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Introduction

In today’s digital world, where everything is related to data, the more data you own, the more control and compliance you have over the market, which is why companies are looking for ways to use data to improve their business. But at the same time, they have to make sure they are protecting people’s privacy. It is very tricky to strike a balance between both of them. Imagine you are trying to bake a cake where you need to use all the ingredients to make it taste great, but you also have to make sure no one can tell what’s in it. That’s kind of what companies are dealing with when it comes to data. Here, ‘Pseudonymisation’ emerges as a critical technical and legal mechanism that offers a middle ground between data anonymisation and unrestricted data processing. 

Legal Framework and Regulatory Landscape 

Pseudonymisation, as defined by the General Data Protection Regulation (GDPR) in Article 4(5), refers to “the processing of personal data in such a manner that the personal data can no longer be attributed to a specific data subject without the use of additional information, provided that such additional information is kept separately and is subject to technical and organisational measures to ensure that the personal data are not attributed to an identified or identifiable natural person”. This technique represents a paradigm shift in data protection strategy, enabling organisations to preserve data utility while significantly reducing privacy risks. The growing importance of this balance is evident in the proliferation of data protection laws worldwide, from GDPR in Europe to India’s Digital Personal Data Protection Act (DPDP) of 2023. 

Its legal treatment varies across jurisdictions, but a convergent approach is emerging that recognises its value as a data protection safeguard while maintaining that the pseudonymised data remains personal data. Article 25(1) of GDPR recognises it as “an appropriate technical and organisational measure” and emphasises its role in reducing risks to data subjects. It protects personal data by reducing the risk of identifying individuals during data processing. The European Data Protection Board’s (EDPB) 2025 Guidelines on Pseudonymisation provide detailed guidance emphasising the importance of defining the “pseudonymisation domain”. It defines who is prevented from attributing data to specific individuals and ensures that the technical and organised measures are in place to block unauthorised linkage of pseudonymised data to the original data subjects. In India, while the DPDP Act does not explicitly define pseudonymisation, legal scholars argue that such data would still fall under the definition of personal data, as it remains potentially identifiable. The Act defines personal data defined in section 2(t) broadly as “any data about an individual who is identifiable by or in relation to such data,” suggesting that the pseudonymised information, being reversible, would continue to require compliance with data protection obligations. 

Further, the DPDP  Act, 2023 also includes principles of data minimisation and purpose limitation. Section 8(4) says that a “Data Fiduciary shall implement appropriate technical and organisational measures to ensure effective observance of the provisions of this Act and the Rules made under it.” The concept of Pseudonymization fits here because it is a recognised technical safeguard, which means companies can use pseudonymization as one of the methods or part of their compliance toolkit under Section 8(4) of the DPDP Act. However, its use should be assessed on a case to case basis, since ‘encryption’ is also considered one of the strongest methods for protecting personal data. The suitability of pseudonymization depends on the nature of the processing activity, the type of data involved, and the level of risk that needs to be mitigated. In practice, organisations may use pseudonymization in combination with other safeguards to strengthen overall compliance and security.

The European Court of Justice’s recent jurisprudence has introduced nuanced considerations about when pseudonymised data might not constitute personal data for certain entities. In cases where only the original controller possesses the means to re-identify individuals, third parties processing such data may not be subject to the full scope of data protection obligations, provided they cannot reasonably identify the data subjects. The “means reasonably likely” assessment represents a significant development in understanding the boundaries of data protection law. 

Corporate Implementation Strategies 

Companies find that pseudonymisation is not just about following rules, but it also brings real benefits. By using this technique, businesses can keep their data more secure and reduce the damage in the event of a breach. Customers feel more confident knowing that their information is protected, which builds trust. Additionally, companies can utilise this data for their research or other important purposes without compromising user privacy. 

Key Benefits of Pseudonymisation:

1. Enhanced Privacy Protection: It hides personal details like names or IDs with fake ones (with artificial values or codes), making it harder for accidental privacy breaches.
2. Preserved Data Utility: Unlike completely anonymous data, pseudonymised data keeps its usefulness by maintaining important patterns and relationships within datasets.
3. Facilitate Data Sharing: It’s easier to share pseudonymised data with partners or researchers because it protects privacy while still being useful.

However, using pseudonymisation is not as easy as companies have to deal with tricky technical issues like choosing the right methods, such as encryption or tokenisation and managing security keys safely. They have to implement strong policies to stop anyone from figuring out who the data belongs to. This can get expensive and complicated, especially when dealing with a large amount of data, and it often requires expert help and regular upkeep. 

Balancing Privacy Rights and Data Utility

The primary challenge in pseudonymisation is striking the right balance between protecting individuals' privacy and maintaining the utility of the data. To get this right, companies need to consider several factors, such as why they are using the data, the potential hacker's level of skill, and the type of data being used.

Conclusion 

Pseudonymisation offers a practical middle ground between full anonymisation and restricted data use, enabling organisations to harness the value of data while protecting individual privacy. Legally, it is recognised as a safeguard but still treated as personal data, requiring compliance under frameworks like GDPR and India’s DPDP Act. For companies, it is not only regulatory adherence but also ensuring that it builds trust and enhances data security. However, its effectiveness depends on robust technical methods, governance, and vigilance. Striking the right balance between privacy and data utility is crucial for sustainable, ethical, and innovation-driven data practices.

