#FactCheck-No, Thai Transgender Women Did Not Assault Indian Army Lieutenant General
Executive Summary
A video is rapidly going viral on social media, with the claim that transgender women assaulted an Indian Army officer in Thailand. The post further claims that the officer had failed to pay the agreed amount, following which the incident took place. A research by CyberPeace’s research wing found that the viral video is fake. In fact, the video shows an incident that took place in Pattaya, Thailand, in December 2025. The 52-year-old Indian national, Raj Jasuja, was allegedly assaulted by a group of transgender women following a dispute over payment for sexual services. Therefore, the claim made in the post is false.
Claim:
A video shared on social media claims that transgender women assaulted an Indian Army general in Thailand.
https://x.com/navyhato/status/2094322001490116659
https://archive.ph/Scfgd
Fact Check:
To verify the claim, we conducted a Google search using relevant keywords but found no credible media reports confirming that Indian Army Lieutenant General Rajiv Kumar Sahni was involved in the incident. During the subsequent research, we compared the viral screenshot with the original report published by The Times of India. The comparison revealed that the original headline, “Indian national Raj Jasuja thrashed by transwomen in Thailand after allegedly refusing to pay for escort service,” had been digitally altered to read, “Indian Army Lieutenant General Rajiv Kumar Sahni thrashed by transwomen in Thailand after allegedly refusing to pay for escort service.” Further, both the viral screenshot and the original report display the same breadcrumb trail — “News / Indian National Raj Jasuja Thrashed By Transwomen In T…”. This provides further evidence that the headline in the viral screenshot was digitally manipulated.
After establishing the context of the viral claim, we conducted a Google search using relevant keywords. During the search, we found a report published on The Indian Express website on January 5, 2026, which featured the same visuals as those seen in the viral video. According to the report, a 52-year-old Indian tourist was allegedly assaulted by a group of transgender women in Pattaya, Thailand. The incident reportedly stemmed from a dispute over an unpaid fee for sexual services. The viral video, recorded on December 27, shows three transgender women allegedly assaulting the man with slippers. According to the report, the dispute escalated after the man allegedly refused to pay the amount demanded and attempted to leave the spot in a car. One of the transgender women reportedly accused him of refusing to make the payment. He was subsequently kicked and beaten before emergency responders intervened.
In the next step of our research, we found another report published on the India Today website on January 5, 2026. The report featured the same visuals as those seen in the viral video. According to the report, the incident took place on the morning of December 27, 2025, in the Walking Street area of Pattaya, Thailand.
Conclusion:
Our research found that the claim that Indian Army Lieutenant General Rajiv Kumar Sahni was assaulted in Thailand is false. The viral video actually shows an incident that took place in Pattaya, Thailand, in December 2025, in which 52-year-old Indian national Raj Jasuja was allegedly assaulted by a group of transgender women following a dispute over payment for sexual services. The viral post falsely links the incident to an Indian Army officer, thereby misrepresenting the identity of the person involved.
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Executive Summary
Amid the floods in Nepal, several videos are being shared on social media, claiming to show the current situation in the country. A research done by the Research Wing of the CyberPeace found that the video showing elephants is from an incident that occurred before the floods in Nepal, while the video showing the street is likely AI-generated.
Claim 1
Videos claiming to show a street being swept away by flash floods in Nepal and a herd of elephants being carried away by the floods and reaching Uttar Pradesh are going viral on social media.
https://www.instagram.com/p/DcimGaiiCa7/
Claim 2
A newly surfaced video purportedly shows the devastation caused by floods in Nepal, with cars and homes being destroyed. It is being claimed that 350 people have died and 750 are missing so far
https://x.com/UTDbrims/status/2092829398462341484?s=48
Fact Check 1
The first video claims to show a herd of wild elephants that was swept away by the floods in Nepal and reached Uttar Pradesh. We conducted a reverse image search of the first video using Google Lens and found a report by Hindi news publication Dainik Bhaskar, which shared a video featuring visuals similar to those in the viral clip. According to the report, a herd of wild elephants coming from the Gerua River reached the gates of the Chaudhary Charan Singh Ghaghra Barrage in Bahraich district, Uttar Pradesh, due to the strong current of the river. The report also quoted the Forest Range Officer as saying that the area is adjacent to an elephant corridor, which is why elephants frequently move through the region.
Fact Check 2
The second video shows a street being completely swept away by floodwaters. We conducted a reverse image search of the video using Google Lens but could not find any credible source that had shared the footage. Following this, we ran the video through the AI detection tool Hive Moderation, which flagged it as 99 percent likely to be AI-generated.
