#FactCheck: Fake Phishing link on Modi Government is giving ₹5,000 to all Indian citizens via UPI

Introduction

The unprecedented rise of social media, challenges with regional languages, and the heavy use of messaging apps like WhatsApp have all led to an increase in misinformation in India. False stories spread quickly and can cause significant harm, like political propaganda and health-related mis/misinformation. Programs that teach people how to use social media responsibly and attempt to check facts are essential, but they do not always connect with people deeply. Reading stories, attending lectures, and using tools that check facts are standard passive learning methods used in traditional media literacy programs. 

Adding game-like features to non-game settings is called "gamification," it could be a new and interesting way to answer this question. Gamification involves engaging people by making them active players instead of just passive consumers of information. Research shows that interactive learning improves interest, thinking skills, and memory. People can learn to recognise fake news safely by turning fact-checking into a game before encountering it in real life. A study by Roozenbeek and van der Linden (2019) showed that playing misinformation games can significantly enhance people's capacity to recognise and avoid false information.

Several misinformation-related games have been successfully implemented worldwide:

• The Bad News Game – This browser-based game by Cambridge University lets players step into the shoes of a fake news creator, teaching them how misinformation is crafted and spread (Roozenbeek & van der Linden, 2019).
• Factitious – A quiz game where users swipe left or right to decide whether a news headline is real or fake (Guess et al., 2020).
• Go Viral! – A game designed to inoculate people against COVID-19 misinformation by simulating the tactics used by fake news peddlers (van der Linden et al., 2020).

For programs to effectively combat misinformation in India, they must consider factors such as the responsible use of smartphones, evolving language trends, and common misinformation patterns in the country. Here are some key aspects to keep in mind:

1. Vernacular Languages

There should be games in Hindi, Tamil, Bengali, Telugu, and other major languages since that is how rumours spread in different areas and diverse cultural contexts. AI voice conversation and translation can help reduce literacy differences. Research shows that people are more likely to engage with and trust information in their native language (Pennycook & Rand, 2019).

2. Games Based on WhatsApp

Interactive tests and chatbot-powered games can educate consumers directly within the app they use most frequently since WhatsApp is a significant hub for false information. A game with a WhatsApp-like interface where players may feel like they are in real life, having to decide whether to avoid, check the facts of, or forward messages that are going viral could be helpful in India.

3. Detecting False Information

As part of a mobile-friendly game, players can pretend to be reporters or fact-checkers and have to prove stories that are going viral. They can do the same with real-life tools like reverse picture searches or reliable websites that check facts. Research shows that doing interactive tasks to find fake news makes people more aware of it over time (Lewandowsky et al., 2017).

4. Reward-Based Participation

Participation could be increased by providing rewards for finishing misleading challenges, such as badges, diplomas, or even incentives on mobile data. This might be easier to do if there are relationships with phone companies. Reward-based learning has made people more interested and motivated in digital literacy classes (Deterding et al., 2011).

5. Universities and Schools 

Educational institutions can help people spot false information by adding game-like elements to their lessons. Hamari et al. (2014) say that students are more likely to join and remember what they learn when there are competitive and interactive parts to the learning. Misinformation games can be used in media studies classes at schools and universities by using models to teach students how to check sources, spot bias, and understand the psychological tricks that misinformation campaigns use.

What Artificial Intelligence Can Do for Gamification

Artificial intelligence can tailor learning experiences to each player in false games. AI-powered misinformation detection bots could lead participants through situations tailored to their learning level, ensuring they are consistently challenged. Recent natural language processing (NLP) developments enable AI to identify nuanced misinformation patterns and adjust gameplay accordingly (Zellers et al., 2019). This could be especially helpful in India, where fake news is spread differently depending on the language and area.

Possible Opportunities

Augmented reality (AR) scavenger hunts for misinformation, interactive misinformation events, and educational misinformation tournaments are all examples of games that help fight misinformation. India can help millions, especially young people, think critically and combat the spread of false information by making media literacy fun and interesting. Using Artificial Intelligence (AI) in gamified treatments for misinformation could be a fascinating area of study in the future. AI-powered bots could mimic real-time cases of misinformation and give quick feedback, which would help students learn more.

