#FactCheck-Mock drill video falsely linked to death of Indian sailor in Gulf of Oman
Executive Summary
Amid reports of attacks on ships in the Gulf of Oman that led to the death of three Indian nationals, a video showing a person lying injured on a ship is being widely circulated on social media. The clip is being shared with the claim that the United States sent the body of a deceased Indian sailor to India in a “wrapped” condition. CyberPeace Research Wing research found the claim to be misleading. The viral video is unrelated to any recent incident involving Indian nationals and is, in fact, from a mock drill conducted on board a ship.
Claim:
An Instagram user ‘fakirchand.sharma’ shared the video on June 15, 2026, claiming that an Indian sailor, identified as Nishant Urthnathan, died due to health complications on a vessel anchored at Duqm port, Oman. The post further alleged that the United States returned the body in a “wrapped” condition, sparking outrage on social media. https://www.instagram.com/fakirchand.sharma/reel/DZlsxj9TIaS , https://perma.cc/C9SF-J8ZR
Fact Check:
A reverse image search of keyframes from the viral video led to an Instagram account ‘bipul_raja_vlogs’, where the same video was posted on June 10, 2026. The original caption clearly stated that the visuals were from a fire drill conducted on board a ship. https://www.instagram.com/p/DZbnZlGPAlL
Further posts from the same account on June 15 and June 17, 2026, also show similar mock drill visuals and clarify that the content is being misused online. The account features multiple posts related to routine ship operations and training exercises. https://www.instagram.com/p/DZnEXOzCc2X
The profile also contains several other photos and videos of crew members working on the ship. We also checked the YouTube channel linked in this profile, “Sunnybabu,” where a mock drill video was uploaded on January 21, 2026. https://www.instagram.com/bipul_raja_vlogs
Conclusion:
The research confirms that the viral video is unrelated to the death of any Indian sailor in the Gulf of Oman. The clip actually shows a mock drill conducted on a ship and is being falsely shared with a misleading narrative.
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Introduction
Due to the rapid growth of high-capability AI systems around the world, growing concerns regarding safety, accountability, and governance have arisen throughout the world; thus, California has responded by passing the Transparency in Frontier Artificial Intelligence Act (TFAIA), the first state statute focused on "frontier" (highly capable) AI models. This statute is unique in that it does not only target harms caused by AI models in the form of consumer protection as compared to the majority of state statutes; rather, this statute addresses the catastrophic and systemic risks to society associated with large-scale AI systems. As California is a global technology leader, the TFAIA is positioned to have a significant impact on both domestic regulation and the evolution of international legal frameworks for AI technology (and as such has the potential to influence corporate compliance practices and the establishment of global norms related to the use of AI).
Understanding the Transparency in Frontier Artificial Intelligence Act
The Transparency in Frontier Artificial Intelligence Act provides a specific regulatory process for companies that create sophisticated AI systems with societal, economic, or national security implications. Covered developers are required to publish an extensive safety and transparency policy that details how they navigate risk throughout the artificial intelligence lifecycle. The act requires developers to notify the government of any significant incidents or failures with their deployed frontier models on a timely basis.
A significant aspect of the TFAIA is that it establishes the concept of "process transparency", which does not explicitly control how AI developers create their models, but rather holds them accountable for their internal safety governance by mandating that they develop Documented safety frameworks that outline risk assessment, mitigation, and monitoring processes. The act allows developers to protect their trade secrets, patents, and national defense concerns by providing them with limited opportunities for exemption and/or redaction of their documents so that they can maintain a balance between data openness and safeguarding sensitive information..
Extraterritorial Impact on Global AI Developers
While the Act is a state law, its implementation has far-reaching effects. Many of the largest AI companies have facilities, research labs or customers in California. Therefore, to be compliant with the TFAIA, these companies are required to do so commercially. The ability to develop a unified compliance model across regions enables companies to avoid developing duplicate compliance models.
This same pattern has occurred in other regulatory areas, like data protection regulations; where a region's regulations effectively became global compliance benchmarks for that regulatory area. The TFAIA could similarly serve as a global standard for transparency in frontier AI and shape how companies build their governance structure globally even if they don't have explicit regulations in the regions where they operate.
Influence on International AI Regulatory Models
The TFAIA offers a unique perspective on global discussions about regulating AI. In contrast to other legislation which defines different levels of risk depending on the type of AI, the TFAIA targets specifically high-impact or emerging technologies. Other nations may see value in this model of tiered regulations based on capability and apply it for their own regulation of AI, with the strictest obligations placed on those with the most critical potential harm.
The TFAIA may serve as a guide for international public policy makers by showing how they can reference existing standards and best practices in developing regulations, thus improving interoperability and potentially lessening regulatory barriers to cross-border AI innovations.
Corporate Governance, Compliance Costs, and Competition
From an industry perspective, the Act revolutionizes the way companies govern themselves. Developers are now required to create thorough risk assessments, red-teaming exercises, incident response protocols, and have board oversight for AI safety and regulation. The number of people involved in this process increases accountability but at the same time the increases will create a burden of cost for all involved.
The burden of compliance will be easier for large tech companies than for smaller or start-ups, and thus large tech companies may solidify their position of dominance over the development of frontier AI. Smaller and newer developers may be blocked from entering the market unless some form of proportional or scaled compliance mechanism for where they operate emerges. These developments certainly raise issues surrounding innovation policy and competition law at a global scale that will need to be addressed by regulators in conjunction with AI safety concerns.
