#FactCheck-AI-Generated Graphic Falsely Claims Union Education Minister Dharmendra Pradhan Will Resign on July 22.

Introduction

A Reuters investigation has uncovered an elephant in the room regarding Meta Platforms' internal measures to address online fraud and illicit advertising. The confidential documents that Reuters reviewed disclosed that Meta was planning to generate approximately 10% of its 2024 revenue, i.e., USD 16 billion, from ads related to scams and prohibited goods. The findings point out a disturbing paradox: on the one hand, Meta is a vocal advocate for digital safety and platform integrity, while on the other hand, the internal logs of the company indicate the existence of a very large area allowing the shunning of fraudulent advertisement activities that exploit users throughout the world. 

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The Scale of the Problem

Internal Meta projections show that its platforms, Facebook, Instagram, and WhatsApp, are displaying a staggering 15 billion scam ads per day combined. The advertisements include deceitful e-commerce promotions, fake investment schemes, counterfeit medical products, and unlicensed gambling platforms.

Meta has developed sophisticated detection tools, but even then, the system does not catch the advertisers until they are 95% certain to be fraudsters. By having at least that threshold for removing an ad, the company is unlikely to lose much money. As a result, instead of turning the fraud adjacent advertisers down, it charges them higher ad rates, which is the strategy they call “penalty bids” internally. 

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Internal Acknowledgements & Business Dependence

Internal documents that date between 2021 and 2025 reveal that the financial, safety, and lobbying divisions of Meta were cognizant of the enormity of revenues generated from scams. One of the 2025 strategic papers even describes this revenue source as "violating revenue," which implies that it includes ads that are against Meta's policies regarding scams, gambling, sexual services, and misleading healthcare products.

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The company's top executives consider the cost-benefit scenario of stricter enforcement. According to a 2024 internal projection, Meta's half-yearly earnings from high-risk scam ads were estimated at USD 3.5 billion, whereas regulatory fines for such violations would not exceed USD 1 billion, thus making it a tolerable trade-off from a commercial viewpoint. At the same time, the company intends to scale down scam ad revenue gradually, thus from 10.1% in 2024 to 7.3% by 2025, and 6% by 2026; however, the documents also reveal a planned slowdown in enforcement to avoid "abrupt reductions" that could affect business forecasts.

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Algorithmic Amplification of Scams

One of the most alarming situations is the fact that Meta's own advertising algorithms amplify scam content. It has been reported that users who click on fraudulent ads are more likely to see other similar ads, as the platform's personalisation engine assumes user "interest."

This scenario creates a self-reinforcing feedback loop where the user engagement with scam content dictates the amount of such content being displayed. Thus, a digital environment is created which encourages deceptive engagement and consequently, user trust is eroded and systemic risk is amplified.

An internal presentation in May 2025 was said to put a number on how deeply the platform's ad ecosystem was intertwined with the global fraud economy, estimating that one-third of the scams that succeeded in the U.S. were due to advertising on Meta's platforms.

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Regulatory & Legal Implications

The disclosures arrived at the same time as the US and UK governments started to closely check the company's activities more than ever before.

• The U.S. Securities and Exchange Commission (SEC) is said to be looking into whether Meta has had any part in the promotion of fraudulent financial ads.
• The UK’s Financial Conduct Authority (FCA) found that Meta’s platforms were the main sources of scams related to online payments and claimed that the amount of money lost was more than all the other social platforms combined in 2023.

Meta’s spokesperson, Andy Stone, at first denied the accusations, stating that the figures mentioned in the leak were “rough and overly-inclusive”; nevertheless, he conceded that the company’s consistent efforts toward enforcement had negatively impacted revenue and would continue to do so.

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Operational Challenges & Policy Gaps

The internal documents also reveal the weaknesses in Meta's day-to-day operations when it comes to the implementation of its own policies.

• Because of the large number of employees laid off in 2023, the whole department that dealt with advertiser-brand impersonation was said to have been dissolved.
• Scam ads were categorised as a "low severity" issue, which was more of a "bad user experience" than a critical security risk.
• At the end of 2023, users were submitting around 100,000 legitimate scam reports per week, of which Meta dismissed or rejected 96%.

