#FactCheck - Misleading Video Allegedly Depicting Trampling of Indian Tri-colour in Kerala or Tamil Nadu Circulates on Social Media

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.

‍

Introduction
‍

Generative AI, particularly deepfake technology, poses significant risks to security in the financial sector. Deepfake technology can convincingly mimic voices, create lip-sync videos, execute face swaps, and carry out other types of impersonation through tools like DALL-E, Midjourney, Respeecher, Murf, etc, which are now widely accessible and have been misused for fraud. For example, in 2024, cybercriminals in Hong Kong used deepfake technology to impersonate the Chief Financial Officer of a company, defrauding it of $25 million. Surveys, including Regula’s Deepfake Trends 2024 and Sumsub reports, highlight financial services as the most targeted sector for deepfake-induced fraud.

Deepfake Technology and Its Risks to Financial Systems

India’s financial ecosystem, including banks, NBFCs, and fintech companies, is leveraging technology to enhance access to credit for households and MSMEs. The country is a leader in global real-time payments and its digital economy comprises 10% of its GDP.  However, it faces unique cybersecurity challenges. According to the RBI’s 2023-24 Currency and Finance report, banks cite cybersecurity threats, legacy systems, and low customer digital literacy as major hurdles in digital adoption. Deepfake technology intensifies risks like:

• Social Engineering Attacks: Information security breaches through phishing, vishing, etc. become more convincing with deepfake imagery and audio.
• Bypassing Authentication Protocols: Deepfake audio or images may circumvent voice and image-based authentication systems, exposing sensitive data.
• Market Manipulation: Misleading deepfake content making false claims and endorsements can harm investor trust and damage stock market performance.
• Business Email Compromise Scams: Deepfake audio can mimic the voice of a real person with authority in the organization to falsely authorize payments.
• Evolving Deception Techniques: The usage of AI will allow cybercriminals to deploy malware that can adapt in real-time to carry out phishing attacks and inundate targets with increased speed and variations. Legacy security frameworks are not suited to countering automated attacks at such a scale.

Existing Frameworks and Gaps

In 2016, the RBI introduced cybersecurity guidelines for banks, neo-banking, lending, and non-banking financial institutions, focusing on resilience measures like Board-level policies, baseline security standards, data leak prevention, running penetration tests, and mandating Cybersecurity Operations Centres (C-SOCs). It also mandated incident reporting to the RBI for cyber events. Similarly, SEBI’s Cybersecurity and Cyber Resilience Framework (CSCRF) applies to regulated entities (REs) like stock brokers, mutual funds, KYC agencies, etc., requiring policies, risk management frameworks, and third-party assessments of cyber resilience measures. While both frameworks are comprehensive, they require updates addressing emerging threats from generative AI-driven cyber fraud.

Cyberpeace Recommendations 

• AI Cybersecurity to Counter AI Cybercrime: AI-generated attacks can be designed to overwhelm with their speed and scale. Cybercriminals increasingly exploit platforms like LinkedIn, Microsoft Teams, and Messenger, to target people.  More and more organizations of all sizes will have to use AI-based cybersecurity for detection and response since generative AI is becoming increasingly essential in combating hackers and breaches.
• Enhancing Multi-factor Authentication (MFA):  With improving image and voice-generation/manipulation technologies, enhanced authentication measures such as token-based authentication or other hardware-based measures, abnormal behaviour detection, multi-device push notifications, geolocation verifications, etc. can be used to improve prevention strategies.  New targeted technological solutions for content-driven authentication can also be implemented.
• Addressing Third-Party Vulnerabilities: Financial institutions often outsource operations to vendors that may not follow the same cybersecurity protocols, which can introduce vulnerabilities. Ensuring all parties follow standardized protocols can address these gaps.
• Protecting Senior Professionals: Senior-level and high-profile individuals at organizations are at a greater risk of being imitated or impersonated since they hold higher authority over decision-making and have greater access  to sensitive information. Protecting their identity metrics through technological interventions is of utmost importance.
• Advanced Employee Training:  To build organizational resilience, employees must be trained to understand how generative and emerging technologies work. A well-trained workforce can significantly lower the likelihood of successful human-focused human-focused cyberattacks like phishing and impersonation.
• Financial Support to Smaller Institutions: Smaller institutions may not have the resources to invest in robust long-term cybersecurity solutions and upgrades. They require financial and technological support from the government to meet requisite standards.

Conclusion

According to The India Cyber Threat Report 2025 by the Data Security Council of India (DSCI) and Seqrite, deepfake-enabled cyberattacks, especially in the finance and healthcare sectors, are set to increase in 2025. This has the potential to disrupt services, steal sensitive data, and exploit geopolitical tensions, presenting a significant risk to the critical infrastructure of India. 

