#FactCheck: Viral video claims BSF personnel thrashing a person selling Bangladesh National Flag in West Bengal

Biological data includes biometric information such as fingerprints, facial recognition, DNA sequences, and behavioral traits. Genetic data can be extracted from an individual’s remains long after their death and can continue to identify both that individual and an expanding pool of their living relatives. This persistent identification can significantly reduce privacy over time, revealing genetic characteristics and familial relationships across successive generations.

Key Developments in Privacy Protection for Biological Data:

Legal texts have been created relating to personal data protection and privacy broadly, and can sometimes prove to be poor adaptations specifically for ‘biometric data’ and its safety. Some examples are mentioned below:

1. EU and UK- GDPR

GDPR focuses primarily on biometrics in Biological Data while deciphering the technology's immense potential. The EU describes “personal data” under the General Data Protection Regulation (GDPR) including any identifiable information about a particular person. For example, this can include names, identification numbers, location data, and other structured and unstructured data. In addition, the GDPR has more specific requirements around processing sensitive or “special categories of personal data.” These “special categories” include things like genetic and biometric data. For biometric security to work well, citizens' rights must be protected appropriately, and the data collected by private and public concerns must be managed carefully and sensibly. 

2. USA

California Consumer Privacy Act (CCPA) grants Californian consumers the right to protect their personal information and biometric data including the right to disclosure or access, the right to be forgotten, and data portability. The sale of personal information and the option of opt-out is also given to consumers. Additionally, it contains the right to take legal action, with penalties imposed for violations.

The California Privacy Rights Act was passed on November 3, 2020, and took effect on January 1, 2023, with a lookback period starting January 1, 2022. It introduces sensitive personal information which includes biometric data and other sensitive details. 

Virginia's Consumer Data Protection Act, effective from January 1, 2023, designates genetic and biometric data as sensitive data that must be protected.

Illinois' Biometric Information Privacy Act is recognised as the most robust biometric privacy law in the United States. The significance of the Rosenbach v. Six Flags case lies in the Illinois Supreme Court's ruling that a plaintiff does not need to demonstrate additional harm to impose penalties on a BIPA violator. A mere loss of statutory biometric privacy rights is sufficient to warrant penalties.

3. India

As per Rule 2(1)(b) of the SPDI Rules, Sensitive Personal Data or Information, including biometric data is included under its meaning. The term ‘biometric data’ has not been defined in the Digital Personal Data Protection Act, 2023. The need for data privacy under the DPDP Act emerges only if such data is subsequently digitised under extraction and manipulation, including notice and consent requirements and penalties. 

The Biotech-PRIDE (Promotion of Research and Innovation through Data Exchange) Guidelines of 2021 are aimed at fostering an exchange of information which would thereby enhance research and innovation among various research groups nationwide. These guidelines do not deal with the generation of biological data but are a mechanism to share and exchange information and knowledge generated according to existing laws, rules, regulations and norms of the country. They will ensure data-sharing benefits, maximise use, avoid duplication, maximise integration, ownership of information, better decision-making and equity of access

How is Biological Data vulnerable?

• Biological data is often immutable, meaning it cannot be altered once compromised. Unlike other authentications that can be changed, compromised biometric data poses a permanent risk, making its protection paramount.
• The use of facial recognition technology by law enforcement agencies and the creation of databases by the same also highlights the urgent need for stringent privacy protections.
• Advances in technology, particularly AI and ML, make it easier to collect, analyse, and utilise biometric data by manipulating biometric data. This in turn is leading to new forms of identity theft and fraud that make it necessary to enhance security measures and ethical considerations to prevent abuse.
• Cross-border data transfers raise serious privacy concerns, especially as countries have varying levels and standards of data protection. 
• Wearable health-related biometric devices lack the required privacy protections which ends up making the data they collect vulnerable to misuse and breaches.

Future Outlook

With the growing use of biological data, there is likely to be increased pressure on regulatory bodies to strengthen privacy protections. This necessitates a need for enhanced security measures to protect users' identities and further prevent any form of unauthorised access. Future developments should be aimed at including strict consent requirements, and enhanced data security measures, especially for wearable devices. A new legal framework specifically designed to address the challenges posed by biometric data would be welcome. Biological data protection is an emerging need in the digital environment that we live in today.

