#FactCheck - Viral Image of AIMIM President Asaduddin Owaisi Holding Lord Rama Portrait Proven Fake
Executive Summary:
In recent times an image showing the President of AIMIM, Asaduddin Owaisi holding a portrait of Hindu deity Lord Rama, has gone viral on different social media platforms. After conducting a reverse image search, CyberPeace Research Team then found that the picture was fake. The screenshot of the Facebook post made by Asaduddin Owaisi in 2018 reveals him holding Ambedkar’s picture. But the photo which has been morphed shows Asaduddin Owaisi holding a picture of Lord Rama with a distorted message gives totally different connotations in the political realm because in the 2024 Lok Sabha elections, Asaduddin Owaisi is a candidate from Hyderabad. This means there is a need to ensure that before sharing any information one must check it is original in order to eliminate fake news.
Claims:
AIMIM Party leader Asaduddin Owaisi standing with the painting of Hindu god Rama and the caption that reads his interest towards Hindu religion.
Fact Check:
In order to investigate the posts, we ran a reverse search of the image. We identified a photo that was shared on the official Facebook wall of the AIMIM President Asaduddin Owaisi on 7th April 2018.
Comparing the two photos we found that the painting Asaduddin Owaisi is holding is of B.R Ambedkar whereas the viral image is of Lord Rama, and the original photo was posted in the year 2018.
Hence, it was concluded that the viral image was digitally modified to spread false propaganda.
Conclusion:
The photograph of AIMIM President Asaduddin Owaisi holding up one painting of Lord Rama is fake as it has been morphed. The photo that Asaduddin Owaisi uploaded on a Facebook page on 7 Apr 2018 depicted him holding a picture of Bhimrao Ramji Ambedkar. This photograph was digitally altered and the false captions were written to give an altogether different message of Asaduddin Owaisi. It has even highlighted the necessity of fighting fake news that has spread widely through social media platforms especially during the political realm.
- Claim: AIMIM President Asaduddin Owaisi was holding a painting of the Hindu god Lord Rama in his hand.
- Claimed on: X (Formerly known as Twitter)
- Fact Check: Fake & Misleading
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Executive Summary
An image is being widely shared on social media with the claim that Iranian missile forces targeted an LPG tanker heading towards India, causing a massive fire onboard. CyberPeace Research Wing ’s research found the claim to be misleading. The research revealed that the viral image is not related to any recent Iran-India development or any missile attack. The image actually shows a fire incident involving the Cameroon-flagged LPG tanker MV Falcon near the coast of Aden, Yemen, in October 2025, and is being circulated with a false context.
Claim
A Facebook user shared the viral image on July 8, 2026, claiming: “Iranian missile force has struck an LPG tanker going to India.” The post link, archive link, and screenshot are provided below.
Fact Check
To verify the viral claim, we conducted a keyword-based search on Google. However, we did not find any credible media report confirming that an India-bound LPG tanker was targeted by Iranian missile forces. During the research, we extracted keyframes from the viral image and conducted a reverse image search using Google Lens. The search led us to a report published by NDTV on October 20, 2025, which contained the same visuals as the viral image. The report link and screenshot are provided below.
According to the NDTV report, a fire broke out onboard the Cameroon-flagged LPG tanker MV Falcon following an explosion near the coast of Aden, Yemen. The incident involved 23 Indian crew members, who were rescued safely. The incident occurred on October 18, 2025, at around 07:00 UTC, when the vessel was sailing approximately 113 nautical miles southeast of Aden while heading towards Djibouti. Following the explosion, the vessel went adrift and around 15% of the ship was engulfed in flames. Further verification through keyword searches led us to an India Today report published on October 20, 2025, which also confirmed that the MV Falcon caught fire after an explosion off the coast of Aden, Yemen. The report stated that all 23 Indian crew members onboard were rescued. The report also mentioned that authorities had initiated an research into the incident and ruled out speculation that the explosion was caused by a Houthi attack. The report link and screenshot are provided below.
Conclusion
Our research found that the viral claim is misleading. The image being shared as proof of an Iranian missile strike on an India-bound LPG tanker is actually from an unrelated incident that occurred in October 2025. The image shows the fire that broke out onboard the Cameroon-flagged LPG tanker MV Falcon near Aden, Yemen, following an explosion. The old image is being circulated with a false narrative linking it to Iran and India.
Introduction
A policy, no matter how artfully conceived, is like a timeless idiom, its truth self-evident, its purpose undeniable, standing in silent witness before those it vows to protect, yet trapped in the stillness of inaction, where every moment of delay erodes the very justice it was meant to serve. This is the case of the Digital Personal Data Protection Act, 2023, which holds in its promise a resolution to all the issues related to data protection and a protection framework at par with GDPR and Global Best Practices. While debates on its substantive efficacy are inevitable, its execution has emerged as a site of acute contention. The roll-out and the decision-making have been making headlines since late July on various fronts. The government is being questioned by industry stakeholders, media and independent analysts on certain grounds, be it “slow policy execution”, “centralisation of power” or “arbitrary amendments”. The act is now entrenched in a never-ending dilemma of competing interests under the DPDP Act.