Conclusion
Our research found that the viral videos being shared as scenes of devastation caused by the recent floods in Nepal are misleading. The video showing a herd of elephants is from a separate incident in Bahraich, Uttar Pradesh, and predates the Nepal floods. Meanwhile, the video showing a street being swept away could not be traced to any credible source and was flagged as 99 percent likely to be AI-generated by Hive Moderation.
Executive Summary
A video is going viral on social media showing ‘injured’ security personnel being carried into ambulances. The clip is being shared with claims that a terrorist attack recently took place in Kishtwar. The video surfaced nearly a year after the terror attack in Pahalgam on April 22, 2025, adding to confusion among users online. Research by CyberPeace Research Wing found that the claim is false. The viral video is actually from a mock drill conducted in Kishtwar, not a real terror incident.
Claim
An Instagram user ‘thenewjbharat’ shared the video on April 30, 2026, claiming that a terrorist attack had taken place again in Kishtwar.
https://www.instagram.com/thenewjbharat/
Fact Check
To verify the claim, we extracted keyframes from the viral video and conducted a reverse image search using Google Lens. This led us to the same clip uploaded on April 24, 2026 by an Instagram user ‘kishtwar_breaking_news’. According to the post, the video shows a mock drill conducted by local authorities to assess emergency preparedness. Officials and rescue teams participated in the exercise.
We also found a related news video uploaded on April 23, 2026, by the YouTube channel of Daily Excelsior, which featured visuals matching the viral clip. The report confirmed that the drill was carried out to evaluate readiness for emergency situations.
Conclusion
Our research confirms that the viral video does not show a real terrorist attack. It is footage from a mock drill conducted in Kishtwar and is being falsely shared with misleading claims.
The Expanding Governance Challenge of Artificial Intelligence
Artificial intelligence (AI) systems are increasingly embedded in economic and social infrastructure. They are being adopted in financial services, healthcare diagnostics, hiring systems, and public administration. But while these systems improve efficiency and decision-making, they also introduce new forms of technological risk.
Unlike conventional software, AI systems learn patterns from data and continue to evolve as they run. This poses governance issues since risks can arise throughout the AI life cycle, whether at the coding level or in their implementation.
The latest regulatory frameworks, such as the European Union’s AI Act (EU AI Act) and the UNESCO Recommendation on the Ethics of Artificial Intelligence, note that responsible AI governance depends on the realisation of where risks emerge across the development process.
This article maps the AI system lifecycle, identifies the risks that emerge at each stage and evaluates the policy tools used to mitigate them using the lifecycle framework developed by the Organisation of Economic Co-operation and Development (OECD).
The Lifecycle of an AI System
AI systems are developed through a structured process that includes problem definition, dataset collection and preparation, model development, testing and validation, deployment, and monitoring.
The OECD conceptualises this development process as the AI system lifecycle. Each stage entails various technical and administrative procedures, since choices made during these stages will dictate the goals and limits of an AI system. Further, the quality and representativeness of training sets will have a strong effect on the behaviour of models after implementation.
Since this is an iterative and not a linear procedure, risks can be introduced at each stage of the AI lifecycle. New data can be retrained into different models, and systems are regularly updated once they have been deployed, to address performance degradation, model errors, or unintended outputs. This iterative process means governance must address risks across the entire lifecycle, not just at deployment.
Where AI Risks Emerge
AI risks usually emerge earlier in the development process, especially in the phases when system objectives are formulated and training data are chosen. The EU AI Act and the UNESCO Recommendation on the Ethics of AI outline the following risks: bias and discrimination, privacy and data security violations, the absence of transparency in automated decision-making, and risks to fundamental rights.
AI Governance Risk Landscape: Core Risk Categories Under International Frameworks
Risk categories jointly identified by the EU AI Act and UNESCO Recommendation on the Ethics of Artificial Intelligence
Outlining the risks throughout the AI lifecycle helps understand the areas where governance interventions are most necessary. For example, discriminatory outcomes often result from biased or unrepresentative training data, while safety failures are typically linked to inadequate testing before deployment. Risks such as misinformation arise post the development process, when generative AI systems are deployed at scale on digital platforms.
AI System Lifecycle: Key Risks at Each Stage
Risks identified per the EU AI Act and UNESCO Recommendation on the Ethics of AI
Understanding where risks emerge across the lifecycle explains why governance frameworks classify AI systems by risk and apply oversight at multiple stages.
Policy Tools for Mitigating AI Risks
Governments and international organisations have developed regulatory tools to help mitigate AI risks in the lifecycle. These tools are meant to make sure that AI technologies are identified as up to standard in safety, accountability and fairness prior to and after deployment.
For example, the OECD AI Policy Observatory recommends that governments adopt policy instruments such as risk evaluations, algorithmic auditing necessities, regulatory sandboxes, and transparency necessities of AI systems. The European Union’s Artificial Intelligence Act (AI Act) is one of the most comprehensive systems of governance that introduces a risk-oriented regulation strategy. It mandates adherence to requirements concerning data governance, documentation, human oversight, and robustness, and cybersecurity. Such requirements bring regulatory checkpoints to the lifecycle of AI systems.
