domestically but across borders also in a safe secure way and has impact on controlling scams. One thing we need to remember with scams all we are doing by taking action against scams is increasing the cost of the business case and if we increase the cost of the business case here then another area becomes more favorable and that could be just different types of scams or it could be different locations and so that leads to the what do we need to do globally. We need to act across borders. We need to act as a collective global community.

GSMA has a program called United Against Scams there will be a lot of things about that in Barcelona but India is obviously taking great action domestically or taking steps domestically sharing that knowledge across borders and being able to share that data across borders is important and so I would leave it at those two points

Dr. M P Tangirala

Thank you, it also gives us pause for thought, maybe as regulators we also need to look at collaborating efforts across regulators because there are again sectoral issues that we need to do and so with that we are now at the end of the session, I would request the audience to give a big round of applause to my panelists who have given us very good insight into the topic at hand and thank you so much

Moderator

Thank you moderator sir and all our distinguished panelists for such a vibrant discussion on usage of responsible AI the standards, the repository, various government app for enhancing consumer experience. Your insights will greatly benefit the overall digital ecosystem. Now I would request Dr. M.P. Tangirala to present mementos to our distinguished speakers as a token of appreciation. First to Mr. Julian Gorman. To Dr. Rajkumar Upadhaya. To Mr. Mathan Babu. To Mr. S.T. Abbas. Now I invite Sri A.K. Jha, Principal Advisor, TRAI to present a memento to the moderator of this session, Dr. M.P. Tangirala as a token of appreciation for moderating such a productive session Thank you so much, sir Now I take this opportunity to invite all the speakers for a group photograph I once again would request Chairman, sir, M.P.

Tangirala, Secretary, sir and all the Principal Advisors to please join the session speakers of this panel for a group photograph Thank you give a huge round of applause to all the panelists for joining us. Thank you. Thank you. Thank you. Thank you.

Dr. M P Tangirala

Speech speed

115 words per minute

Speech length

929 words

Speech time

482 seconds

Human‑in‑the‑loop oversight

Explanation

Dr. Tangirala stresses that AI automation must retain human control so that decisions do not become autonomous and unchecked. He notes that ultimate responsibility for decision integrity stays with telecom providers even as efficiency improves.

Evidence

“We need this automation to have an element of human control that is so that the system does not run away with its own decisions” [1]. “Then, of course, this whole issue of the human in the loop or human in the mix” [2]. “And therefore, you know, whether it’s outage management, service continuity, grievance handling, you know, while efficiencies may improve, the responsibility for decision integrity ultimately remains with the telecom service providers” [14].

Major discussion point

Balancing Innovation, Privacy, and Trust in AI‑Driven Telecom Services

Topics

Artificial intelligence | Human rights and the ethical dimensions of the information society | The enabling environment for digital development

Proactive customer communication

Explanation

He argues that clear and proactive communication with customers about AI‑driven outcomes is essential to maintain trust and avoid confusion, especially when customers are indirectly affected by automated decisions.

Evidence

“And clear and proactive communication with the customers would become very important” [16].

Major discussion point

Balancing Innovation, Privacy, and Trust in AI‑Driven Telecom Services

Topics

Human rights and the ethical dimensions of the information society | Building confidence and security in the use of ICTs

Moderator

Speech speed

48 words per minute

Speech length

290 words

Speech time

355 seconds

Balancing innovation with privacy and trust

Explanation

The moderator frames the session around the need to innovate in AI‑driven telecom services while safeguarding privacy and building trust among users.

Evidence

“Innovation with privacy and trust” [12]. “Let’s engage deeply on how to balance information” [13].

Major discussion point

Balancing Innovation, Privacy, and Trust in AI‑Driven Telecom Services

Topics

The enabling environment for digital development | Human rights and the ethical dimensions of the information society

Mr. Julian Gorman

Speech speed

158 words per minute

Speech length

1349 words

Speech time

510 seconds

Global collaboration and regulatory alignment

Explanation

Julian Gorman outlines the formation of a cross‑sector coalition, the role of India as a telecom superpower, and calls for cross‑border data sharing and coordinated anti‑scam actions. He stresses that sharing data through standardized APIs is key to combating scams worldwide.