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Human Impact: When Fraud Becomes Personal

The financial and ethical issues have tangible human consequences. The Reuters investigation documented multiple cases of individuals defrauded through hijacked Meta accounts.

One striking example involves a Canadian Air Force recruiter, whose hacked Facebook account was used to promote fake cryptocurrency schemes. Despite over a hundred user reports, Meta failed to act for weeks, during which several victims, including military colleagues, lost tens of thousands of dollars.

The case underscores not just platform negligence, but also the difficulty of law enforcement collaboration. Canadian authorities confirmed that funds traced to Nigerian accounts could not be recovered due to jurisdictional barriers, a recurring issue in transnational cyber fraud.

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Ethical and Cybersecurity Implications

The research has questioned extremely important things at least from the perspective of cyber policy:

1. Platform Accountability: Meta, by its practice, is giving more importance to the monetary aspect rather than the truth, and in this way, it is going against the principles of responsible digital governance.
2. Transparency in Ad Ecosystems: The lack of transparency in digital advertising systems makes it very easy for dishonest actors to use automated processes with very little supervision.
3. Algorithmic Responsibility: The use of algorithms that impact the visibility of misleading content and targeting can be considered the direct involvement of the algorithms in the fraud.
4. Regulatory Harmonisation: The presence of different and disconnected enforcement frameworks across jurisdictions is a drawback to the efforts in dealing with cross-border cybercrime.
5. Public Trust: Users’ trust in the digital world is mainly dependent on the safety level they see and the accountability of the companies.

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Conclusion

Meta’s records show a very unpleasant mix of profit, laxity, and failure in the policy area concerning scam-related ads. The platform’s readiness to accept and even profit from fraudulent players, though admitting the damage they cause, calls for an immediate global rethinking of advertising ethics, regulatory enforcement, and algorithmic transparency.

With the expansion of its AI-driven operations and advertising networks, protecting the users of Meta must evolve from being just a public relations goal to being a core business necessity, thus requiring verifiable accountability measures, independent audits, and regulatory oversight. It is an undeniable fact that there are billions of users who count on Meta’s platforms for their right to digital safety, which is why this right must be respected and enforced rather than becoming optional.

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References

• https://www.reuters.com/investigations/meta-is-earning-fortune-deluge-fraudulent-ads-documents-show-2025-11-06/?utm_source=chatgpt.com
• https://www.indiatoday.in/technology/news/story/leaked-docs-claim-meta-made-16-billion-from-scam-ads-even-after-deleting-134-million-of-them-2815183-2025-11-07

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Executive Summary:

In late 2024 an Indian healthcare provider experienced a severe cybersecurity attack that demonstrated how powerful AI ransomware is. This blog discusses the background to the attack, how it took place and the effects it caused (both medical and financial), how organisations reacted, and the final result of it all, stressing on possible dangers in the healthcare industry with a lack of sufficiently adequate cybersecurity measures in place. The incident also interrupted the normal functioning of business and explained the possible economic and image losses from cyber threats. Other technical results of the study also provide more evidence and analysis of the advanced AI malware and best practices for defending against them. 

1. Introduction

The integration of artificial intelligence (AI) in cybersecurity has revolutionised both defence mechanisms and the strategies employed by cybercriminals. AI-powered attacks, particularly ransomware, have become increasingly sophisticated, posing significant threats to various sectors, including healthcare. This report delves into a case study of an AI-powered ransomware attack on a prominent Indian healthcare provider in 2024, analysing the attack's execution, impact, and the subsequent response, along with key technical findings.

2. Background 

 In late 2024, a leading healthcare organisation in India which is involved in the research and development of AI techniques fell prey to a ransomware attack that was AI driven to get the most out of it. With many businesses today relying on data especially in the healthcare industry that requires real-time operations, health care has become the favourite of cyber criminals. AI aided attackers were able to cause far more detailed and damaging attack that severely affected the operation of the provider whilst jeopardising the safety of the patient information.  

