#FactCheck -AI-Altered Clip Falsely Attributes Taliban Comments to General Upendra Dwivedi
Executive Summary
A video clip of Indian Army Chief Upendra Dwivedi is being widely shared across social media platforms with the claim that he criticised the Indian government's policy towards Taliban-ruled Afghanistan. In the viral clip, the Army Chief is allegedly heard saying that India is doing nothing except sending money to the Taliban government due to the Centre’s failed policies.
However, CyberPeace Research Wing research found the claim to be false. The viral video is a deepfake. In the original footage, General Upendra Dwivedi was speaking about Operation Sindoor and the preparedness of the Indian Armed Forces for a possible “Operation Sindoor 2.0.” He made no remarks regarding the Taliban or the government’s Afghanistan policy.
Claim
An X user named “XaQil” shared the viral video on May 31, 2026, with the caption:“Due to failed policies of the Central Government, India is doing nothing except sending Money to Taliban government. How can money alone do everything?” — Army Chief General Upendra Dwivedi.
Fact Check
In the viral video, General Dwivedi is purportedly heard making remarks about India’s Afghanistan policy, the Taliban, Pakistan, Iran, and India’s diplomatic position. To verify the claim, we searched for the original source of the video. A reverse image search of key frames led us to the authentic footage posted by news agency ANI on its official X account on May 30, 2026.
In the original video, General Dwivedi was responding to a question about Operation Sindoor. He stated that the operation was still ongoing, hostilities had only paused temporarily, and that the Indian Armed Forces were fully prepared if “Operation Sindoor 2.0” became necessary.
He also spoke about enhancing coordination among the three services and maintaining operational readiness.”
No part of his statement mentioned the Taliban, Afghanistan, Pakistan, Iran, or criticism of the Central Government.
Further corroboration came from media reports covering the same event. According to a report published by Navbharat Times on May 30, 2026, General Dwivedi made the remarks during the passing-out parade of the 150th course of the National Defence Academy (NDA), where he attended as the chief guest. He reiterated that the armed forces were fully prepared for “Operation Sindoor 2.0” if required.
Since the content of the viral clip did not match the original statement, we examined it using InVID’s MeVer Deepfake Detector. The tool flagged signs of AI manipulation and indicated that the video had likely been altered.
Conclusion
Cyber Peace Foundation found that the viral video purportedly showing Army Chief General Upendra Dwivedi criticising the Indian government’s policy towards the Taliban is a deepfake. The Army Chief made no such remarks. The original video was recorded during an NDA event, where he spoke about Operation Sindoor and the preparedness of the Indian Armed Forces for a possible future operation. The viral clip has been manipulated using AI to spread a false narrative.
Related Blogs
Executive Summary
Amid political developments following the 2026 West Bengal Assembly elections, a photo is being widely circulated on social media claiming that former cricketer and Baharampur MP Yusuf Pathan has joined the Bharatiya Janata Party (BJP). The viral image shows Pathan wearing a BJP scarf and standing alongside Union Health Minister JP Nadda. CyberPeace Research Wing research found the claim to be false. The image in circulation is AI-generated and does not depict any real event.
Claim:
A Facebook user ‘Mohd Anwar Dhadoli Khurd’ shared the viral image on June 16, 2026, claiming that Yusuf Pathan has joined the BJP. The post has since been widely shared across platforms.
Post link: https://www.facebook.com/mohd.anwar.dhadoli.khurd/posts/pfbid02WAo4uXhhM1qEVTMbxTjKpTHkoeAbfEztwkX6tWLWpN8WBzNavgtJgKV8JoBGULSul
Archive link: https://perma.cc/4A3H-YTPL
Fact Check:
A reverse image search of the viral photo did not yield any credible news reports or authentic sources linking the image to any such political development. No related information was found on Yusuf Pathan’s official social media accounts either, raising suspicion about the authenticity of the image. The image was then analysed using AI detection tools. Sightengine reported a 99% probability of the image being AI-generated.
Additionally, analysis using “Undetectable.ai” also indicated a high likelihood of AI manipulation.
Conclusion:
The research confirms that the viral image claiming Yusuf Pathan’s entry into the BJP is AI-generated and does not reflect any real-world event.
