TRAI issues guidelines to Access Service Providers to prevent misuse of messaging services

Introduction

Artificial intelligence is often hailed as a democratiser of knowledge, opportunity and skill. It is set to improve diagnostics, personalised learning, and productivity to boost the economy, which can assist millions of people to leave poverty. However, this may be an incomplete picture. A report of the United Nations Development Programme in 2025 tells a more complex tale. The Next Great Divergence: Why AI May Widen Inequality Between Countries cautions that, unless acts are taken to intervene, AI will not alleviate inequality between countries but will instead concentrate benefits in already advantaged economies and increase risks in more vulnerable ones.

Two Gaps, One Crisis

AI is not going to create a level playing field: it has been injected into a world where there is unprecedented inequality. The report outlines two structural asymmetries that will influence the ways in which its effects manifest: a capability gap and a vulnerability gap. 

Those countries that have high connectivity, skills, compute and regulation will be in a position to reap a greater portion of the AI dividend. Others will be exposed to greater risks of job losses, information exclusion, misinformation, and the indirect consequences of increased energy and water demands. 

The centre of this transition is the Asia-Pacific region, that harbors a population of more than 55 per cent of the world. More than half of the global AI users are now located in the region, but the initial positions are quite different. Nations such as Singapore and South Korea are already spending a lot of money on AI infrastructure, with others still striving to offer basic broadband services. Two out of three individuals already use AI tools in certain high-income economies. In most countries with low incomes, the utilisation is lower. Such figures are important as they depict not only a gap in technology but also a structural difference in terms of who controls AI and who is controlled by the latter.

When Inequality Becomes a Trust Problem

Any trusted technological system is based on three tenets: transparency, fairness and accountability. AI inequality negatively impacts all three.

If governments implement imported AI systems in areas with limited technical capability, with limited transparency on their operation, their construction, and their biases. Citizens do not really trust when decision-making systems are black boxes and domestic institutions lack the know-how to question them.

Data exclusion also interferes with fairness. The AI systems trained with the datasets not sufficiently representative of the rural population, linguistic minorities, and women will generate poorer results in those groups systematically. Since South Asian women are much less likely to own a smartphone,  this impacts their representation in digital data, and consequently in any AI system trained on such data. 

Safety Risks Are Not Evenly Distributed

The lack of trust has a direct safety aspect. For example, those countries that have less robust information ecosystems have a greater exposure to AI-generated misinformation that can bias the discourse of the populace, alter elections, and cause violence. They also have the weakest capability of screening, tagging, or combating such content.

The same can be said about labour markets. The very same technologies that can speed up marginalisation and destabilise governance increase human insecurity, especially among employees in the informal economy with weak social security. The UNDP report points out that the exposure of female employment to disruption by AI is disproportionate to that of male employment, which further presents a gendered dimension in an already unequal situation.

Risks of infrastructure are skewed as well. Large AI systems may create disproportionately high energy and water demands on countries that host the data infrastructure without there being an equivalent economic payback. The environmental cost is local while profits are outsourced. Dangers of AI spread downwards, and the advantages go upwards.

The Governance Gap and Regulatory Arbitrage

Governance is perhaps the most important aspect. There are only a few states that presently have extensive AI regulation systems. This gives rise to a patchy landscape, in which safety standards differ dramatically and where companies have an incentive to install systems in jurisdictions that have weaker regulation.

The main reason is the lack of capability, as expressed by Philip Schellekens, chief economist of the UNDP in Asia and the Pacific, who says that those countries that invest in skills, computing power and well-run governance structures will gain. The rest will be left far behind. 

This departure has its ramifications outside the nations. When users in other areas are subjected to widely different rates of safety and equity by the same international platforms, the concept of uniform digital norms would no longer be sustainable. Confidence in AI systems is lost not only locally but also on a global scale.

Way Forward

The UNDP report makes it clear that there is no inevitability of divergence. To avert it, however, it is necessary to consider AI governance as a development, rather than a technology problem.

The capacity to govern should be constructed and not presumed. This implies assisting countries in establishing regulatory systems, institutional capacity, and facilitating cross-border collaboration on standards. It can also imply considering some AI features as a public good, with common models and open standards that do not allow a few firms or states to become too powerful.

The UNDP articulates the problem in a simple manner: in the end, the world's people and not machines must decide on what technologies should be given priority and how to utilise them optimally.

Conclusion

AI inequality is often framed as an economic divergence story. But its implications run deeper. It reshapes who is protected, who is visible in data, and who has the power to challenge harmful outcomes. The risk is not just that some countries fall behind economically. It is that the global digital ecosystem fragments into zones of high trust and low trust, high protection and low protection. The choices made now will determine which path prevails. AI can reinforce existing divides or help bridge them. 

But that outcome will not be decided by the technology itself. It will be decided by how societies choose to distribute access, power, and responsibility in the systems they build.

