#FactCheck-AI-Generated Video Falsely Shows Samay Raina Making a Joke on Rekha

Introduction
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The rapid rise of AI tools has reshaped how health content spreads on platforms like Instagram Reels and YouTube Shorts. These sub-minute videos promise quick fixes for weight loss, glowing skin, or reduced anxiety, often delivered through polished visuals and confident AI-generated voiceovers. The result feels highly personalised, as if the advice is tailored to each viewer, even though it is usually generic and widely recycled.

Short-form videos tend to compress complex health topics into “one tip” solutions, such as drinking a specific detox drink daily or following a single workout for rapid fat loss. While appealing, this oversimplification removes essential context, including individual health conditions, long-term risks, and scientific nuance. For example, viral diet trends or fitness hacks may work for some but can be ineffective or even harmful for others.

Algorithms play a major role in amplifying such content. Videos that promise dramatic transformations or instant results are more likely to gain engagement, which pushes them to wider audiences. Repeated exposure then builds familiarity, making the advice seem more credible over time. Audiences often trust this content due to its clean presentation, authoritative tone, and frequent repetition. However, the risks include misinformation, unrealistic expectations, and potential harm from unverified practices. To approach such content critically, viewers should cross-check claims with credible medical sources, avoid relying on single tip solutions, and remember that real health advice is rarely one size fits all.
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The Illusion of Personalisation
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AI-generated health content often mimics personalisation through:

• Synthetic voiceovers that designers created to match different age groups through their voice output, which speak specifically to people who are 20 years old and younger.
• The script development process uses data that tracks currently popular search terms.
• Viewers can interpret information through visual elements, which show changes between two different states.

The process of "personalisation" uses generalised data that does not match individual health profiles to create targeted results. The videos fail to provide a medical assessment because they do not consider:

• Existing medical conditions
• Hereditary differences
• Personal habits and the impact of surrounding conditions

The users will think that general medical advice applies to their personal health needs, which will lead them to use this advice inappropriately.

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Short-Form Content and Oversimplification
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Short-form videos have time limitations, which result in reduced complex medical information development into basic medical stories. The typical patterns of evaluation include these two patterns of evaluation include:

• “One-tip solutions” (e.g., “Drink this before bed to burn fat”)
• Binary framing (“good vs bad foods”)
• The process of eliminating all disclaimers and side effects information

For example, the three diet methods here the three diet methods here

• Viral detox drinks that make the claim to "flush toxins" from the body
• Extreme calorie-cutting diet hacks
• Fitness shortcuts that guarantee users will see results within days

The content demonstrates a pattern of disregarding essential human body operation rules that include both metabolic patterns and human body operation over extended periods of time.
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Algorithmic Amplification and Virality
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The recommendation algorithms used by Instagram and YouTube deliver their most important results through three main factors, which include:

• Engagement (likes, shares, watch time)
• Retention rates
• Emotional or aspirational triggers 

Health-related content that claims to deliver:

• Immediate body changes
• Needs minimal work from viewers
• Results in extreme physical changes

Attractive health-related content that displays extreme physical changes through quick transformations. The system produces a continuous cycle during which:

1. Misleading content gains traction
2. Algorithms amplify it further
3. More creators replicate similar formats using AI tools 

The system produces a secondary result that favours content that people share instead of content that has authentic credibility.
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Why Do Users Trust AI-Generated Health Content?
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Several psychological and technological factors contribute to trust:

1. Professional Aesthetics - AI tools generate high-quality visual content together with authentic voiceover performance and expert-level script documentation, which replicates professional communication methods.
2. Repetition and Familiarity - When people encounter identical recommendations multiple times, their belief in those recommendations increases through the illusory truth effect.
3. Authority Signals

• Medical terminology serves as a standard term
• Medical professionals appear in stock footage through lab coat visuals
• The narrator delivers information through an assertive speaking style

4. Perceived Personal Relevance - Algorithmic targeting makes users feel the content is "meant for them.

Real-World Examples of Viral Trends
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The typical types of health misinformation that artificial intelligence systems spread through their enhanced capabilities include:

• Diet Trends: Keto shortcuts, extreme intermittent fasting variants
• Fitness Hacks: Spot reduction exercises (scientifically unsupported)
• Supplement Advice: Unverified claims about vitamins or herbal products
• Mental Health Tips: Oversimplified coping strategies that lack clinical evidence

The statement that drinking warm lemon water will detox your liver continues to be popular despite the fact that the liver has natural self-detoxification abilities.