 3. Attack Execution 

The attack began with the launch of a phishing email designed to target a hospital administrator. They received an email with an infected attachment which when clicked in some cases injected the AI enabled ransomware into the hospitals network. AI incorporated ransomware was not as blasé as traditional ransomware, which sends copies to anyone, this studied the hospital’s IT network. First, it focused and targeted important systems which involved implementation of encryption such as the electronic health records and the billing departments. 

The fact that the malware had an AI feature allowed it to learn and adjust its way of propagation in the network, and prioritise the encryption of most valuable data. This accuracy did not only increase the possibility of the potential ransom demand but also it allowed reducing the risks of the possibility of early discovery. 

 4. Impact 

•  The consequences of the attack were immediate and severe: The consequences of the attack were immediate and severe.
•  Operational Disruption: The centralization of important systems made the hospital cease its functionality through the acts of encrypting the respective components. Operations such as surgeries, routine medical procedures and admitting of patients were slowed or in some cases referred to other hospitals. 
•  Data Security: Electronic patient records and associated billing data became off-limit because of the vulnerability of patient confidentiality. The danger of data loss was on the verge of becoming permanent, much to the concern of both the healthcare provider and its patients. 
•  Financial Loss: The attackers asked for 100 crore Indian rupees (approximately 12 USD million) for the decryption key. Despite the hospital not paying for it, there were certain losses that include the operational loss due to the server being down, loss incurred by the patients who were affected in one way or the other, loss incurred in responding to such an incident and the loss due to bad reputation. 

5. Response

As soon as the hotel’s management was informed about the presence of ransomware, its IT department joined forces with cybersecurity professionals and local police. The team decided not to pay the ransom and instead recover the systems from backup. Despite the fact that this was an ethically and strategically correct decision, it was not without some challenges. Reconstruction was gradual, and certain elements of the patients’ records were permanently erased. 

 In order to avoid such attacks in the future, the healthcare provider put into force several organisational and technical actions such as network isolation and increase of cybersecurity measures. Even so, the attack revealed serious breaches in the provider’s IT systems security measures and protocols. 

6. Outcome

The attack had far-reaching consequences:

• Financial Impact: A healthcare provider suffers a lot of crashes in its reckoning due to substantial service disruption as well as bolstering cybersecurity and compensating patients. 
•  Reputational Damage: The leakage of the data had a potential of causing a complete loss of confidence from patients and the public this affecting the reputation of the provider. This, of course, had an effect on patient care, and ultimately resulted in long-term effects on revenue as patients were retained. 
•  Industry Awareness: The breakthrough fed discussions across the country on how to improve cybersecurity provisions in the healthcare industry. It woke up the other care providers to review and improve their cyber defence status. 

7. Technical Findings

The AI-powered ransomware attack on the healthcare provider revealed several technical vulnerabilities and provided insights into the sophisticated mechanisms employed by the attackers. These findings highlight the evolving threat landscape and the importance of advanced cybersecurity measures.

7.1 Phishing Vector and Initial Penetration

• Sophisticated Phishing Tactics: The phishing email was crafted with precision, utilising AI to mimic the communication style of trusted contacts within the organisation. The email bypassed standard email filters, indicating a high level of customization and adaptation, likely due to AI-driven analysis of previous successful phishing attempts.
• Exploitation of Human Error: The phishing email targeted an administrative user with access to critical systems, exploiting the lack of stringent access controls and user awareness. The successful penetration into the network highlighted the need for multi-factor authentication (MFA) and continuous training on identifying phishing attempts.