References:

1. https://gdpr-info.eu/art-4-gdpr/
2. https://www.meity.gov.in/static/uploads/2024/06/2bf1f0e9f04e6fb4f8fef35e82c42aa5.pdf
3. https://gdpr-info.eu/art-25-gdpr/
4. https://www.edpb.europa.eu/system/files/2025-01/edpb_guidelines_202501_pseudonymisation_en.pdf
5. https://curia.europa.eu/juris/document/document.jsf?text=&docid=303863&pageIndex=0&doclang=EN&mode=req&dir=&occ=first&part=1&cid=16466915
6. https://curia.europa.eu/juris/document/document.jsf?text=&docid=303863&pageIndex=0&doclang=EN&mode=req&dir=&occ=first&part=1&cid=16466915

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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

With the increasing frequency and severity of cyber-attacks on critical sectors, the government of India has formulated the National Cyber Security Reference Framework (NCRF) 2023, aimed to address cybersecurity concerns in India. In today’s digital age, the security of critical sectors is paramount due to the ever-evolving landscape of cyber threats. Cybersecurity measures are crucial for protecting essential sectors such as banking, energy, healthcare, telecommunications, transportation, strategic enterprises, and government enterprises. This is an essential step towards safeguarding these critical sectors and preparing for the challenges they face in the face of cyber threats. Protecting critical sectors from cyber threats is an urgent priority that requires the development of robust cybersecurity practices and the implementation of effective measures to mitigate risks.

Overview of the National Cyber Security Policy 2013

The National Cyber Security Policy of 2013 was the first attempt to address cybersecurity concerns in India. However, it had several drawbacks that limited its effectiveness in mitigating cyber risks in the contemporary digital age. The policy’s outdated guidelines, insufficient prevention and response measures, and lack of legal implications hindered its ability to protect critical sectors adequately. Moreover, the policy should have kept up with the rapidly evolving cyber threat landscape and emerging technologies, leaving organisations vulnerable to new cyber-attacks. The 2013 policy failed to address the evolving nature of cyber threats, leaving organisations needing updated guidelines to combat new and sophisticated attacks.

As a result, an updated and more comprehensive policy, the National Cyber Security Reference Framework 2023, was necessary to address emerging challenges and provide strategic guidance for protecting critical sectors against cyber threats.

Highlights of NCRF 2023

Strategic Guidance: NCRF 2023 has been developed to provide organisations with strategic guidance to address their cybersecurity concerns in a structured manner.

Common but Differentiated Responsibility (CBDR): The policy is based on a CBDR approach, recognising that different organisations have varying levels of cybersecurity needs and responsibilities.

Update of National Cyber Security Policy 2013: NCRF supersedes the National Cyber Security Policy 2013, which was due for an update to align with the evolving cyber threat landscape and emerging challenges.

Different from CERT-In Directives: NCRF is distinct from the directives issued by the Indian Computer Emergency Response Team (CERT-In) published in April 2023. It provides a comprehensive framework rather than specific directives for reporting cyber incidents.

Combination of robust strategies: National Cyber Security Reference Framework 2023 will provide strategic guidance, a revised structure, and a proactive approach to cybersecurity, enabling organisations to tackle the growing cyberattacks in India better and safeguard critical sectors. Rising incidents of malware attacks on critical sectors

In recent years, there has been a significant increase in malware attacks targeting critical sectors. These sectors, including banking, energy, healthcare, telecommunications, transportation, strategic enterprises, and government enterprises, play a crucial role in the functioning of economies and the well-being of societies. The escalating incidents of malware attacks on these sectors have raised concerns about the security and resilience of critical infrastructure.

Banking: The banking sector handles sensitive financial data and is a prime target for cybercriminals due to the potential for financial fraud and theft.

Energy: The energy sector, including power grids and oil companies, is critical for the functioning of economies, and disruptions can have severe consequences for national security and public safety.

Healthcare: The healthcare sector holds valuable patient data, and cyber-attacks can compromise patient privacy and disrupt healthcare services. Malware attacks on healthcare organisations can result in the theft of patient records, ransomware incidents that cripple healthcare operations, and compromise medical devices.

Telecommunications: Telecommunications infrastructure is vital for reliable communication, and attacks targeting this sector can lead to communication disruptions and compromise the privacy of transmitted data. The interconnectedness of telecommunications networks globally presents opportunities for cybercriminals to launch large-scale attacks, such as Distributed Denial-of-Service (DDoS) attacks.

Transportation: Malware attacks on transportation systems can lead to service disruptions, compromise control systems, and pose safety risks.

Strategic Enterprises: Strategic enterprises, including defence, aerospace, intelligence agencies, and other sectors vital to national security, face sophisticated malware attacks with potentially severe consequences. Cyber adversaries target these enterprises to gain unauthorised access to classified information, compromise critical infrastructure, or sabotage national security operations.

Government Enterprises: Government organisations hold a vast amount of sensitive data and provide essential services to citizens, making them targets for data breaches and attacks that can disrupt critical services.

Conclusion  

The sectors of banking, energy, healthcare, telecommunications, transportation, strategic enterprises, and government enterprises face unique vulnerabilities and challenges in the face of cyber-attacks. By recognising the significance of safeguarding these sectors, we can emphasise the need for proactive cybersecurity measures and collaborative efforts between public and private entities. Strengthening regulatory frameworks, sharing threat intelligence, and adopting best practices are essential to ensure our critical infrastructure’s resilience and security. Through these concerted efforts, we can create a safer digital environment for these sectors, protecting vital services and preserving the integrity of our economy and society. The rising incidents of malware attacks on critical sectors emphasise the urgent need for updated cybersecurity policy, enhanced cybersecurity measures, a collaboration between public and private entities, and the development of proactive defence strategies. National Cyber Security Reference Framework 2023 will help in addressing the evolving cyber threat landscape, protect critical sectors, fill the gaps in sector-specific best practices, promote collaboration, establish a regulatory framework, and address the challenges posed by emerging technologies. By providing strategic guidance, this framework will enhance organisations’ cybersecurity posture and ensure the protection of critical infrastructure in an increasingly digitised world.