As the threat landscape changes,  institutions will have to continue to embrace AI and Machine Learning (ML) for threat detection and response. The financial sector must prioritize robust cybersecurity strategies, participate in regulation-framing procedures, adopt AI-based solutions, and enhance workforce training, to safeguard against AI-enabled fraud. Collaborative efforts among policymakers, financial institutions, and technology providers will be essential to strengthen defenses.

‍

Sources 

• https://sumsub.com/newsroom/deepfake-cases-surge-in-countries-holding-2024-elections-sumsub-research-shows/ 
• https://www.globenewswire.com/news-release/2024/10/31/2972565/0/en/Deepfake-Fraud-Costs-the-Financial-Sector-an-Average-of-600-000-for-Each-Company-Regula-s-Survey-Shows.html 
• https://www.sipa.columbia.edu/sites/default/files/2023-05/For%20Publication_BOfA_PollardCartier.pdf 
• https://edition.cnn.com/2024/02/04/asia/deepfake-cfo-scam-hong-kong-intl-hnk/index.html 
• https://www.rbi.org.in/Commonman/English/scripts/Notification.aspx?Id=1721 
• https://elplaw.in/leadership/cybersecurity-and-cyber-resilience-framework-for-sebi-regulated-entities/ 
• https://economictimes.indiatimes.com/tech/artificial-intelligence/ai-driven-deepfake-enabled-cyberattacks-to-rise-in-2025-healthcarefinance-sectors-at-risk-report/articleshow/115976846.cms?from=mdr 

‍

Introduction

A message has recently circulated on WhatsApp alleging that voice and video chats made through the app will be recorded, and devices will be linked to the Ministry of Electronics and Information Technology’s system from now on. WhatsApp from now, record the chat activities and forward the details to the Government. The Anti-Government News has been shared on social media.

Message claims

• The fake WhatsApp message claims that an 11-point new communication guideline has been established and that voice and video calls will be recorded and saved. It goes on to say that WhatsApp devices will be linked to the Ministry’s system and that Facebook, Twitter, Instagram, and all other social media platforms will be monitored in the future.
• The fake WhatsApp message further advises individuals not to transmit ‘any nasty post or video against the government or the Prime Minister regarding politics or the current situation’. The bogus message goes on to say that it is a “crime” to write or transmit a negative message on any political or religious subject and that doing so could result in “arrest without a warrant.”
• The false message claims that any message in a WhatsApp group with three blue ticks indicates that the message has been noted by the government. It also notifies Group members that if a message has 1 Blue tick and 2 Red ticks, the government is checking their information, and if a member has 3 Red ticks, the government has begun procedures against the user, and they will receive a court summons shortly.

WhatsApp does not record voice and video calls

There has been news which is spreading that WhatsApp records voice calls and video calls of the users. the news is spread through a message that has been recently shared on social media. As per the Government, the news is fake, that WhatsApp cannot record voice and video calls. Only third-party apps can record voice and video calls. Usually, users use third-party Apps to record voice and video calls.

Third-party apps used for recording voice and video calls

• App Call recorder
• Call recorder- Cube ACR
• Video Call Screen recorder for WhatsApp FB
• AZ Screen Recorder
• Video Call Recorder for WhatsApp

Case Study

In 2022 there was a fake message spreading on social media, suggesting that the government might monitor WhatsApp talks and act against users. According to this fake message, a new WhatsApp policy has been released, and it claims that from now on, every message that is regarded as suspicious will have three 3 Blue ticks, indicating that the government has taken note of that message. And the same fake news is spreading nowadays.

WhatsApp Privacy policies against recording voice and video chats

The WhatsApp privacy policies say that voice calls, video calls, and even chats cannot be recorded through WhatsApp because of end-to-end encryption settings. End-to-end encryption ensures that the communication between two people will be kept private and safe.

WhatsApp Brand New Features

• Chat lock feature: WhatsApp Chat Lock allows you to store chats in a folder that can only be viewed using your device’s password or biometrics such as a fingerprint. When you lock a chat, the details of the conversation are automatically hidden in notifications. The motive of WhatsApp behind the cha lock feature is to discover new methods to keep your messages private and safe. The feature allows the protection of most private conversations with an extra degree of security
• Edit chats feature: WhatsApp can now edit your WhatsApp messages up to 15 minutes after they have been sent. With this feature, the users can make the correction in the chat or can add some extra points, users want to add.

Conclusion

The spread of misinformation and fake news is a significant problem in the age of the internet. It can have serious consequences for individuals, communities, and even nations. The news is fake as per the government, as neither WhatsApp nor the government could have access to WhatsApp chats, voice, and video calls on WhatsApp because of end-to-end encryption. End-to-end encryption ensures to protect of the communications of the users. The government previous year blocked 60 social media platforms because of the spreading of Anti India News. There is a fact check unit which identifies misleading and false online content.