#FactCheck- AI-Generated Video Falsely Claims Iran Attack on Apple, Microsoft in Israel

Introduction

The advent of Electronic Vehicles (EVs) represents a transformative leap towards a more sustainable and environmentally conscious transportation future by nations. However, as these vehicles become increasingly connected and reliant on advanced technological systems, a parallel concern emerges—data privacy. Integrating sophisticated technologies in EVs, such as GPS tracking, biometric authentication, and in-car connectivity, raises substantial questions about the collection, storage, and potential misuse of sensitive personal information. This intersection of automotive innovation and data privacy underscores the need for comprehensive solutions and regulatory frameworks to ensure that the benefits of electric vehicles are realised without compromising the privacy and security of their users.

Electronic vehicles primarily record three types of data;

1. Driving behaviour and patterns: The e-vehicle records braking and driving patterns, including acceleration, speed, and swerve. Some vehicles even track air conditioning usage and airbag deployment to determine the point of failure in the event of a crash.
2. Location data: The e-vehicles also track GPS systems to gauge the speed and direction of the vehicle.
3. EV functions and use of telematic services: Monitoring of EV functions includes battery use management, battery charging history, battery deterioration, electrical system functions and software version information.

Data Privacy requirements of companies

Companies manufacturing e-vehicles are saddled with several data privacy requirements as concerns about consumer safety. Data collected by e-vehicles may be sensitive in nature. Location tracking is a key issue that has garnered attention. The constant recording of a driver's whereabouts can lead to the creation of detailed profiles, raising questions about the potential misuse or unauthorised access to this sensitive information. The risk of surveillance, stalking, or even theft of valuable personal data is a genuine concern for EV owners.

Moreover, integrating smart features, such as voice recognition, biometric authentication, and in-car personal assistants, adds another layer of complexity. These features require the collection and processing of personal data. If not handled securely, they may become vulnerable to hacking or unauthorised access, leading to identity theft or other malicious activities. Additionally, Smart charging systems offer convenience by allowing remote monitoring and control of charging, but they also gather extensive data. The geographical data collected during charging may raise concerns about location privacy.

Striking a delicate balance between leveraging this data for enhancing vehicle performance and user experience while safeguarding the privacy of EV owners is paramount. Transparent privacy policies, secure data storage practices, and stringent encryption protocols are essential components of a comprehensive approach to data protection. If a company is eyeing the international market or utilising cloud-based software with decentralised global data storage, it must also navigate international privacy and data protection laws. A prime example is the General Data Protection Regulation (GDPR), a globally recognised and stringent data protection law applicable to both European-based companies and international entities providing goods, services, or monitoring activities of residents within Europe. 

Manufacturers of these vehicles are subjected to compliance with this comprehensive legal framework. Obligations on companies are levied by them being data fiduciaries; dual liability may also emanate since some data fiduciaries may also qualify as data processors. Special care must be taken when data is being transferred to third parties. 

Further, compliance with consumer safety laws is also an important consideration. In India, the Consumer Protection Act of 2019 safeguards the rights of consumers, holding manufacturers, sellers, and service providers responsible for any harm resulting from faulty or defective products. This extends the Act's coverage to include manufacturers and sellers of internet and technology-based products. When read with the Digital Personal Data Protection Act of 2023 (DPDP Act), the Consumer Protection Act of 2019 takes on additional significance. The DPDP Act, focusing on the security of an individual's digital personal data, introduces provisions such as mandatory consent, purpose limitation, data minimisation, obligatory security measures by organisations, data localisation, and enforcing accountability and compliance. These provisions apply to information generated by and for consumers, offering a comprehensive framework for protecting digital personal data.