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Risks and Public Health Implications
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The widespread consumption of such content creates multiple dangers, which include:

1. Physical Health Risks

• Nutritional deficiencies from extreme diets
• Injury from improper exercise techniques
• Delayed medical consultation

2. Psychological Impact

• Unrealistic body image expectations
• Anxiety due to conflicting advice

3. Misinformation Ecosystem

• The public loses confidence in evidence-based medicine
• Unverified or pseudoscientific practices spread throughout society

Regulatory and Ethical Concerns

The increase of AI-generated health materials connects to more extensive problems, which include:

• Who is responsible for the content
• Who is responsible for the platform
• How AI systems show their inner workings to users

Most platforms today do not have strict systems that can:

• Verify medical claims
• Display which health advice comes from artificial intelligence
• Punish users who spread false information multiple times

The absence of regulations allows misleading information to spread without consequences.
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A CyberPeace Perspective: Building Digital Health Resilience
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The problem needs complete involvement from several parties to create effective solutions that protect both online security and data integrity. 
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For Users

• Users should confirm claims by using trustworthy medical resources, which include the WHO and peer-reviewed studies.
• People should avoid using "quick solutions" until they receive guidance from certified experts.
• Users should exercise caution when they encounter content that does not include necessary warning signs.

For Platforms

• Platforms should implement systems that enable users to identify AI-generated content.
• Platforms should decrease the visibility of health information that contains false statements.
• Platforms should support authentic health content producers who have been validated.

For Policymakers

• Policymakers should create standards that govern AI-produced medical content.
• Policymakers need to enhance initiatives that teach people about the health information available online.

For Content Creators

• Content creators must show how they implement AI technologies.
• They should stay away from making claims that either go beyond what is needed or state things as absolute truth.

Conclusion
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AI-generated health tips on short-form video platforms create complex research conditions that involve three scientific fields: technology, psychology and public health. The tools provide equal access to information, yet create higher risks for people to believe false information when they use the tools without responsible usage. 

The challenge requires organisations to maintain user safety through accurate information management while providing users with transparent digital health services. The growing dependence of users on algorithm-based content requires educational institutions to develop students' critical thinking abilities and digital skills to minimise negative effects from AI-driven communication methods.
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References
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• https://pmc.ncbi.nlm.nih.gov/articles/PMC12924558/
• https://academic.oup.com/heapro/article/40/2/daaf023/8100645
• https://pmc.ncbi.nlm.nih.gov/articles/PMC12673052/
• https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2025.1713794/full
• https://www.who.int/teams/digital-health-and-innovation/digital-channels/combatting-misinformation-online
• https://link.springer.com/article/10.1186/s12982-025-00777-2
• https://www.washingtonpost.com/health/2026/04/21/chatbot-medical-advice-accurate/

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Somewhere in a compliance meeting right now, someone is saying "we have eighteen months, we're fine." That sentence is doing the same thing a snooze button does at 6 a.m.: technically buying time, while quietly making the actual wake up call worse. India's data protection law just started its countdown, and the 18 months everyone keeps citing is not a grace period to procrastinate through. It is closer to a runway before takeoff. Runways exist for one purpose: building up speed until the plane has no choice but to leave the ground. Standing still on one is not a strategy.