Evidence

“At GSMA, about 12 months ago, we formed a coalition called Cross -Sector Any Scam Task Force” [60]. “And the important thing also for India is India is rising not just economically but also in its position in the telecom world and the GSMA sort of global ecosystem is India is a real telecom superpower and it’s on the rise” [130]. “So the proof of concept we are trying to do in Southeast Asia is actually prove that data can be shared domestically but across borders also in a safe secure way and has impact on controlling scams” [64]. “GSMA has a program called United Against Scams there will be a lot of things about that in Barcelona but India is obviously taking great action domestically or taking steps domestically sharing that knowledge across borders and being able to share that data across borders is important and so I would leave it at those two points” [67]. “I mean, what we’re talking about here is sharing data between multi -parties through standardized interfaces and then using AI or something or other to produce a good outcome” [21]. “If I start with the first thing, data sharing through standardized interfaces” [62]. “standard interfaces, you know, GSMA has open gateway APIs program and that is contributing to providing data points which can be used in assessing risk for transactions” [65]. “Open gateway APIs is one thing” [66].

Major discussion point

Global Collaboration and Regulatory Alignment

Topics

Data governance | Artificial intelligence | Building confidence and security in the use of ICTs | The enabling environment for digital development

Dr. Rajkumar Upadhyay

Speech speed

171 words per minute

Speech length

1925 words

Speech time

671 seconds

AI‑Powered fraud detection and scam mitigation

Explanation

Dr. Upadhyay describes AI‑based products such as Fraud Pro, Sanchar Sati and deduplication tools that detect fraudulent connections and enable crowd‑sourced blocking of spam, spoof and scam calls, resulting in millions of disconnections.

Evidence

“So one of the key application, key product what we have developed is Fraud Pro” [41]. “We are able to today identify fraud” [42]. “And this is again using our Sanchar Sati app” [43]. “Is there data deduplication that can be done today?” [44]. “And that is where, you know, there are impactful applications of AI in telecoms, in spam and fraud prevention, which a person had mentioned in his opening remarks about how 2 .1 million numbers were disconnected using AI -based tracking” [46]. “wherein if you get a fraudulent call or a promotional call or any kind of call or fake KYC or faking as police, you can report and using crowdsourcing, we are able to disconnect the number and we are able to take” [47]. “And just to bring to your notice, 70 lakh connections have been disconnected using this” [48].

Major discussion point

AI‑Powered Fraud Detection and Scam Mitigation

Topics

Artificial intelligence | Building confidence and security in the use of ICTs | Data governance

AI‑federated early‑warning system for disaster management

Explanation

He explains an AI‑driven platform that aggregates sensor data and alerts from multiple agencies, creates geo‑targeted cell‑broadcast messages, and has already reduced cyclone‑related deaths to zero in pilot deployments.

Evidence

“Today, using AI and the sensors, the system what we have done, this is one unified platform where all the alert generating agencies, IMD, CWC, FRI, DGSE, so all alert generating agencies are connected through APIs, auto” [69]. “It prepares the message and finds out what is the geo‑targeted area” [113]. “So we developed a technology called Cell Broadcast And it was recently used in Cyclone Montha And how do you use AI?” [115]. “So this has A very good, and in fact we have Published a paper in ITU, ITU has taken This as a report So this is going Forward, we feel that this particular System will meet The requirement of early warning for all By UN by 2027” [116]. “The death is zero” [121].

Major discussion point

AI in Disaster Management and Public Safety

Topics

Artificial intelligence | Building confidence and security in the use of ICTs | Social and economic development

Mathan Babu Kasilingam

Speech speed

159 words per minute

Speech length

AI Standards, Incident Reporting, and Governance

Topics

Artificial intelligence | Monitoring and measurement | Data governance

Syed Tausif Abbas

Speech speed

148 words per minute

Speech length

552 words

Speech time

223 seconds

Voluntary AI incident‑reporting schema

Explanation

Abbas describes a voluntary schema with a taxonomy that captures incident details, impact categories and causes, enabling service providers and regulators to analyse failures and prevent recurrence.