Conclusion

The intersection of e-vehicles and data privacy necessitates a careful and comprehensive approach to ensure the coexistence of automotive innovation and user security. As electric vehicles record intricate data related to driving behaviour, location, and telematic services, companies manufacturing these vehicles must navigate a complex landscape of data privacy requirements. The potential risks associated with location tracking, smart features, and the extensive data collected during charging underscore the importance of transparent privacy policies, secure data storage practices, and stringent encryption protocols. Moreover, as companies expand globally, compliance with international privacy laws like the GDPR becomes imperative. Balancing the enhancement of vehicle performance and user experience with the safeguarding of privacy is paramount. Manufacturers, deemed as data fiduciaries, must exercise diligence, especially when transferring data to third parties. Additionally, adherence to consumer safety laws, such as the Consumer Protection Act of 2019, further emphasises the need for a holistic and vigilant approach to ensure the responsible use of data in the evolving landscape of e-vehicles.

References

• https://digitalcommons.law.scu.edu/cgi/viewcontent.cgi?article=1556&context=chtlj 
• https://cyberswitching.com/electric-car-charging-and-data-privacy/#:~:text=Smart%20charging%20systems%20provide%20convenience,in%20safeguarding%20EV%20user%20privacy

Introduction

Artificial intelligence is often hailed as a democratiser of knowledge, opportunity and skill. It is set to improve diagnostics, personalised learning, and productivity to boost the economy, which can assist millions of people to leave poverty. However, this may be an incomplete picture. A report of the United Nations Development Programme in 2025 tells a more complex tale. The Next Great Divergence: Why AI May Widen Inequality Between Countries cautions that, unless acts are taken to intervene, AI will not alleviate inequality between countries but will instead concentrate benefits in already advantaged economies and increase risks in more vulnerable ones.

Two Gaps, One Crisis

AI is not going to create a level playing field: it has been injected into a world where there is unprecedented inequality. The report outlines two structural asymmetries that will influence the ways in which its effects manifest: a capability gap and a vulnerability gap. 

Those countries that have high connectivity, skills, compute and regulation will be in a position to reap a greater portion of the AI dividend. Others will be exposed to greater risks of job losses, information exclusion, misinformation, and the indirect consequences of increased energy and water demands. 

The centre of this transition is the Asia-Pacific region, that harbors a population of more than 55 per cent of the world. More than half of the global AI users are now located in the region, but the initial positions are quite different. Nations such as Singapore and South Korea are already spending a lot of money on AI infrastructure, with others still striving to offer basic broadband services. Two out of three individuals already use AI tools in certain high-income economies. In most countries with low incomes, the utilisation is lower. Such figures are important as they depict not only a gap in technology but also a structural difference in terms of who controls AI and who is controlled by the latter.

When Inequality Becomes a Trust Problem

Any trusted technological system is based on three tenets: transparency, fairness and accountability. AI inequality negatively impacts all three.

If governments implement imported AI systems in areas with limited technical capability, with limited transparency on their operation, their construction, and their biases. Citizens do not really trust when decision-making systems are black boxes and domestic institutions lack the know-how to question them.

Data exclusion also interferes with fairness. The AI systems trained with the datasets not sufficiently representative of the rural population, linguistic minorities, and women will generate poorer results in those groups systematically. Since South Asian women are much less likely to own a smartphone,  this impacts their representation in digital data, and consequently in any AI system trained on such data. 

Safety Risks Are Not Evenly Distributed

The lack of trust has a direct safety aspect. For example, those countries that have less robust information ecosystems have a greater exposure to AI-generated misinformation that can bias the discourse of the populace, alter elections, and cause violence. They also have the weakest capability of screening, tagging, or combating such content.

The same can be said about labour markets. The very same technologies that can speed up marginalisation and destabilise governance increase human insecurity, especially among employees in the informal economy with weak social security. The UNDP report points out that the exposure of female employment to disruption by AI is disproportionate to that of male employment, which further presents a gendered dimension in an already unequal situation.

Risks of infrastructure are skewed as well. Large AI systems may create disproportionately high energy and water demands on countries that host the data infrastructure without there being an equivalent economic payback. The environmental cost is local while profits are outsourced. Dangers of AI spread downwards, and the advantages go upwards.

The Governance Gap and Regulatory Arbitrage

Governance is perhaps the most important aspect. There are only a few states that presently have extensive AI regulation systems. This gives rise to a patchy landscape, in which safety standards differ dramatically and where companies have an incentive to install systems in jurisdictions that have weaker regulation.