Problems and Moral Consequences

While gaming is an interesting way to fight false information, it also comes with some problems that you should think about:

• Ethical Concerns: Games that try to imitate how fake news spreads must ensure players do not learn how to spread false information by accident.
• Scalability: Although worldwide misinformation initiatives exist, developing and expanding localised versions for India's varied language and cultural contexts provide significant challenges.
• Assessing Impact: There is a necessity for rigorous research approaches to evaluate the efficacy of gamified treatments in altering misinformation-related behaviours, keeping cultural and socio-economic contexts in the picture.

Conclusion 

A gamified approach can serve as an effective tool in India's fight against misinformation. By integrating game elements into digital literacy programs, it can encourage critical thinking and help people recognize misinformation more effectively. The goal is to scale these efforts, collaborate with educators, and leverage India's rapidly evolving technology to make fact-checking a regular practice rather than an occasional concern.

As technology and misinformation evolve, so must the strategies to counter them. A coordinated and multifaceted approach, one that involves active participation from netizens, strict platform guidelines, fact-checking initiatives, and support from expert organizations that proactively prebunk and debunk misinformation can be a strong way forward.

References

• Deterding, S., Dixon, D., Khaled, R., & Nacke, L. (2011). From game design elements to gamefulness: defining "gamification". Proceedings of the 15th International Academic MindTrek Conference.
• Guess, A., Nagler, J., & Tucker, J. (2020). Less than you think: Prevalence and predictors of fake news dissemination on Facebook. Science Advances.
• Hamari, J., Koivisto, J., & Sarsa, H. (2014). Does gamification work?—A literature review of empirical studies on gamification. Proceedings of the 47th Hawaii International Conference on System Sciences.
• Lewandowsky, S., Ecker, U. K., & Cook, J. (2017). Beyond misinformation: Understanding and coping with the “post-truth” era. Journal of Applied Research in Memory and Cognition.
• Pennycook, G., & Rand, D. G. (2019). Fighting misinformation on social media using “accuracy prompts”. Nature Human Behaviour.
• Roozenbeek, J., & van der Linden, S. (2019). The fake news game: actively inoculating against the risk of misinformation. Journal of Risk Research.
• van der Linden, S., Roozenbeek, J., Compton, J. (2020). Inoculating against fake news about COVID-19. Frontiers in Psychology.
• Zellers, R., Holtzman, A., Rashkin, H., Bisk, Y., Farhadi, A., Roesner, F., & Choi, Y. (2019). Defending against neural fake news. Advances in Neural Information Processing Systems.

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The race for global leadership in AI is in full force. As China and the US emerge as the ‘AI Superpowers’ in the world, the world grapples with the questions around AI governance, ethics, regulation, and safety. Some are calling this an ‘AI Arms Race.’ Most of the applications of these AI systems are in large language models for commercial use or military applications. Countries like Germany, Japan, France, Singapore, and India are now participating in this race and are not mere spectators. 

The Government of India’s Ministry of Electronics and Information Technology (MeitY) has launched the IndiaAI Mission, an umbrella program for the use and development of AI technology. This MeitY initiative lays the groundwork for supporting an array of AI goals for the country. The government has allocated INR 10,300 crore for this endeavour. This mission includes pivotal initiatives like the IndiaAI Compute Capacity, IndiaAI Innovation Centre (IAIC), IndiaAI Datasets Platform, IndiaAI Application Development Initiative, IndiaAI FutureSkills, IndiaAI Startup Financing, and Safe & Trusted AI.

There are several challenges and opportunities that India will have to navigate and capitalize on to become a significant player in the global AI race. The various components of India’s ‘AI Stack’ will have to work well in tandem to create a robust ecosystem that yields globally competitive results. The IndiaAI mission focuses on building large language models in vernacular languages and developing compute infrastructure. There must be more focus on developing good datasets and research as well.