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What is Deepfake
Deepfakes have been, a fascinating but unsettling phenomenon that is now prominent in this digital age. These incredibly convincing films have drawn attention and blended in well with our high-tech surroundings. The lifelike but completely manufactured quality of deepfake videos has become an essential component of our digital environment as we traverse the broad reaches of our digital society. While these works have an undoubtedly captivating charm, they have important ramifications. Come along as we examine the deep effects that misuse of deepfakes can have on our globalized digital culture. After many actors now business tycoon Ratan Tata has become the latest victim of deepfake. Tata called out a post from a user that used a fake interview of him in a video recommending Investments.
Case Study
The nuisance of deep fake is sparing none from actors politicians to entrepreneurs everyone is getting caught in the Trap. Soon after the actresses Rashmika Mandana, Katrina Kaif, Kajol and other actresses fell prey to the rising scenario of deepfake, a new case from the industry emerged, which took Mr. Ratan Tata on storm. Business tycoon Ratan Tata has become the latest victim of deepfake. He took to his social media sharing an image of the interview that asked people to invest money in a project in a post on Instagram. Ratan Tata called out a post from a user that used a fake interview of him in a video recommending these Investments.
This nuisance that has been created because of the deepfake is sparing nobody from actors to politicians to entrepreneurs now everyone is getting caught in the trap the latest victim being Ratan Tata. Tech magnate Ratan Tata is the most recent victim of this deepfake phenomenon. The millionaire was seen in the video, which was posted by the Instagram user, giving his followers a once-in-a-million opportunity to "exaggerate investments risk-free."
In the stated video, Ratan Tata was seen giving everyone in India advice mentioning to the public regarding the opportunity to increase their money with no risk and a 100% guarantee. The caption of the video clip stated, "Go to the channel right now."
Tata annotated both the video and the screenshot of the caption with the word "FAKE."
Ongoing Deepfake Assaults in India
Deepfake videos continue to target celebrities, and Priyanka Chopra is also a recent victim of this unsettling trend. Priyanka's deepfake adopts a different strategy than other examples, including actresses like Rashmika Mandanna, Katrina Kaif, Kajol, and Alia Bhatt. Rather than editing her face in contentious situations, the misleading film keeps her looking the same but modifies her voice and replaces real interview quotes with made-up commercial phrases. The deceptive video shows Priyanka promoting a product and talking about her yearly salary, highlighting the worrying development of deepfake technology and its possible effects on prominent personalities.
Prevention and Detection
In order to effectively combat the growing threat posed by deepfake technology, people and institutions should place a high priority on developing critical thinking abilities, carefully examining visual and auditory cues for discrepancies, making use of tools like reverse image searches, keeping up with the latest developments in deepfake trends, and rigorously fact-check reputable media sources. Important actions to improve resistance against deepfake threats include putting in place strong security policies, integrating cutting-edge deepfake detection technologies, supporting the development of ethical AI, and encouraging candid communication and cooperation. We can all work together to effectively and mindfully manage the problems presented by deepfake technology by combining these tactics and making adjustments to the constantly changing terrain.
Conclusion
The current instance involving Ratan Tata serves as an example of how the emergence of counterfeit technology poses an imminent danger to our digital civilization. The fake video, which was posted to Instagram, showed the business tycoon giving financial advice and luring followers with low-risk investment options. Tata quickly called out the footage as "FAKE," highlighting the need for careful media consumption. The Tata incident serves as a reminder of the possible damage deepfakes can do to prominent people's reputations. The issue, in Ratan Tata's instance specifically, demands that public personalities be more mindful of the possible misuse of their virtual identities. We can all work together to strengthen our defenses against this sneaky phenomenon and maintain the trustworthiness of our internet-based culture in the face of ever-changing technological challenges by emphasizing preventive measures like strict safety regulations and the implementation of state-of-the-art deepfake detection technologies.
References
- https://economictimes.indiatimes.com/magazines/panache/ratan-tata-slams-deepfake-video-that-features-him-giving-risk-free-investment-advice/articleshow/105805223.cms
- https://www.ndtv.com/india-news/ratan-tata-flags-deepfake-video-of-his-interview-recommending-investments-4640515
- https://www.businesstoday.in/bt-tv/short-video/viralvideo-business-tycoon-ratan-tata-falls-victim-to-deepfake-408557-2023-12-07
- https://www.livemint.com/news/india/false-ratan-tata-calls-out-a-deepfake-video-of-him-giving-investment-advice-11701926766285.html
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
