#FactCheck - Viral Photos Falsely Linked to Iranian President Ebrahim Raisi's Helicopter Crash

The 2020s mark the emergence of deepfakes in general media discourse. The rise in deepfake technology is defined by a very simple yet concerning fact: it is now possible to create perfect imitations of anyone using AI tools that can create audio in any person's voice and generate realistic images and videos of almost anyone doing pretty much anything. The proliferation of deepfake content in the media poses great challenges to the functioning of democracies. especially as such materials can deprive the public of the accurate information it needs to make informed decisions in elections. Deepfakes are created using AI, which combines different technologies to produce synthetic content.

Understanding Deepfakes 

Deepfakes are synthetically generated content created using artificial intelligence (AI). This technology works on an advanced algorithm that creates hyper-realistic videos by using a person’s face, voice or likeness utilising techniques such as machine learning. The utilisation and progression of deepfake technology holds vast potential, both benign and malicious. 

An example is when the NGO Malaria No More which had used deepfake technology in 2019 to sync David Beckham’s lip movements with different voices in nine languages, amplified its anti-malaria message.

Deepfakes have a dark side too. They have been used to spread false information, manipulate public opinion, and damage reputations. They can harm mental health and have significant social impacts. The ease of creating deepfakes makes it difficult to verify media authenticity, eroding trust in journalism and creating confusion about what is true and what is not. Their potential to cause harm has made it necessary to consider legal and regulatory approaches.

India’s Legal Landscape Surrounding Deepfakes

India presently lacks a specific law dealing with deepfakes, but the existing legal provisions offer some safeguards against mischief caused. 

• Deepfakes created with the intent of spreading misinformation or damaging someone’s reputation can be prosecuted under the Bharatiya Nyaya Sanhita of 2023. It deals with the consequences of such acts under Section 356, governing defamation law.
• The Information Technology Act of 2000, the primary law that regulates Indian cyberspace. Any unauthorised disclosure of personal information which is used to create deepfakes for harassment or voyeurism is a violation of the act.
• The unauthorised use of a person's likeness in a deepfake can become a violation of their intellectual property rights and lead to copyright infringement.
• India’s privacy law, the Digital Personal Data Protection Act, regulates and limits the misuse of personal data. It has the potential to address deepfakes by ensuring that individuals’ likenesses are not used without their consent in digital contexts.

India, at present, needs legislation that can specifically address the challenges deepfakes pose. The proposed legislation, aptly titled, ‘the Digital India Act’ aims to tackle various digital issues, including the misuse of deepfake technology and the spread of misinformation. Additionally, states like Maharashtra have proposed laws targeting deepfakes used for defamation or fraud, highlighting growing concerns about their impact on the digital landscape.

Policy Approaches to Regulation of Deepfakes

• Criminalising and penalising the making, creation and distribution of harmful deepfakes as illegal will act as a deterrent.
• There should be a process that mandates the disclosures for synthetic media. This would be to inform viewers that the content has been created using AI. 
• Encouraging tech companies to implement stricter policies on deepfake content moderation can enhance accountability and reduce harmful misinformation.
• The public’s understanding of deepfakes should be promoted. Especially, via awareness campaigns that will empower citizens to critically evaluate digital content and make informed decisions.

Deepfake, Global Overview

There has been an increase in the momentum to regulate deepfakes globally. In October 2023, US President Biden signed an executive order on AI risks instructing the US Commerce Department to form labelling standards for AI-generated content. California and Texas have passed laws against the dangerous distribution of deepfake images that affect electoral contexts and Virginia has targeted a law on the non-consensual distribution of deepfake pornography. 

China promulgated regulations requiring explicit marking of doctored content. The European Union has tightened its Code of Practice on Disinformation by requiring social media to flag deepfakes, otherwise they risk facing hefty fines and proposed transparency mandates under the EU AI Act. These measures highlight a global recognition of the risks that deepfakes pose and the need for a robust regulatory framework.

Conclusion 

With deepfakes being a significant source of risk to trust and democratic processes, a multi-pronged approach to regulation is in order. From enshrining measures against deepfake technology in specific laws and penalising the same, mandating transparency and enabling public awareness, the legislators have a challenge ahead of them. National and international efforts have highlighted the urgent need for a comprehensive framework to enable measures to curb the misuse and also promote responsible innovation. Cooperation during these trying times will be important to shield truth and integrity in the digital age. 

References

• https://digitalcommons.usf.edu/cgi/viewcontent.cgi?article=2245&context=jss
• https://www.thehindu.com/news/national/regulating-deepfakes-generative-ai-in-india-explained/article67591640.ece
• https://www.brennancenter.org/our-work/research-reports/regulating-ai-deepfakes-and-synthetic-media-political-arena
• https://www.responsible.ai/a-look-at-global-deepfake-regulation-approaches/
• https://thesecretariat.in/article/wake-up-call-for-law-making-on-deepfakes-and-misinformation 

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The World Economic Forum reported that AI-generated misinformation and disinformation are the second most likely threat to present a material crisis on a global scale in 2024 at 53% (Sept. 2023). Artificial intelligence is automating the creation of fake news at a rate disproportionate to its fact-checking. It is spurring an explosion of web content mimicking factual articles that instead disseminate false information about grave themes such as elections, wars and natural disasters. 