Resource Allocation and Infrastructure Development

The government is focusing on building the elementary foundation for AI competitiveness. This includes the procurement of AI chips and compute capacity, about 10,000 graphics processing units (GPUs), to support India’s start-ups, researchers, and academics. These GPUs have been strategically distributed, with 70% being high-end newer models and the remaining 30% comprising lower-end older-generation models. This approach ensures that a robust ecosystem is built, which includes everything from cutting-edge research to more routine applications. A major player in this initiative is Yotta Data Services, which holds the largest share of 9,216 GPUs, including 8,192 Nvidia H100s. Other significant contributors include Amazon AWS's managed service providers, Jio Platforms, and CtrlS Datacenters. 

Policy Implications: Charting a Course for Tech Sovereignty and Self-reliance

With this government initiative, there is a concerted effort to develop indigenous AI models and reduce tech dependence on foreign players. There is a push to develop local Large Language Models and domain-specific foundational models, creating AI solutions that are truly Indian in nature and application. Many advanced chip manufacturing takes place in Taiwan, which has a looming China threat. India’s focus on chip procurement and GPUs speaks to a larger agenda of self-reliance and sovereignty, keeping in mind the geopolitical calculus. This is an important thing to focus on, however, it must not come at the cost of developing the technological ‘know-how’ and research. 

Developing AI capabilities at home also has national security implications. When it comes to defence systems, control over AI infrastructure and data becomes extremely important. The IndiaAI Mission will focus on safe and trusted AI, including developing frameworks that fit the Indian context. It has to be ensured that AI applications align with India's security interests and can be confidently deployed in sensitive defence applications.

The big problem here to solve here is the ‘data problem.’ There must be a focus on developing strategies to mitigate the data problem that disadvantages the Indian AI ecosystem. Some data problems are unique to India, such as generating data in local languages. While other problems are the ones that appear in every AI ecosystem development lifecycle namely generating publicly available data and licensed data. India must strengthen its ‘Digital Public Infrastructure’ and data commons across sectors and domains.

India has proposed setting up the India Data Management Office to serve as India’s data regulator as part of its draft National Data Governance Framework Policy. The MeitY IndiaAI expert working group report also talked about operationalizing the India Datasets Platform and suggested the establishment of data management units within each ministry.

Economic Impact: Growth and Innovation

The government’s focus on technology and industry has far-reaching economic implications. There is a push to develop the AI startup ecosystem in the country. The IndiaAI mission heavily focuses on inviting ideas and projects under its ambit. The investments will strengthen the IndiaAI startup financing system, making it easier for nascent AI businesses to obtain capital and accelerate their development from product to market. Funding provisions for industry-led AI initiatives that promote social impact and stimulate innovation and entrepreneurship are also included in the plan. The government press release states, "The overarching aim of this financial outlay is to ensure a structured implementation of the IndiaAI Mission through a public-private partnership model aimed at nurturing India’s AI innovation ecosystem.”

The government also wants to establish India as a hub for sustainable AI innovation and attract top AI talent from across the globe. One crucial aspect that needs to be worked on here is fostering talent and skill development. India has a unique advantage, that is, top-tier talent in STEM fields. Yet we suffer from a severe talent gap that needs to be addressed on a priority basis. Even though India is making strides in nurturing AI talents, out-migration of tech talent is still a reality. Once the hardware manufacturing “goods-side” of economics transitions to service delivery in the field of AI globally, India will need to be ready to deploy its talent. Several structural and policy interfaces, like the New Education Policy and industry-academic partnership frameworks, allow India to capitalize on this opportunity. 

India’s talent strategy must be robust and long-term, focusing heavily on multi-stakeholder engagement. The government has a pivotal role here by creating industry-academia interfaces and enabling tech hubs and innovation parks.

India's Position in the Global AI Race

India’s foreign policy and geopolitical standpoint have been one of global cooperation. This must not change when it comes to AI. Even though this has been dubbed as the “AI Arms Race,” India should encourage worldwide collaboration on AI R&D through collaboration with other countries in order to strengthen its own capabilities. India must prioritise more significant open-source AI development, work with the US, Europe, Australia, Japan, and other friendly countries to prevent the unethical use of AI and contribute to the formation of a global consensus on the boundaries for AI development. 