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What actually got notified, and when

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On 13 November 2025, the Ministry of Electronics and Information Technology notified the Digital Personal Data Protection Rules, 2025, giving operational shape to the Digital Personal Data Protection Act that Parliament had passed back in August 2023. Alongside the Rules themselves, MeitY issued a separate Enforcement Notification setting out exactly when different provisions kick in, and a further notification establishing the Data Protection Board of India, headquartered in the National Capital Region with four members. The final Rules followed a genuinely deliberative process, MeitY had floated draft Rules in January 2025 for public consultation and received 6,915 individual inputs from startups, industry bodies, civil society groups, and citizens before finalising the version now in force. The headline structural decision, and the one causing the most confusion in boardrooms, is that the Rules do not commence all at once. They commence in three distinct phases spread across eighteen months, and different obligations become legally binding at each stage.

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The phased timeline, laid out plainly

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That third date, 13 May 2027, is the one that matters most for the vast majority of organisations, since it is where the bulk of actual operational obligations, the parts that touch product design, customer facing notices, and breach response, become enforceable. Legal commentary tracking the rollout has been consistent that this is described as a hard deadline with no grace period expected once it arrives, since the Data Protection Board is already operational and can begin receiving complaints well before Phase 3 obligations formally take effect.

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Why "later" is a genuinely expensive plan

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The financial stakes attached to Phase 3 non-compliance are not modest. The Schedule to the DPDP Act sets fixed penalty ceilings rather than turnover linked fines, which sounds gentler than Europe's GDPR model until you look at the actual numbers. Failure to implement reasonable security safeguards that results in a data breach can draw a penalty of up to 250 crore rupees per instance, the single highest tier in the Schedule. Failing to notify the Board or affected individuals after a breach occurs can draw up to 200 crore rupees, as can non-compliance with the Act's specific protections for children's data. Because these are assessed per instance rather than as a single capped exposure, a single incident that trips more than one obligation, say, inadequate safeguards that also delay breach notification, can compound into penalty exposure running into hundreds of crores from one event. All penalties collected go to the Consolidated Fund of India rather than to affected individuals directly, meaning the deterrent is aimed squarely at organisational behaviour, not compensation.

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The part everyone keeps underestimating: this is not just a legal department problem

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Perhaps the most consequential shift buried inside the DPDP framework is who actually has to own it. Reading the Rules as a checklist for the legal or privacy team alone misses how far the obligations actually reach. Building a compliant consent lifecycle touches product design. Security safeguards touch cybersecurity and IT infrastructure directly. Retention and deletion logic touches data governance and engineering. Third party risk review touches procurement. Breach preparedness touches internal audit and incident response. And increasingly, as organisations deploy AI systems that process personal data, AI governance enters the picture too, since a model trained or fine tuned on personal data inherits the same DPDP obligations as any other processing activity.

That cross functional reality is where most readiness programmes currently fall short. Treating DPDP compliance as a documentation exercise, updating a privacy policy PDF and calling it done, produces the appearance of compliance without the operational substance a Data Protection Board investigation would actually test. A breach response plan that exists only on paper and has never been rehearsed will not hold up against the 72 hour data principal notification window the Rules impose once Phase 3 lands. A consent mechanism bolted onto a website without corresponding backend logic to honour withdrawal requests will not satisfy an actual audit.

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What a serious readiness posture looks like right now

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Organisations that are ahead of this curve are already treating the eighteen month window as three overlapping workstreams rather than one deadline to hit at the end. 

• The first is discovery: mapping what personal data exists, where it flows, who owns each system that touches it, and why it is collected in the first place, since compliance is structurally impossible without first knowing what you are protecting. This stage typically surfaces uncomfortable findings, shadow data sets nobody formally owns, vendor integrations nobody fully mapped, legacy systems still holding data well past any reasonable retention justification. 
• The second is build: standing up the actual mechanisms, consent flows that can genuinely honour a withdrawal request end to end, rights request handling that does not depend on a single overworked employee checking an inbox, retention and deletion logic wired into the systems themselves rather than described only in a policy document, and security controls proportionate to the sensitivity of what is being protected. 
• The third is proof: generating the internal evidence, audit trails, documented decisions, tested response procedures, that demonstrates governance was real rather than retrofitted after the fact. A Data Protection Board investigation, when it eventually happens, will not be satisfied by a well written policy; it will look for evidence that the policy was actually operational.