References

1. https://www.undp.org/sites/g/files/zskgke326/files/2025-12/undp-rbap-the-next-great-divergence_1.pdf
2. https://www.undp.org/asia-pacific/press-releases/ai-risks-sparking-new-era-divergence-development-gaps-between-countries-widen-undp-report-finds
3. https://www.undp.org/asia-pacific/blog/next-great-divergence-how-ai-could-split-world-again-if-we-dont-intervene
4. https://www.aljazeera.com/news/2025/12/2/ai-threatens-to-widen-inequality-among-states-un
5. https://www.undp.org/asia-pacific/next-great-divergence
6. https://www.eco-business.com/press-releases/ai-risks-spark-new-era-of-divergence-as-development-gaps-widen-undp-report/

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Introduction

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In the hyperconnected world, cyber incidents can no longer be treated as sporadic disruptions; such incidents have become an everyday occurrence. The attack landscape today is very consequential and shows significant multiplication in its frequency, with ransomware attacks incapacitating a health system, phishing attacks hitting a financial institution, or state-sponsored attacks on critical infrastructures. Towards counteracting such threats, traditional ways alone are not enough, they gravely rely on manual research and human intellect. Attackers exercise speed, scale, and stealth, and defenders are always four steps behind. With such a widening gap, it is deemed necessary to facilitate incident response and crisis management with the intervention of automation and artificial intelligence (AI) for faster detection, context-driven decision-making, and collaborative response beyond human capabilities.

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Incident Response and Crisis Management

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Incident response is the structured way in which organisations deal with responding to detecting, segregating, and recovering from security incidents. Crisis management takes this even further, dealing not only with the technical fallout of a breach but also its business, reputation, and regulatory implications. Echelon used to depend on manual teams of people sorting through logs, cross-correlating alarms, and generating responses, a paradigm effective for small numbers but quickly inadequate in today's threat climate. Today's opponents attack at machine speed, employing automation to launch attacks. Under such circumstances, responding with slow, manual methods means delay and draconian consequences. The AI and automation introduction is a paradigm change that allows organisations to equate the pace and precision with which attackers initiate attacks in responding to incidents.

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How Automation Reinvents Response

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Cybercrime automation liberates cybercrime analysts from boring and repetitive tasks that consume time. An analyst manually detects potential threats from a list of hundreds each day, while automated systems sift through noise and focus only on genuine threats. Malware can automatically cause infected computers to be disconnected from the network to avoid spreading or may automatically have its suspicious account permissions removed without human intervention. The security orchestration systems move further by introducing playbooks, predefined steps describing how incidents of a certain type (e.g., phishing attempts or malware infections) should be handled. This ensures fast containment while ensuring consistency and minimising human error amid the urgency of dealing with thousands of alerts.

Automation takes care of threat detection, prioritisation, and containment, allowing human analysts to refocus on more complex decision-making. Instead of drowning in the sea of trivial alerts, security teams can now devote their efforts to more strategic areas: threat hunting and longer-term resilience. Automation is a strong tool of defence, cutting response times down from hours to minutes.

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The Intelligence Layer: AI in Action

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If automation provides speed, then AI is what allows the brain to be intelligent and flexible. Working with old and fixed-rule systems, AI-enabled solutions learn from experiences, adapt to changes in threats, and discover hidden patterns of which human analysts themselves would be unaware. For instance, machine learning algorithms identify normal behaviour on a corporate network and raise alerts on any anomalies that could indicate an insider attack or an advanced persistent threat. Similarly, AI systems sift through global threat intelligence to predict likely attack vectors so organisations can have their vulnerabilities fixed before they are exploited.

AI also boosts forensic analysis. Instead of searching forever for clues, analysts let AI-driven systems trace back to the origin of an event, identify vulnerabilities exploited by attackers, and flag systems that are still under attack. During a crisis, AI is a decision support that predicts outcomes of different scenarios and recommends the best response. In response to a ransomware attack, for example, based on context, AI might advise separating a single network segment or restoring from backup or alerting law enforcement. 

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Real-World Applications and Case Studies

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Already, this mitigation has been provided in the form of real-world applications of automation and AI. Consider, for example, IBM Watson for Cybersecurity, which has been applied in analysing unstructured threat intelligence and providing analysts with actionable results in minutes, rather than days. Like this, systems driven by AI in DARPA's Cyber Grand Challenge demonstrated the ability to automatically identify an instant vulnerability, patch it, and reveal the potential of a self-healing system. AI-powered fraud detection systems stop suspicious transactions in the middle of their execution and work all night to prevent losses. What is common in all these examples is that automation and AI lessen human effort, increase accuracy, and in the event of a cyberattack, buy precious time.

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Challenges and Limitations

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While promising, the technology is still not fully mature. The quality of an AI system is highly dependent on the training data provided; poor training can generate false positives that drown teams or worse false negatives that allow attackers to proceed unabated. Attackers have also started targeting AI itself by poisoning datasets or designing malware that does not get detected. Aside from risks that are more technical, the operational and financial costs involved in implementing advanced AI-based systems present expensive threats to any company. Organisations will have to make expenditures not only on technology but also for the training of staff to best utilise these tools. There are some ethical and privacy issues to consider as well because systems may be processing sensitive personal data, so global data protection laws such as the GDPR or India's DPDP Act could come into conflict.