References

• https://www.cnbc.com/2024/08/17/new-privacy-battle-is-underway-as-tech-gadgets-capture-our-brain-waves.html 
• https://www.snrlaw.in/sense-and-sensitivity-sensitive-information-under-indias-new-data-regime/ 
• https://www.thalesgroup.com/en/markets/digital-identity-and-security/government/biometrics/biometric-data‍
• https://www.business-standard.com/article/economy-policy/govt-releases-guideline-to-provide-framework-for-sharing-of-biological-data-121073001467_1.html

Executive Summary:

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A viral video claiming to show Israelis pleading with Iran to "stop the war" is not authentic. As per our research the footage is AI-generated, created using tools like Google’s Veo, and not evidence of a real protest. The video features unnatural visuals and errors typical of AI fabrication. It is part of a broader wave of misinformation surrounding the Israel-Iran conflict, where AI-generated content is widely used to manipulate public opinion. This incident underscores the growing challenge of distinguishing real events from digital fabrications in global conflicts and highlights the importance of media literacy and fact-checking.

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Claim:

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A X verified  user with the handle "Iran, stop the war, we are sorry" posted a video featuring people holding placards and the Israeli flag. The caption suggests that Israeli citizens are calling for peace and expressing remorse, stating, "Stop the war with Iran! We apologize! The people of Israel want peace." The user further claims that Israel, having allegedly initiated the conflict by attacking Iran, is now seeking reconciliation.

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Fact Check:

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The bottom-right corner of the video displays a "VEO" watermark, suggesting it was generated using Google's AI tool, VEO 3. The video exhibits several noticeable inconsistencies such as robotic, unnatural speech, a lack of human gestures, and unclear text on the placards. Additionally, in one frame, a person wearing a blue T-shirt is seen holding nothing, while in the next frame, an Israeli flag suddenly appears in their hand, indicating possible AI-generated glitches.

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We further analyzed the video using the AI detection tool HIVE Moderation, which revealed a 99% probability that the video was generated using artificial intelligence technology. To validate this finding, we examined a keyframe from the video separately, which showed an even higher likelihood of 99% probability of being AI generated. These results strongly indicate that the video is not authentic and was most likely created using advanced AI tools.

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Conclusion:

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The video is highly likely to be AI-generated, as indicated by the VEO watermark, visual inconsistencies, and a 99% probability from HIVE Moderation. This highlights the importance of verifying content before sharing, as misleading AI-generated media can easily spread false narratives.

• Claim: AI generated video of Israelis saying "Stop the War, Iran We are Sorry". ‍
• Claimed On: Social Media‍
• Fact Check:AI Generated Mislead

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Introduction

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AI is transforming the way work is done and redefining the nature of jobs over the next decade. In the case of India, it is not just what duties will be taken over by machines, but how millions of employees will move to other sectors, which skills will become more sought-after, and how policy will have to change in response. This article relies on recent labour data of India's Periodic Labour Force Survey (PLFS, 2023-24) and discusses the vulnerabilities to disruption by location and social groups. It recommends viable actions that can be taken to ensure that risks are minimised and economic benefits maximised.

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India’s Labour Market and Its Automation Readiness

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According to India’s Periodic Labour Force Survey (PLFS), the labour market is changing and growing. Participation in the labour force improved to 60.1 per percent in 2023-24 versus 57.9 per cent the year before, and the ratio of the worker population also improved, signifying the increased employment uptake both in the rural and urban geographies (PLFS, 2023-24). There has also been an upsurge of female involvement. However,  a big portion of the job market has been low-wage and informal, with most of the jobs being routine and thus most vulnerable to automation. The statistics indicate a two-tiered reality of the Indian labour market: an increased number of working individuals and a structural weakness.

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AI-Driven Automation’s Impact on Tasks and Emerging Opportunities 

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AI-driven automation, for the most part,  affects the task components of jobs rather than wiping out whole jobs. The most automatable tasks are routine and manual, and more recent developments in AI have extended to non-routine cognitive tasks like document review, customer query handling, basic coding and first-level decision-making. There are two concurrent findings of global studies. To start with, part of the ongoing tasks will be automated or expedited. Second, there will be completely new tasks and work positions around data annotation, the operation of AI systems, prompt engineering, algorithmic supervision and AI adherence (World Bank, 2025; McKinsey, 2017).

In the case of India, this change will be skewed by sector. The manufacturing, back-office IT services, retail and parts of financial services will see the highest rate of disruption due to the concentration of routine processes with the ease of technology adoption. In comparison, healthcare, education, high-tech manufacturing and AI safety auditing are placed to create new skilled jobs. NITI Aayog estimates huge returns in GDP with the adoption of AI but emphasises that India has to invest simultaneously in job creation and reskilling to achieve the returns (NITI Aayog, 2025).