The change to the Right to Information Act (RTI), 2005, made possible by Section 44(3) of the DPDP Act, has become a focal point of debate. This amendment is viewed by some as an attack on weakening the hard-won transparency architecture of Indian democracy by substituting an absolute exemption for personal information for the “public interest override” in Section 8(1)(j) of the RTI Act.
The Lag Ledger: Tracking the Delays in DPDP Enforcement
As per a news report of July 28, 2025, the Parliamentary Standing Committee on Information and Communications Technology has expressed its concern over the delayed implementation and has urged the Ministry of Electronics and Information Technology (MeitY) to ensure that data privacy is adequately ensured in the nation. In the report submitted to the Lok Sabha on July 24, the committee reviewed the government’s reaction to the previous recommendations and concluded that MeitY had only been able to hold nine consultations and twenty awareness workshops about the Draft DPDP Rules, 2025. In addition, four brainstorming sessions with academic specialists were conducted to examine the needs for research and development. The ministry acknowledges that this is a specialised field that urgently needs industrial involvement. Another news report dated 30th July, 2025, of a day-long consultation held where representatives from civil society groups, campaigns, social movements, senior lawyers, retired judges, journalists, and lawmakers participated on the contentious and chilling effects of the Draft Rules that were notified in January this year. The organisers said in a press statement the DPDP Act may have a negative impact on the freedom of the press and people’s right to information and the activists, journalists, attorneys, political parties, groups and organisations “who collect, analyse, and disseminate critical information as they become ‘data fiduciaries’ under the law.”
The DPDP Act has thus been caught up in an uncomfortable paradox: praised as a significant legislative achievement for India’s digital future, but caught in a transitional phase between enactment and enforcement, where every day not only postpones protection but also feeds worries about the dwindling amount of room for accountability and transparency.
The Muzzling Effect: Diluting Whistleblower Protections
The DPDP framework raises a number of subtle but significant issues, one of which is the possibility that it would weaken safeguards for whistleblowers. Critics argue that the Act runs the risk of trapping journalists, activists, and public interest actors who handle sensitive material while exposing wrongdoing because it expands the definition of “personal data” and places strict compliance requirements on “data fiduciaries.”One of the most important checks on state overreach may be silenced if those who speak truth to power are subject to legal retaliation in the absence of clear exclusions of robust public-interest protections.
Noted lawyer Prashant Bhushan has criticised the law for failing to protect whistleblowers, warning that “If someone exposes corruption and names officials, they could now be prosecuted for violating the DPDP Act.”
Consent Management under the DPDP Act
In June 2025, the National e-Governance Division (NeGD) under MeitY released a Business Requirement Document (BRD) for developing consent management systems under the DPDP Act, 2023. The document supports the idea of “Consent Manager”, which acts as a single point of contact between Data Principals and Data Fiduciaries. This idea is fundamental to the Act, which is now being operationalised with the help of MeitY’s “Code for Consent: The DPDP Innovation Challenge.” The government has established a collaborative ecosystem to construct consent management systems (CMS) that can serve as a single, standardised interface between Data Principals and Data Fiduciaries by choosing six distinct entities, such as Jio Platforms, IDfy, and Zoop. Such a framework could enable people to have meaningful control over their personal data, lessen consent fatigue, and move India’s consent architecture closer to international standards if it is implemented precisely and transparently.
There is no debate to the importance of this development however, there are various concerns associated with this advancement that must be considered. Although effective, a centralised consent management system may end up being a single point of failure in terms of political overreach and technical cybersecurity flaws. Concerns are raised over the concentration of power over the framing, seeking, and recording of consent when big corporate entities like Jio are chosen as key innovators. Critics contend that the organisations responsible for generating revenue from user data should not be given the responsibility for designing the gatekeeping systems. Furthermore, the CMS can create opaque channels for data access, compromising user autonomy and whistleblower protections, in the absence of strong safeguards, transparency mechanisms and independent oversight.
Conclusion
Despite being hailed as a turning point in India’s digital governance, the DPDP Act is still stuck in a delayed and unequal transition from promise to reality. Its goals are indisputable, but so are the conundrum it poses to accountability, openness, and civil liberties. Every delay increases public mistrust, and every safeguard that remains unsolved. The true test of a policy intended to safeguard the digital rights of millions lies not in how it was drafted, but in the integrity, pace, and transparency with which it is to be implemented. In the digital age, the true cost of delay is measured not in time, but in trust. CyberPeace calls for transparent, inclusive, and timely execution that balances innovation with the protection of digital rights.
References
- https://www.storyboard18.com/how-it-works/parliamentary-committee-raises-concern-with-meity-over-dpdp-act-implementation-lag-77105.htm
- https://thewire.in/law/excessive-centralisation-of-power-lawyers-activists-journalists-mps-express-fear-on-dpdp-act
- https://www.medianama.com/2025/08/223-jio-idfy-meity-consent-management-systems-dpdpa/
- https://www.downtoearth.org.in/governance/centre-refuses-to-amend-dpdp-act-to-protect-journalists-whistleblowers-and-rti-activists
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