The IndiaAI Mission will have far-reaching implications for India’s diplomatic and economic relations. The unique proposition that India comes with is its ethos of inclusivity, ethics, regulation, and safety from the get-go. We should keep up the efforts to create a powerful voice for the Global South in AI. The IndiaAI Mission marks a pivotal moment in India's technological journey. Its success could not only elevate India's status as a tech leader but also serve as a model for other nations looking to harness the power of AI for national development and global competitiveness. In conclusion, the IndiaAI Mission seeks to strengthen India's position as a global leader in AI, promote technological independence, guarantee the ethical and responsible application of AI, and democratise the advantages of AI at all societal levels.

References

• ​​Ashwini Vaishnaw to launch IndiaAI portal, 10 firms to provide 14,000 GPUs. (2025, February 17). https://www.business-standard.com/. Retrieved February 25, 2025, from https://www.business-standard.com/industry/news/indiaai-compute-portal-ashwini-vaishnaw-gpu-artificial-intelligence-jio-125021700245_1.html
• Global IndiaAI Summit 2024 being organized with a commitment to advance responsible development, deployment and adoption of AI in the country. (n.d.). https://pib.gov.in/PressReleaseIframePage.aspx?PRID=2029841
• India to Launch AI Compute Portal, 10 Firms to Supply 14,000 GPUs. (2025, February 17). apacnewsnetwork.com. https://apacnewsnetwork.com/2025/02/india-to-launch-ai-compute-portal-10-firms-to-supply-14000-gpus/
• INDIAai | Pillars. (n.d.). IndiaAI. https://indiaai.gov.in/
• IndiaAI Innovation Challenge 2024 | Software Technology Park of India | Ministry of Electronics & Information Technology Government of India. (n.d.). http://stpi.in/en/events/indiaai-innovation-challenge-2024
• IndiaAI Mission To Deploy 14,000 GPUs For Compute Capacity, Starts Subsidy Plan. (2025, February 17). www.businessworld.in. Retrieved February 25, 2025, from https://www.businessworld.in/article/indiaai-mission-to-deploy-14000-gpus-for-compute-capacity-starts-subsidy-plan-548253
• India’s interesting AI initiatives in 2024: AI landscape in India. (n.d.). IndiaAI. https://indiaai.gov.in/article/india-s-interesting-ai-initiatives-in-2024-ai-landscape-in-india
• Mehra, P. (2025, February 17). Yotta joins India AI Mission to provide advanced GPU, AI cloud services. Techcircle. https://www.techcircle.in/2025/02/17/yotta-joins-india-ai-mission-to-provide-advanced-gpu-ai-cloud-services/
• IndiaAI 2023: Expert Group Report – First Edition. (n.d.). IndiaAI. https://indiaai.gov.in/news/indiaai-2023-expert-group-report-first-edition
• Satish, R., Mahindru, T., World Economic Forum, Microsoft, Butterfield, K. F., Sarkar, A., Roy, A., Kumar, R., Sethi, A., Ravindran, B., Marchant, G., Google, Havens, J., Srichandra (IEEE), Vatsa, M., Goenka, S., Anandan, P., Panicker, R., Srivatsa, R., . . . Kumar, R. (2021). Approach Document for India. In World Economic Forum Centre for the Fourth Industrial Revolution, Approach Document for India [Report]. https://www.niti.gov.in/sites/default/files/2021-02/Responsible-AI-22022021.pdf
• Stratton, J. (2023, August 10). Those who solve the data dilemma will win the A.I. revolution. Fortune. https://fortune.com/2023/08/10/workday-data-ai-revolution/
• Suri, A. (n.d.). The missing pieces in India’s AI puzzle: talent, data, and R&D. Carnegie Endowment for International Peace. https://carnegieendowment.org/research/2025/02/the-missing-pieces-in-indias-ai-puzzle-talent-data-and-randd?lang=en
• The AI arms race. (2024, February 13). Financial Times. https://www.ft.com/content/21eb5996-89a3-11e8-bf9e-8771d5404543

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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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