The main reason is the lack of capability, as expressed by Philip Schellekens, chief economist of the UNDP in Asia and the Pacific, who says that those countries that invest in skills, computing power and well-run governance structures will gain. The rest will be left far behind. 

This departure has its ramifications outside the nations. When users in other areas are subjected to widely different rates of safety and equity by the same international platforms, the concept of uniform digital norms would no longer be sustainable. Confidence in AI systems is lost not only locally but also on a global scale.

Way Forward

The UNDP report makes it clear that there is no inevitability of divergence. To avert it, however, it is necessary to consider AI governance as a development, rather than a technology problem.

The capacity to govern should be constructed and not presumed. This implies assisting countries in establishing regulatory systems, institutional capacity, and facilitating cross-border collaboration on standards. It can also imply considering some AI features as a public good, with common models and open standards that do not allow a few firms or states to become too powerful.

The UNDP articulates the problem in a simple manner: in the end, the world's people and not machines must decide on what technologies should be given priority and how to utilise them optimally.

Conclusion

AI inequality is often framed as an economic divergence story. But its implications run deeper. It reshapes who is protected, who is visible in data, and who has the power to challenge harmful outcomes. The risk is not just that some countries fall behind economically. It is that the global digital ecosystem fragments into zones of high trust and low trust, high protection and low protection. The choices made now will determine which path prevails. AI can reinforce existing divides or help bridge them. 

But that outcome will not be decided by the technology itself. It will be decided by how societies choose to distribute access, power, and responsibility in the systems they build.

References

1. https://www.undp.org/sites/g/files/zskgke326/files/2025-12/undp-rbap-the-next-great-divergence_1.pdf
2. https://www.undp.org/asia-pacific/press-releases/ai-risks-sparking-new-era-divergence-development-gaps-between-countries-widen-undp-report-finds
3. https://www.undp.org/asia-pacific/blog/next-great-divergence-how-ai-could-split-world-again-if-we-dont-intervene
4. https://www.aljazeera.com/news/2025/12/2/ai-threatens-to-widen-inequality-among-states-un
5. https://www.undp.org/asia-pacific/next-great-divergence
6. https://www.eco-business.com/press-releases/ai-risks-spark-new-era-of-divergence-as-development-gaps-widen-undp-report/

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Executive Summary:
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A widely circulated claim on social media, including a post from the official X account of Pakistan, alleges that the Pakistan Air Force (PAF) carried out an airstrike on India, supported by a viral video. However, according to our research, the video used in these posts is actually footage from the video game Arma-3 and has no connection to any real-world military operation. The use of such misleading content contributes to the spread of false narratives about a conflict between India and Pakistan and has the potential to create unnecessary fear and confusion among the public.

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Claim:
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Viral social media posts, including the official Government of Pakistan X handle, claims that the PAF launched a successful airstrike against Indian military targets. The footage accompanying the claim shows jets firing missiles and explosions on the ground. The video is presented as recent and factual evidence of heightened military tensions.

Fact Check:

As per our research using reverse image search, the videos circulating online that claim to show Pakistan launching an attack on India under the name 'Operation Sindoor' are misleading. There is no credible evidence or reliable reporting to support the existence of any such operation. The Press Information Bureau (PIB) has also verified that the video being shared is false and misleading. During our research, we also came across footage from the video game Arma-3 on YouTube, which appears to have been repurposed to create the illusion of a real military conflict. This strongly indicates that fictional content is being used to propagate a false narrative. The likely intention behind this misinformation is to spread fear and confusion by portraying a conflict that never actually took place.

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Conclusion:
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It is true to say that Pakistan is using the widely shared misinformation videos to attack India with false information. There is no reliable evidence to support the claim, and the videos are misleading and irrelevant. Such false information must be stopped right away because it has the potential to cause needless panic. No such operation is occurring, according to authorities and fact-checking groups.

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• Claim: Viral social media posts claim PAF attack on India
• Claimed On:  Social Media
• Fact Check: False and Misleading