Evidence

“the name of application what are the technology used what is the purpose like that then what was the impact or harm information with the what was the incident like physical harm environmental property psychological so these things also forms part of the 30 key fields in which the input is to be given for the schema and then some of the information which is to be masked later on so those related to the name of the submitter email and other things related to submitter information which will be redacted later on similarly the taxonomy as earlier I told that it will classify the incident into different categories depending upon the incident type whether it is a subcategory as network description service quality outage or it is a security breach or AI mismanagement or then affected system whether core is affected whether the radio access network is affected whether the edge is affected or IOT components or physical so which part of the network is affected or any application which is related to user is affected and then what is the incident severity whether it is critical high moderate or low so that also will be recorded and cause of failure if it is known to the user otherwise the deployer or the service provider has to enter this what was the cause of the failure so basically this database will give input to the service provider also that they themselves can examine it they can analyze it and then realign their AI related application so that these incidents don’t recur in future” [74]. “So similarly since the AI has already begun, so we should have this mechanism in place so that we can have the AI incident reporting database also available” [75]. “so telecom service providers they have already started using the AI application in their network optimization network and services to the users orchestration of resources so many things already the AI application has started so if any incident which gives an unintended outcome if it is recorded and reported then it will be in the best interest of the service provider that those incidents are analyzed and then rectified for so that it is not occurring in the future so in this way it is a can be best utilized by the service provider and since the structure of the schema and taxonomy both are given so it will be a same structure compilation of data which every service provider is doing so that will give benefit to the regulator and policy makers to how to go about the AI policy because of those input which we get from those incidents” [76].

Major discussion point

AI Standards, Incident Reporting, and Governance

Topics

Artificial intelligence | Data governance | Monitoring and measurement

Anil Kumar Jha

Speech speed

180 words per minute

Speech length

94 words

Speech time

31 seconds

Call for coordinated global and Indian actions on scam mitigation

Explanation

Jha asks the panel to suggest concrete steps for global leaders and for India to align with the world in combating scams, emphasizing the need for coordinated policy and operational measures.

Evidence

“Could you suggest two steps that global leaders should take to align the world with themselves and two steps that India should take to align with the world” [133].

Major discussion point

Global Collaboration and Regulatory Alignment

Topics

The enabling environment for digital development | Data governance

Agreements

Agreement points

AI is essential for fraud detection and prevention at scale

Speakers

– Mr. Julian Gorman
– Dr. Rajkumar Upadhyay
– Mathan Babu Kasilingam

Arguments

Regulation cannot move as fast as scammers who are technically capable and well-funded, requiring industry innovation focus

Fraud Pro software successfully detected fraudulent connections by grouping images and demographics, leading to disconnection of 86.7 crore mobile numbers

AI provides “million eyes and million hands” capability for fraud detection, offering significant advantages over human-only approaches

Summary

All speakers agree that AI is crucial for combating fraud and scams due to its ability to scale detection capabilities far beyond human capacity and respond faster than traditional regulatory approaches

Topics

Artificial intelligence | Building confidence and security in the use of ICTs

Cross-sector collaboration is necessary for effective scam prevention

Speakers

– Mr. Julian Gorman
– Dr. Rajkumar Upadhyay
– Dr. M P Tangirala

Arguments

Arguments

Human-in-the-loop control is necessary to prevent AI systems from making autonomous decisions without oversight

Building customer trust requires AI systems to demonstrate reasonability, reliability, privacy protection, and transparency

Summary

Both speakers emphasize the importance of maintaining human control and oversight in AI systems to ensure accountability and prevent unintended autonomous decisions

Topics

Artificial intelligence | Human rights and the ethical dimensions of the information society

Information and communication technologies for development | Social and economic development | The enabling environment for digital development

Unexpected consensus

Arguments

Enterprises initially adopted AI in piecemeal approaches for quick wins but created siloed data repositories and infrastructure

Companies are now consolidating AI infrastructure into comprehensive platforms with centralized data and common LLMs

Summary

Kasilingam describes the evolution from siloed AI implementations to centralized platforms, but this represents an internal organizational disagreement about optimal AI architecture rather than disagreement between speakers.