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The actual question worth asking

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The right question was never "when does DPDP become enforceable." Phase 1 already answered that; the law is live, and the Data Protection Board already exists and can act. The better question, the one worth taking into any leadership review between now and May 2027, is simpler and considerably less comfortable: will the organisation actually be ready when each phase's obligations become operational, or will readiness be assembled in a scramble once the deadline stops being theoretical. 18 months sounds long right up until the week it does not, and by the time Phase 3 lands, "we'll get to it" will no longer be a sentence any organisation gets to finish.

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References

1. Ministry of Electronics and Information Technology, Digital Personal Data Protection Rules, 2025, notified 13 November 2025. Press Information Bureau, "Digital Personal Data Protection Rules, 2025 Notified," 14 November 2025. https://static.pib.gov.in/WriteReadData/specificdocs/documents/2025/nov/doc20251117695301.pdf
2. Shardul Amarchand Mangaldas & Co, "Enforcement of the DPDP Act and notification of the DPDP rules." https://www.amsshardul.com/insight/enforcement-of-the-dpdp-act-and-notification-of-the-dpdp-rules/
3. S&R Associates, "India's Digital Personal Data Protection Regime Takes Effect." https://www.snrlaw.in/indias-digital-personal-data-protection-regime-takes-effect/
4. Khurana & Khurana, "MeitY Notifies Rules Operationalising The DPDP Framework." https://www.khuranaandkhurana.com/update-meity-notifies-rules-operationalising-the-dpdp-framework-in-india
5. Exchange4media, "DPDP Act 2025: Penalties for violations can reach Rs 250 crore." https://www.exchange4media.com/digital-news/dpdp-act-2025-penalties-for-confirmed-violations-can-reach-rs-250-crore-149360.html
6. Seclore, "DPDP Rules 2025: India's Complete Compliance Guide." https://www.seclore.com/fundamentals/dpdp-rules-2025-compliance-guide/

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Introduction

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The use of AI in content production, especially images and videos, is changing the foundations of evidence. AI-generated videos and images can mirror a person’s facial features, voice, or actions with a level of fidelity to which the average individual may not be able to distinguish real from fake. The ability to provide creative solutions is indeed a beneficial aspect of this technology. However, its misuse has been rapidly escalating over recent years. This creates threats to privacy and dignity, and facilitates the creation of dis/misinformation. Its real-world consequences are the manipulation of elections, national security threats, and the erosion of trust in society. 

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Why India Needs Deepfake Regulation

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Deepfake regulation is urgently needed in India, evidenced by the recent Rashmika Mandanna incident, where a hoax deepfake of an actress created a scandal throughout the country. This was the first time that an individual's image was superimposed on the body of another woman in a viral deepfake video that fooled many viewers and created outrage among those who were deceived by the video. The incident even led to law enforcement agencies issuing warnings to the public about the dangers of manipulated media.

This was not an isolated incident; many influencers, actors, leaders and common people have fallen victim to deepfake pornography, deepfake speech scams, defraudations, and other malicious uses of deepfake technology. The rapid proliferation of deepfake technology is outpacing any efforts by lawmakers to regulate its widespread use. In this regard, a Private Member’s Bill was introduced in the Lok Sabha in its Winter Session. This proposal was presented to the Lok Sabha as an individual MP's Private Member's Bill. Even though these have had a low rate of success in being passed into law historically, they do provide an opportunity for the government to take notice of and respond to emerging issues. In fact, Private Member's Bills have been the catalyst for government action on many important matters and have also provided an avenue for parliamentary discussion and future policy creation. The introduction of this Bill demonstrates the importance of addressing the public concern surrounding digital impersonation and demonstrates that the Parliament acknowledges digital deepfakes to be a significant concern and, therefore, in need of a legislative framework to combat them.

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Key Features Proposed by the New Deepfake Regulation Bill

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The proposed legislation aims to create a strong legal structure around the creation, distribution and use of deepfake content in India. Its five core proposals  are: 

1. Prior Consent Requirement: individuals must give their written approval before producing or distributing deepfake media, including digital representations of themselves, as well as their faces, images, likenesses and voices. This aims to protect women, celebrities, minors, and everyday citizens against the use of their identities with the intent to harm them or their reputations or to harass them through the production of deepfakes.