According to a report by the Centre for the Study of Democratic Institutions, a Canadian think tank, the most prevalent effect of Generative AI is the ability to flood the information ecosystem with misleading and factually-incorrect content. As reported by Democracy Reporting International during the 2024 elections of the European Union, Google's Gemini, OpenAI’s ChatGPT 3.5 and 4.0, and Microsoft’s AI interface ‘CoPilot’ were inaccurate one-third of the time when engaged for any queries regarding the election data.   Therefore, a need for an innovative regulatory approach like regulatory sandboxes which can address these challenges while encouraging responsible AI innovation is desired.

What Is AI-driven Misinformation?

False or misleading information created, amplified, or spread using artificial intelligence technologies is AI-driven misinformation. Machine learning models are leveraged to automate and scale the creation of false and deceptive content. Some examples are deep fakes, AI-generated news articles, and bots that amplify false narratives on social media.

The biggest challenge is in the detection and management of AI-driven misinformation. It is difficult to distinguish AI-generated content from authentic content, especially as these technologies advance rapidly. 

AI-driven misinformation can influence elections, public health, and social stability by spreading false or misleading information. While public adoption of the technology has undoubtedly been rapid, it is yet to achieve true acceptance and actually fulfill its potential in a positive manner because there is widespread cynicism about the technology - and rightly so. The general public sentiment about AI is laced with concern and doubt regarding the technology’s trustworthiness, mainly due to the absence of a regulatory framework maturing on par with the technological development. 

Regulatory Sandboxes: An Overview

Regulatory sandboxes refer to regulatory tools that allow businesses to test and experiment with innovative products, services or businesses under the supervision of a regulator for a limited period. They engage by creating a controlled environment where regulators allow businesses to test new technologies or business models with relaxed regulations.

Regulatory sandboxes have been in use for many industries and the most recent example is their use in sectors like fintech, such as the UK’s Financial Conduct Authority sandbox. These models have been known to encourage innovation while allowing regulators to understand emerging risks. Lessons from the fintech sector show that the benefits of regulatory sandboxes include facilitating firm financing and market entry and increasing speed-to-market by reducing administrative and transaction costs. For regulators, testing in sandboxes informs policy-making and regulatory processes. Looking at the success in the fintech industry, regulatory sandboxes could be adapted to AI, particularly for overseeing technologies that have the potential to generate or spread misinformation.

The Role of Regulatory Sandboxes in Addressing AI Misinformation

Regulatory sandboxes can be used to test AI tools designed to identify or flag misinformation without the risks associated with immediate, wide-scale implementation. Stakeholders like AI developers, social media platforms, and regulators work in collaboration within the sandbox to refine the detection algorithms and evaluate their effectiveness as content moderation tools.

These sandboxes can help balance the need for innovation in AI and the necessity of protecting the public from harmful misinformation. They allow the creation of a flexible and adaptive framework capable of evolving with technological advancements and fostering transparency between AI developers and regulators. This would lead to more informed policymaking and building public trust in AI applications.

CyberPeace Policy Recommendations 

Regulatory sandboxes offer a mechanism to predict solutions that will help to regulate the misinformation that AI tech creates. Some policy recommendations are as follows: 

1. Create guidelines for a global standard for including regulatory sandboxes that can be adapted locally and are useful in ensuring consistency in tackling AI-driven misinformation.
2. Regulators can propose to offer incentives to companies that participate in sandboxes. This would encourage innovation in developing anti-misinformation tools, which could include tax breaks or grants.
3. Awareness campaigns can help in educating the public about the risks of AI-driven misinformation and the role of regulatory sandboxes can help manage public expectations.
4. Periodic and regular reviews and updates to the sandbox frameworks should be conducted to keep pace with advancements in AI technology and emerging forms of misinformation should be emphasized.

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Conclusion and the Challenges for Regulatory Frameworks

Regulatory sandboxes offer a promising pathway to counter the challenges that AI-driven misinformation poses while fostering innovation. By providing a controlled environment for testing new AI tools, these sandboxes can help refine technologies aimed at detecting and mitigating false information. This approach ensures that AI development aligns with societal needs and regulatory standards, fostering greater trust and transparency. With the right support and ongoing adaptations, regulatory sandboxes can become vital in countering the spread of AI-generated misinformation, paving the way for a more secure and informed digital ecosystem.

References

• https://www.thehindu.com/sci-tech/technology/on-the-importance-of-regulatory-sandboxes-in-artificial-intelligence/article68176084.ece
• https://www.oecd.org/en/publications/regulatory-sandboxes-in-artificial-intelligence_8f80a0e6-en.html
• https://www.weforum.org/publications/global-risks-report-2024/
• https://democracy-reporting.org/en/office/global/publications/chatbot-audit#Conclusions

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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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