Topics

Artificial intelligence | The digital economy | Data governance

Unexpected differences

Global vs domestic focus in AI and scam prevention

Speakers

– Mr. Julian Gorman
– Dr. Rajkumar Upadhyay

Arguments

India must embrace its role as a global telecom superpower and work as part of a global community, not just focus domestically

AI-based disaster management system federates inputs from multiple agencies and provides geo-targeted alerts, reducing casualties to zero in recent cyclones

Explanation

While both speakers acknowledge India’s technological capabilities, Gorman emphasizes India’s responsibility as a global telecom superpower to think beyond domestic concerns, while Upadhyay focuses primarily on domestic achievements and solutions. This creates an unexpected tension between global leadership expectations and domestic success focus.

Topics

The enabling environment for digital development | Information and communication technologies for development | Building confidence and security in the use of ICTs

Overall assessment

Summary

The discussion revealed surprisingly few direct disagreements among speakers, with most conflicts being implicit rather than explicit. The main areas of disagreement centered around regulatory approaches (industry-led vs government-led), implementation strategies (voluntary vs mandatory standards), and focus scope (global vs domestic priorities).

Disagreement level

Low to moderate disagreement level. The speakers largely complemented each other’s perspectives rather than directly contradicting them. However, the underlying philosophical differences about the role of regulation, the pace of standardization, and the balance between global cooperation and domestic focus could have significant implications for policy development and implementation strategies in AI governance and cybersecurity.

Partial agreements

All speakers agree that AI is essential for combating fraud and scams, but they disagree on implementation approaches. Gorman emphasizes cross-border data sharing and regulatory sandboxes, Upadhyay focuses on domestic AI solutions and government-led initiatives, while Kasilingam emphasizes private sector scalability and infrastructure consolidation.

Speakers

– Mr. Julian Gorman
– Dr. Rajkumar Upadhyay
– Mathan Babu Kasilingam

Arguments

Data sharing across borders is essential for effective scam prevention, requiring regulatory sandboxes and policy support

Fraud Pro software successfully detected fraudulent connections by grouping images and demographics, leading to disconnection of 86.7 crore mobile numbers

AI provides ‘million eyes and million hands’ capability for fraud detection, offering significant advantages over human-only approaches

Topics

Building confidence and security in the use of ICTs | Artificial intelligence | Data governance

Both speakers agree on the importance of human oversight and customer trust in AI systems, but they approach it differently. Tangirala emphasizes the need for human control to prevent autonomous decision-making, while Kasilingam focuses on systematic trust-building through privacy certification and transparency principles.

Speakers

– Dr. M P Tangirala
– Mathan Babu Kasilingam

Arguments

Human-in-the-loop control is necessary to prevent AI systems from making autonomous decisions without oversight

Building customer trust requires AI systems to demonstrate reasonability, reliability, privacy protection, and transparency

Topics

Artificial intelligence | Human rights and the ethical dimensions of the information society | Building confidence and security in the use of ICTs

Information and communication technologies for development | Social and economic development | The enabling environment for digital development

Takeaways

Key takeaways

AI-driven fraud prevention requires industry collaboration rather than relying solely on regulation, as scammers move faster than regulatory frameworks

India has successfully implemented AI-based systems that have disconnected 86.7 crore fraudulent mobile numbers and reduced disaster-related casualties to zero in recent cyclones

Cross-border collaboration is essential for effective scam prevention, as local actions simply shift criminal activity to other locations or methods

Enterprise AI adoption is evolving from siloed, piecemeal approaches to consolidated platforms with centralized data and common infrastructure to reduce costs and improve security

Customer trust in AI systems requires transparency, privacy by design, human oversight, and clear communication about AI-driven decisions that affect customers

India has developed world’s first AI incident reporting standard to help organizations learn from AI failures and improve system reliability

Four pillars for combating scams include: network security, ecosystem data sharing, customer protection services, and continuous digital skills development

Resolutions and action items

GSMA to continue proof of concept for cross-border data sharing in Southeast Asia to demonstrate safe and secure international collaboration