Transparency, Public Trust, and Accountability
The TFAIA bolsters the capability of citizens, researchers and journalists to oversee the development and the use of artificial intelligence (AI) through its requirement for public disclosure of the safety framework of AI systems. The disclosures will allow citizens, researchers and journalists to critically evaluate corporate claims of responsible AI development. Over time, this evaluation could increase trust in publically regulated AI systems and would expose businesses that exhibit a poor risk management process.
However, how useful this transparency is depends on the quality and comparability of the information being disclosed. Many current disclosures are either too vague or too complex, thus limiting the ability to conduct meaningful oversight. There should be a push for clearer guidance and/or the establishment of standardised disclosure forms for the purposes of public accountability (i.e., citizens) and uniformity between countries.
Conclusion
The Transparency in Frontier Artificial Intelligence Act is a transformative development in the regulation of Artificial Intelligence Technology, specifically, a whole new risk profile of this new generation of AI / (Advanced High-Powered) Technologies such as Autonomous Vehicles. This new California law will create global impact because it Be will change how technology companies operate, create regulatory frameworks and develop standards to govern/oversee the use of Autonomous Vehicles. The Act creates a “transparent” means for regulating (or governing) Autonomous Vehicles as opposed to relying solely on “technical” means for these systems. As other regions experience similar challenges that US Government is facing with respect to this new generation of AI (written laws), California's approach will likely be used as an example for how AI laws are written in the future and develop a more unified and responsible international AI regulatory framework.
References
- https://www.whitecase.com/insight-alert/california-enacts-landmark-ai-transparency-law-transparency-frontier-artificial
- https://www.gov.ca.gov/2025/09/29/governor-newsom-signs-sb-53-advancing-californias-world-leading-artificial-intelligence-industry/
- https://www.mofo.com/resources/insights/251001-california-enacts-ai-safety-transparency-regulation-tfaia-sb-53
- https://www.dlapiper.com/en/insights/publications/2025/10/california-law-mandates-increased-developer-transparency-for-large-ai-models
Executive Summary
A video widely circulated on social media claims to show a confrontation between a Zee News journalist and Chief of Army Staff (COAS) General Upendra Dwivedi during the Indian Army’s Annual Press Briefing 2026. The video alleges that General Dwivedi made sensitive remarks regarding ‘Operation Sindoor’, including claims that the operation was still ongoing and that diplomatic intervention by former US President Donald Trump had restricted India’s military response. Several social media users shared the clip while questioning the Indian Army’s operational decisions and demanding accountability over the alleged remarks. The CyberPeace concludes that the viral video claiming to show a discussion between a Zee News journalist and Chief of Army Staff General Upendra Dwivedi on ‘Operation Sindoor’ is misleading and digitally manipulated. Although the visuals were sourced from the Indian Army’s Annual Press Briefing 2026, the audio was artificially created and added later to misinform viewers. The Army Chief did not make any remarks regarding diplomatic interference or limitations on military action during the briefing.
Claim:
An X (formerly Twitter) user, Abbas Chandio (@AbbasChandio__), shared the video on January 14, asserting that it showed a Zee News journalist questioning the Army Chief about the status and outcomes of ‘Operation Sindoor’ during a recent press conference. In the clip, the journalist is purportedly heard challenging the Army Chief over his earlier statement that the operation was “still ongoing,” while the COAS is allegedly heard responding that diplomatic intervention during the conflict limited the Army’s ability to pursue further military action. Here is the link and archive link to the post, along with a screenshot.
The reverse image search also directed to an extended version of the footage uploaded on the official YouTube channel of India Today. The original video was identified as coverage from the Indian Army’s Annual Press Conference 2026, held on January 13 in New Delhi and addressed by COAS General Upendra Dwivedi. Upon reviewing the original press briefing footage, CyberPeace found no instance where a Zee News journalist questioned the Army Chief about ‘Operation Sindoor’. There was also no mention of the statements attributed to General Dwivedi in the viral clip.
In the authentic footage, journalist Anuvesh Rath was seen raising questions related to defence procurement and modernization, not military operations or diplomatic interventions. Here is the link to the original video, along with a screenshot.
To further verify the claim, CyberPeace extracted the audio track from the viral video and analysed it using the AI-based voice detection tool Aurigin. The analysis revealed that the voice heard in the clip was artificially generated, indicating the use of synthetic or manipulated audio. This confirmed that while genuine visuals from the Army’s official press briefing were used, a fabricated audio track had been overlaid to falsely attribute controversial statements to the Army Chief and a Zee News journalist.
Conclusion
The CyberPeace concludes that the viral video claiming to show a discussion between a Zee News journalist and Chief of Army Staff General Upendra Dwivedi on ‘Operation Sindoor’ is misleading and digitally manipulated. Although the visuals were sourced from the Indian Army’s Annual Press Briefing 2026, the audio was artificially created and added later to misinform viewers. The Army Chief did not make any remarks regarding diplomatic interference or limitations on military action during the briefing. The video is a clear case of digital manipulation and misinformation, aimed at creating confusion and casting doubts over the Indian Army’s official position.
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