Mapping these policy tools across the lifecycle illustrates how governance mechanisms can intervene at different stages of AI development.
Governance Overlay: Policy Interventions Across the AI Lifecycle
Regulatory tools mapped at each stage of AI development per the EU AI Act and UNESCO Recommendation on the Ethics of AI
Several policy tools are directed at the risks that occur in the pre-developmental stages. In one example, algorithmic impact assessment has been applied in various jurisdictions to measure the possible consequences of automated decision systems on society before implementation. On the same note, the requirements of dataset documentation, including dataset transparency requirements and model cards, are aimed at enhancing accountability during the training and development stages of the AI systems. Therefore, lifecycle-based policy design allows regulators to intervene before harmful outcomes occur, rather than responding only after AI systems have caused damage in real-world environments.
The Policy Gap in AI Governance
The misalignment between risks and governance tools across the AI lifecycle indicates a critical structural gap in existing regulations. Numerous governance processes become activated after AI systems are classified as “high risk” or after they are implemented in the real world. But the most serious sources of damage have their roots in earlier stages of the development procedure.
An example is that prejudiced or unbalanced training data is almost inevitably a source of discriminative results in automated decision systems. When these types of models are applied in areas like staffing, credit rating, or in providing services to the public, such biases can quickly spread to large populations and undermine democratic rights. In the same way, the lack of transparency in model design might result in the fact that the regulator or individuals are affected by the decision-making process. This reflects a broader timing gap in AI governance, where risks originate during design and development, but regulatory intervention typically occurs only after deployment.
Analysis
1. Key risks originate before deployment: As depicted in the lifecycle mapping, the data collection and model development phase presents several significant governance risks as opposed to the deployment phase. Structural issues can be entrenched within AI systems even before they are deployed in practice due to bias in data sets, incomplete reporting of training sets, and obscured network designs.
2. Data governance is a primary point of vulnerability: Most of the instances of algorithmic discrimination listed above are associated with training material that is not representative of some population groups or is historical. Since machine learning models are optimisations of patterns that exist in datasets, these biases can be carried through the whole lifecycle and reproduced after deployment.
3. Regulatory approaches remain mismatched across jurisdictions: Different countries adopt varying approaches to AI governance, ranging from risk-based frameworks such as the EU AI Act to more sector-specific or voluntary guidelines in other regions. This divergence creates inconsistencies in safety, accountability, and enforcement standards, allowing risks to persist across borders and potentially undermining the protection of users in globally deployed AI systems.
4. Governance interventions remain uneven across the lifecycle: Whereas the various regulatory instruments aim at deployment and monitoring, fewer instruments systematically tackle the risks that are posed by the previous design and development phases.
Recommendations
1. Introduce mandatory lifecycle risk assessments: The regulatory systems need to demand systemic risk evaluation at the beginning of AI development, especially at the problem design and dataset selection phases. This would assist in detecting possible harmful applications in advance, before systems are constructed and installed.
2. Strengthen dataset governance standards: Training datasets must be supplemented with documentation as to their provenance, composition and limitations. Standardised documentation frameworks of data sets can assist in the discovery by regulators and auditors of the potential sources of bias or privacy threats.
3. Expand independent algorithmic auditing: AI systems can be assessed by regular third-party audits based on fairness, strength, and security weaknesses. The auditing mechanisms especially apply to high-risk systems employed in employment, finance or the public services.
4. Integrate continuous monitoring requirements: AI systems may be monitored regularly after implementation to identify model drift, unforeseen consequences, or abuse. Reporting systems can facilitate the process where the regulators can see the emerging risks and modify the governance systems.
Conclusion - The Need for Global AI Governance
Despite growing regulatory attention, global air governance remains fragmented. Different jurisdictions adopt varying approaches to risk classification, oversight, and enforcement, leading to inconsistencies in safety and accountability standards. Given that AI systems are often developed, deployed, and used across borders, this lack of coordination allows risks to persist beyond national regulatory frameworks.
Addressing these challenges requires a shift towards greater international cooperation and lifecycle-based governance. Developing shared standards, improving cross-border regulatory alignment, and embedding oversight across all stages of AI development will be essential to ensuring that AI systems are safe, transparent, and accountable in a globally interconnected environment.
References
- OECD AI lifecycle
- OECD AI system lifecycle description
- OECD AI governance lifecycle framework
- EU AI Act overview
- EU AI Act risk categories
- UNESCO Recommendation on the Ethics of AI
- AI governance lifecycle analysis
- OECD AI policy tools database