7.2 AI-Driven Malware Behavior

• Dynamic Network Mapping: Once inside the network, the AI-powered malware executed a sophisticated mapping of the hospital's IT infrastructure. Using machine learning algorithms, the malware identified the most critical systems—such as Electronic Health Records (EHR) and the billing system—prioritising them for encryption. This dynamic mapping capability allowed the malware to maximise damage while minimising its footprint, delaying detection.
• Adaptive Encryption Techniques: The malware employed adaptive encryption techniques, adjusting its encryption strategy based on the system's response. For instance, if it detected attempts to isolate the network or initiate backup protocols, it accelerated the encryption process or targeted backup systems directly, demonstrating an ability to anticipate and counteract defensive measures.
• Evasive Tactics: The ransomware utilised advanced evasion tactics, such as polymorphic code and anti-forensic features, to avoid detection by traditional antivirus software and security monitoring tools. The AI component allowed the malware to alter its code and behaviour in real time, making signature-based detection methods ineffective.

7.3 Vulnerability Exploitation

• Weaknesses in Network Segmentation: The hospital’s network was insufficiently segmented, allowing the ransomware to spread rapidly across various departments. The malware exploited this lack of segmentation to access critical systems that should have been isolated from each other, indicating the need for stronger network architecture and micro-segmentation.
• Inadequate Patch Management: The attackers exploited unpatched vulnerabilities in the hospital’s IT infrastructure, particularly within outdated software used for managing patient records and billing. The failure to apply timely patches allowed the ransomware to penetrate and escalate privileges within the network, underlining the importance of rigorous patch management policies.

7.4 Data Recovery and Backup Failures

• Inaccessible Backups: The malware specifically targeted backup servers, encrypting them alongside primary systems. This revealed weaknesses in the backup strategy, including the lack of offline or immutable backups that could have been used for recovery. The healthcare provider’s reliance on connected backups left them vulnerable to such targeted attacks.
• Slow Recovery Process: The restoration of systems from backups was hindered by the sheer volume of encrypted data and the complexity of the hospital’s IT environment. The investigation found that the backups were not regularly tested for integrity and completeness, resulting in partial data loss and extended downtime during recovery.

7.5 Incident Response and Containment

• Delayed Detection and Response: The initial response was delayed due to the sophisticated nature of the attack, with traditional security measures failing to identify the ransomware until significant damage had occurred. The AI-powered malware’s ability to adapt and camouflage its activities contributed to this delay, highlighting the need for AI-enhanced detection and response tools.
• Forensic Analysis Challenges: The anti-forensic capabilities of the malware, including log wiping and data obfuscation, complicated the post-incident forensic analysis. Investigators had to rely on advanced techniques, such as memory forensics and machine learning-based anomaly detection, to trace the malware’s activities and identify the attack vector.

8. Recommendations Based on Technical Findings

To prevent similar incidents, the following measures are recommended:

1. AI-Powered Threat Detection: Implement AI-driven threat detection systems capable of identifying and responding to AI-powered attacks in real time. These systems should include behavioural analysis, anomaly detection, and machine learning models trained on diverse datasets.
2. Enhanced Backup Strategies: Develop a more resilient backup strategy that includes offline, air-gapped, or immutable backups. Regularly test backup systems to ensure they can be restored quickly and effectively in the event of a ransomware attack.
3. Strengthened Network Segmentation: Re-architect the network with robust segmentation and micro-segmentation to limit the spread of malware. Critical systems should be isolated, and access should be tightly controlled and monitored.
4. Regular Vulnerability Assessments: Conduct frequent vulnerability assessments and patch management audits to ensure all systems are up to date. Implement automated patch management tools where possible to reduce the window of exposure to known vulnerabilities.
5. Advanced Phishing Defences: Deploy AI-powered anti-phishing tools that can detect and block sophisticated phishing attempts. Train staff regularly on the latest phishing tactics, including how to recognize AI-generated phishing emails.

9. Conclusion

The AI empowered ransomware attack on the Indian healthcare provider in 2024 makes it clear that the threat of advanced cyber attacks has grown in the healthcare facilities. Sophisticated technical brief outlines the steps used by hackers hence underlining the importance of ongoing active and strong security. This event is a stark message to all about the importance of not only remaining alert and implementing strong investments in cybersecurity but also embarking on the formulation of measures on how best to counter such incidents with limited harm. AI is now being used by cybercriminals to increase the effectiveness of the attacks they make and it is now high time all healthcare organisations ensure that their crucial systems and data are well protected from such attacks.