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Creating a Human-AI Collaboration

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The future is not going to be one of substitution by machines but of creating human-AI synergy. Automation can do the drudgery, AI can provide smarts, and human professionals can use judgment, imagination, and ethical decisions. One would want to build AI-fuelled Security Operations Centres where technology and human experts work in tandem. Continuous training must be provided to AI models to reduce false alarms and make them most resistant against adversarial attacks. Regular conduct of crisis drills that combine AI tools and human teams can ensure preparedness for real-time events. Likewise, it is worth integrating ethical AI guidelines into security frameworks to ensure a stronger defence while respecting privacy and regulatory compliance.

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Conclusion

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Cyber-attacks are an eventuality in this modern time, but the actual impact need not be so harsh. The organisations can maintain the programmatic method of integrating automation and AI into incident response and crisis management so that the response against the very threat can be shifted from reactive firefighting to proactive resilience. Automation gives speed and efficiency while AI gives intelligence and foresight, hence putting the defenders on par and possibly exceeding the speed and sophistication of the attackers. But an utmost system without human inquisitiveness, ethical reasoning, and strategic foresight would remain imperfect. The best defence is in that human-machine relationship symbiotic system wherein automation and AI take care of how fast and how many cyber threats come in, whereas human intellect ensures that every response is aligned with larger organizational goals. This synergy is where cybersecurity resiliency will reside in the future-the defenders won't just be reacting to emergencies but will rather be driving the way.

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References

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• https://www.sisainfosec.com/blogs/incident-response-automation/
• https://stratpilot.ai/role-of-ai-in-crisis-management-and-its-critical-importance/
• https://www.juvare.com/integrating-artificial-intelligence-into-crisis-management/
• https://www.motadata.com/blog/role-of-automation-in-incident-management/

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Executive Summary:

Recently, CyberPeace faced a case involving a fraudulent Android application imitating the Punjab National Bank (PNB). The victim was tricked into downloading an APK file named "PNB.apk" via WhatsApp. After the victim installed the apk file, it resulted in unauthorized multiple transactions on multiple credit cards.

Case Study: The Attack: Social Engineering Meets Malware

The incident started when the victim clicked on a Facebook ad for a PNB credit card. After submitting basic personal information, the victim receives a WhatsApp call from a profile displaying the PNB logo. The attacker, posing as a bank representative, fakes the benefits and features of the Credit Card and convinces the victim to install an application named PNB.apk.  The so called bank representative sent the app through WhatsApp, claiming it would expedite the credit card application. The application was installed in the mobile device as a customer care application. It asks for permissions such as  to send or view SMS messages. The application opens only if the user provides this permission. 

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It extracts the credit card details from the user such as Full Name, Mobile Number, complain, on further pages irrespective of  Refund, Pay or Other. On further processing, it asks for other information such as credit card number, expiry date and cvv number. 

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Now the scammer has access to all the details of the credit card information, access to read or view the sms to intercept OTPs. 

The victim, thinking they were securely navigating the official PNB website, was unaware that the malware was granting the hacker remote access to their phone. This led to ₹4 lakhs worth of 11 unauthorized transactions across three credit cards.

The Investigation & Analysis:

Upon receiving the case through CyberPeace helpline, the CyberPeace Research Team acted swiftly to neutralize the threat and secure the victim’s device. Using a secure remote access tool, we gained control of the phone with the victim’s consent. Our first step was identifying and removing the malicious "PNB.apk" file, ensuring no residual malware was left behind. 

Next, we implemented crucial cyber hygiene practices:

1. Revoking unnecessary permissions – to prevent further unauthorized access.
2. Running antivirus scans – to detect any remaining threats.
3. Clearing sensitive data caches – to remove stored credentials and tokens.

The CyberPeace Helpline team assisted the victim to report the fraud to the National Cybercrime Portal and helpline (1930) and promptly blocked the compromised credit cards.

The technical analysis for the app was taken ahead and by using the md5 hash file id. This app was marked as malware in virustotal and it has all the permissions such as Send/Receive/Read SMS, System Alert Window.  

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In the similar way, we have found another application in the name of “Axis Bank” which is circulated through whatsapp which is having similar permission access and the details found in virus total are as follows:

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Recommendations:

This case study implies the increasingly sophisticated methods used by cybercriminals, blending social engineering with advanced malware. Key lessons include:

• Be vigilant when downloading the applications, even if they appear to be from legitimate sources. It is advised to install any application after checking through an application store and not through any social media. 
• Always review app permissions before granting access.
• Verify the identity of anyone claiming to represent financial institutions.
• Use remote access tools responsibly for effective intervention during a cyber incident.

By acting quickly and following the proper protocols, we successfully secured the victim’s device and prevented further financial loss.

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