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Groups with Highest Vulnerability in the Transition to Automation

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The PLFS emphasises that a large portion of the Indian population does not have any formal employment and that the social protection is minimal and formal training is not available to them. The risk of displacement is likely to be the greatest for informal employees, making up almost 90% of India’s labour force, who carry out low-skilled, repetitive jobs in the manufacturing and retail industry (PLFS, 2023-24). Women and young people in low-level service jobs also face a greater challenge of transition pressure unless the reskilling and placement efforts can be tailored to them. Meanwhile, major cities and urban centres are likely to have openings for most of the new skilled opportunities at the expense of an increasing geographic and social divide. 

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The Skills and Supply Challenge

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While India’s education and research ecosystem is expanding, there remain significant gaps in preparing the workforce for AI-driven change. Given the vulnerabilities highlighted earlier, AI-focused reskilling must be a priority to equip workers with practical skills that meet industry needs. Short modular programs in areas such as cloud technologies, AI operations, data annotation, human-AI interaction, and cybersecurity can provide workers with employable skills. Particular attention should be given to routine-intensive sectors like manufacturing, retail, and back-office services, as well as to regions with high informal employment or lower access to formal training. Public-private partnerships and localised training initiatives can help ensure that reskilling translates into concrete job opportunities rather than purely theoretical knowledge (NITI Aayog, 2025)

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The Way Forward

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To facilitate the change process, the policy should focus on three interconnected goals: safeguarding the vulnerable, developing competencies on a large-scale level, and directing innovation towards the widespread ability to benefit.

1. Protect the vulnerable through social buffers. Provide informal workers with social protection in the form of portable benefits, temporary income insurance based on reskilling, and earned training leave. While the new labour codes provide essential protections such as unemployment allowances and minimum wage standards, they could be strengthened by incorporating explicit provisions for reskilling. This would better support informal workers during job transitions and enhance workforce adaptability.
2. Short modular courses on cloud computing, cybersecurity, data annotation, AI operations, and human-AI interaction should be planned through collaboration between public and private training providers. Special preference should be given to industry-certified certifications and apprenticeship-based placements. These apprenticeships should be made accessible in multiple languages to ensure inclusivity. Existing government initiatives, such as NASSCOM’s Future Skills Prime, need better outreach and marketing to reach the workforce effectively.
3. Enhance local labour market mediators. Close the disparity between local demand and the supply of labour in the industry by enhancing placement services and government-subsidised internship programmes for displaced employees and encouraging firms to hire and train locally.
4. Invest in AI literacy, AI ethics, and basic education. Democratise access to research and learning by introducing AI literacy in schools, increasing STEM seats in universities, and creating AI labs in the region (NITI Aayog, 2025).
5. Encourage AI adoption that creates jobs rather than replaces them. Fiscal and regulatory incentives should prioritise AI tools that augment worker productivity in routine roles instead of eliminating positions. Public procurement can support firms that demonstrate responsible and inclusive deployment of AI, ensuring technology benefits both business and workforce.
6. Supervise and oversee the transition. Use PLFS and real-time administrative data to monitor shrinking and expanding occupations. High-frequency labour market dashboards will allow making specific interventions in those regions in which the acceleration of displacement occurs.

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Conclusion

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The integration of AI will significantly impact the future of the Indian workforce, but policy will determine its effect on the labour market.  The PLFS indicates increased employment but a structural weakness of informal and routine employment. Evidence from the Indian market and international research points to the fact that the appropriate combination of social protection, skills building and responsible technology implementation can change disruption into a path of upward mobility. There is a very limited window of action. The extent to which India will realise the productivity and GDP benefits predicted by national research, alongside the investments made in labour market infrastructure, remains uncertain. It is crucial that these efforts lead to the capture of gains and facilitate a fair and inclusive transition for workers.

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References

• Annual Report Periodic Labour Force Survey (PLFS) JULY 2022 - JUNE 2023. 
• Future Jobs: Robots, Artificial Intelligence, and Digital Platforms in East Asia and Pacific, World Bank. 
• Jobs Lost, Jobs Gained: What the Future of Work Will Mean for Jobs, Skills, and Wages, McKinsey Global Institute 
• Roadmap for Job Creation in the AI Economy, NITI Aayog
• India central bank chief warns of financial stability risks from growing use of AI, Reuters 
• AI Cyber Attacks Statistics 2025, SQ Magazine. 

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