Service providers encouraged to voluntarily adopt TEC’s AI incident reporting standard for systematic improvement of AI systems

Industry to focus on creating regulatory sandboxes and policy support for privacy-enhanced data sharing innovations

Organizations to consolidate AI infrastructure from siloed approaches to comprehensive centralized platforms

Continued development and deployment of India’s AI-based solutions (Fraud Pro, Financial Risk Indicator, Chakshu, disaster management) to other countries

Unresolved issues

How to balance data sharing requirements for effective scam prevention with privacy regulations and compliance concerns

The challenge of regulatory frameworks keeping pace with rapidly evolving scammer techniques and AI capabilities

Cost optimization strategies for AI infrastructure, particularly the 80-90% costs associated with storage and compute resources

Skills gap in AI expertise for enterprises and the need for continuous upskilling as AI technology evolves

Standardization of cross-border regulatory cooperation mechanisms for combating international scam operations

Long-term sustainability of voluntary AI incident reporting versus potential mandatory requirements

Suggested compromises

Use of regulatory sandboxes to allow controlled experimentation with data sharing while maintaining privacy protections

Focus on outcome-based regulation rather than prescriptive service-based rules to allow innovation flexibility

Voluntary adoption of AI incident reporting standards as a starting point, with potential evolution to mandatory requirements based on industry experience

Gradual consolidation of enterprise AI infrastructure while maintaining business continuity during transition periods

This comment addresses the fundamental challenge that cybersecurity threats are global while responses are often national. It’s thought-provoking because it highlights the tension between data sovereignty/privacy concerns and the need for cross-border collaboration to combat global threats.

Impact

This comment provided a strong conclusion to the session by tying together themes of collaboration, data sharing, and global cooperation that had been woven throughout the discussion. It reinforced the need for regulatory frameworks that enable rather than hinder international cooperation.

Overall assessment

Explanation

The system has shown success in India and could meet UN requirements for early warning systems by 2027

Disclaimer: This is not an official session record. DiploAI generates these resources from audiovisual recordings, and they are presented as-is, including potential errors. Due to logistical challenges, such as discrepancies in audio/video or transcripts, names may be misspelled. We strive for accuracy to the best of our ability.

AI Automation in Telecom_ Ensuring Accountability and Public Trust India AI Impact Summit 2026

Summary

Keypoints

Major Discussion Points:

Overall Purpose:

Overall Tone:

Speakers

Opening Context and Fraud Prevention Scale

The Global Scam Challenge

India’s AI-Driven Success Stories

Enterprise AI Evolution and Consolidation

AI Incident Reporting Standards

Customer Trust and Human Oversight

Cross-Border Collaboration Challenges

Global Leadership and Knowledge Sharing

Interactive Discussion and Future Directions

Conclusion

Human‑in‑the‑loop oversight

Explanation

Evidence

Major discussion point

Topics

Proactive customer communication

Explanation

Evidence

Major discussion point

Topics

Balancing innovation with privacy and trust

Explanation

Evidence

Major discussion point

Topics

Global collaboration and regulatory alignment

Explanation

Evidence

Major discussion point

Topics

AI‑Powered fraud detection and scam mitigation

Explanation

Evidence

Major discussion point

Topics

AI‑federated early‑warning system for disaster management

Explanation

Evidence

Major discussion point

Topics

Privacy‑by‑design certification (ISO 27701)

Explanation

Evidence

Major discussion point

Topics

AI infrastructure, data silos, and cost management

Explanation

Evidence

Major discussion point

Topics

AI standards – incident reporting benefits for operators

Explanation

Evidence

Major discussion point

Topics

Voluntary AI incident‑reporting schema

Explanation

Evidence

Major discussion point

Topics

Call for coordinated global and Indian actions on scam mitigation

Explanation

Evidence

Major discussion point

Topics

Agreement points

AI is essential for fraud detection and prevention at scale

Speakers

Arguments

Summary

Topics

Cross-sector collaboration is necessary for effective scam prevention

Speakers

Privacy‑by‑design certification (ISO 27701)