2. Penalties for Malicious Deepfakes: Serious criminal consequences should be placed for creating or sharing deepfake media, particularly when it is intended to cause harm (defame, harass, impersonate, deceive or manipulate another person). The Bill also addresses financially fraudulent use of deepfakes, political misinformation, interfering with elections and other types of explicit AI-generated media. 

3. Establishment of a Deepfake Task Force: To look at the potential impact of deepfakes on national security, elections and public order, as well as on public safety and privacy. This group will work with academic institutions, AI research labs and technology companies to create advanced tools for the detection of deepfakes and establish best practices for the safe and responsible use of generative AI. 

4. Creation of a Deepfake Detection and Awareness Fund: To assist with the development of tools for detecting deepfakes, increasing the capacity of law enforcement agencies to investigate cybercrime, promoting public awareness of deepfakes through national campaigns, and funding research on artificial intelligence safety and misinformation.

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How Other Countries Are Handling Deepfakes

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1. United States
Many States in the United States, including California and Texas, have enacted laws to prohibit the use of politically deceptive deepfakes during elections. Additionally, the Federal Government is currently developing regulations requiring that AI-generated content be clearly labelled. Social Media Platforms are also being encouraged to implement a requirement for users to disclose deepfakes.

2. United Kingdom
In the United Kingdom, it is illegal to create or distribute intimate deepfake images without consent; violators face jail time. The Online Safety Act emphasises the accountability of digital media providers by requiring them to identify, eliminate, and avert harmful synthetic content, which makes their role in curating safe environments all the more important.

3. European Union:
The EU has enacted the EU AI Act, which governs the use of deepfakes by requiring an explicit label to be affixed to any AI-generated content. The absence of a label would subject an offending party to potentially severe regulatory consequences; therefore, any platform wishing to do business in the EU should evaluate the risks associated with deepfakes and adhere strictly to the EU's guidelines for transparency regarding manipulated media.

4. China:
China has among the most rigorous regulations regarding deepfakes anywhere on the planet. All AI-manipulated media will have to be marked with a visible watermark, users will have to authenticate their identities prior to being allowed to use advanced AI tools, and online platforms have a legal requirement to take proactive measures to identify and remove synthetic materials from circulation.

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Conclusion

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Deepfake technology has the potential to be one of the greatest (and most dangerous) innovations of AI technology. There is much to learn from incidents such as that involving Rashmika Mandanna, as well as the proliferation of deepfake technology that abuses globally, demonstrating how easily truth can be altered in the digital realm. The new Private Member's Bill created by India seeks to provide for a comprehensive framework to address these abuses based on prior consent, penalties that actually work, technical preparedness, and public education/awareness. With other nations of the world moving towards increased regulation of AI technology, proposals such as this provide a direction for India to become a leader in the field of responsible digital governance.

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References 

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1. https://www.ndtv.com/india-news/lok-sabha-introduces-bill-to-regulate-deepfake-content-with-consent-rules-9761943
2. https://m.economictimes.com/news/india/shiv-sena-mp-introduces-private-members-bill-to-regulate-deepfakes/articleshow/125802794.cms
3. https://www.bbc.com/news/world-asia-india-67305557
4. https://www.akingump.com/en/insights/blogs/ag-data-dive/california-deepfake-laws-first-in-country-to-take-effect
5. https://codes.findlaw.com/tx/penal-code/penal-sect-21-165/ 
6. https://www.mishcon.com/news/when-ai-impersonates-taking-action-against-deepfakes-in-the-uk#:~:text=As%20of%2031%20January%202024,of%20intimate%20deepfakes%20without%20consent. 
7. https://www.politico.eu/article/eu-tech-ai-deepfakes-labeling-rules-images-elections-iti-c2pa/ 
8. https://www.reuters.com/article/technology/china-seeks-to-root-out-fake-news-and-deepfakes-with-new-online-content-rules-idUSKBN1Y30VT/

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