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Introduction

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India’s new Policy for Data Sharing from the National Transport Repository (NTR) released by the Ministry of Road Transport and Highways (MoRTH) in August, 2025, can be seen as a constitutional turning point and a milestone in administrative efficiency. The state has established an unprecedentedly large unified infrastructure by combining the records of 390 million vehicles, 220 million driver’s licenses, and the streams from the e-challan, e-DAR, and FASTag systems. Its supporters hail its promise of private-sector innovation, data-driven research, and smooth governance. However, there is a troubling paradox beneath this facade of advancement: the very structures intended to improve citizen mobility may simultaneously strengthen widespread surveillance. Without strict protections, the NTR runs the risk of violating the constitutional trifecta of need, proportionality, and legality as stated in Puttaswamy v. UOI, which brings to light important issues at the nexus of liberty, law, and data.  

The other pertinent question to be addressed is as India unifies one of its comprehensive datasets on citizen mobility the question becomes more pressing: while motorised citizens are now in the spotlight for accountability, what about the millions of other datasets that are still dispersed, unregulated, and shared inconsistently in the areas of health, education, telecom, and welfare?

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The Legal Backdrop

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MoRTH grounds its new policy in Sections 25A and 62B of the Motor Vehicles Act, 1988. Data is consolidated into a single repository since states are required by Section 136A to electronically monitor road safety. According to the policy, it complies with the Digital Personal Data Protection Act, 2023. 

The DPDP Act itself, however, is rife with state exclusions, particularly Sections 7 and 17, which give government organisations access to personal information for “any function under any law” or for law enforcement purposes. This is where the constitutional issue lies. Prior judicial supervision, warrants, or independent checks are not necessary. With legislative approval, MoRTH is essentially creating a national vehicle database without any constitutional protections. 

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Data, Domination and the New Privacy Paradigm

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As an efficiency and governance reform, VAHAN, SARATHI, e-challan, eDAR, and FASTag are being consolidated into a single National Transport Repository (NTR). However, centralising extensive mobility and identity-linked records on a large scale is more than just a technical advancement; it also changes how the state and private life interact. The NTR must therefore be interpreted through a more comprehensive privacy paradigm, one that acknowledges that data aggregation is a means of enhancing administrative capacity and has the potential to develop into a long-lasting tool of social control and surveillance unless both technological and constitutional restrictions are placed at the same time. 

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Two recent doctrinal developments sharpen this concern. First, the Supreme Court’s foundational ruling that privacy is a fundamental right remains the constitutional lodestar, any state interference must satisfy legality, necessity and proportionality (KS Puttaswamy & Anr. vs UOI). Second, as seen by the court’s most recent refusals to normalise ongoing, warrantless location monitoring, such as the ruling overturning bail requirements that required accused individuals to provide a Google maps pin, as movement tracking necessitates closer examination (Frank Vitus v. Narcotics Control Bureau & Ors.,).When taken as a whole, these authorities maintain that unrestricted, ongoing access to mobility and toll-transaction records is a constitutional issue and cannot be handled as an administrative convenience. 

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Structural Fault Lines in the NTR Framework

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Fundamentally, the NTR policy generates structural vulnerabilities by providing nearly unrestricted access through APIs and even mass transfers on physical media to a broad range of parties, including insurance companies, law enforcement, and intelligence services. This design undermines constitutional protections in three ways: first, it makes it possible to draw conclusions about private life patterns that the Supreme Court has identified as one of the most sensitive data categories by exposing rich mobility trails like FASTag logs and vehicle-linked identities; Second, it allows bulk datasets to circulate outside the ministry’s custodial boundary, which creates the possibility of function creep, secondary use, and monetisation risks reminiscent of the bulk sharing regime that the government itself once abandoned; and third, it introduces coercive exclusion by tying private sector access to Aadhaar-based OTP consent. 

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Reference

• https://morth.nic.in/sites/default/files/circulars_document/Data-Sharing-policy-20082025-merged.pdf

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