#FactCheck - Viral Postcard Attributing Fake UGC Statement to Keshav Prasad Maurya Is False
Executive Summary
A postcard claiming that Uttar Pradesh Deputy Chief Minister Keshav Prasad Maurya commented on the Supreme Court’s stay on the new UGC regulations is being widely shared on social media. The viral postcard suggests that Maurya stated the Modi government would “fight till its last breath” to implement the UGC law and appealed to Dalit, backward and tribal communities to trust the government as their true well-wisher. However, an research by the CyberPeace has found that the viral postcard is fake. Keshav Prasad Maurya has not made any such statement.
Claim
A Facebook user shared the postcard with the caption:“Now read it yourself. Statement of Deputy CM Keshav Prasad Maurya — the Modi government will fight till its last breath to implement the UGC law. An appeal to Dalit, backward and tribal communities to trust the government, calling it their true well-wisher.”
(Archived version of the post available here.)
Fact Check:
During the research, we did not find any credible news reports mentioning such a statement by Deputy Chief Minister Keshav Prasad Maurya regarding the UGC regulations or the Supreme Court’s order. A closer examination of the viral postcard revealed several inconsistencies. Notably, the text on the postcard lacks proper punctuation, such as commas and full stops, which is unusual for professionally designed news graphics. The postcard carries the logo of Navbharat Times (NBT). However, when compared with genuine NBT postcards, the font style used in the viral image does not match NBT’s official design. We also traced the original NBT postcard that appears to have been edited to create the fake one. In the authentic postcard, shared by NBT on January 20, Keshav Prasad Maurya is quoted as saying: Where the lotus has bloomed, it will continue to bloom, and where it has not, under the guidance of PM Modi and the leadership of Nitin Nabin, the lotus will bloom.”
The original statement was digitally altered, and a fabricated quote was inserted to create the viral postcard.
Conclusion
CyberPeace research clearly establishes that the viral postcard is fake. The original Navbharat Times postcard has been tampered with, and Keshav Prasad Maurya’s actual statement has been replaced with a fabricated quote, which is now being circulated with a misleading claim.
Related Blogs
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
Introduction
For years, the story of terror recruitment in Jammu & Kashmir followed a familiar arc: physical infiltration across the Line of Control, local Over Ground Workers (OGWs) acting as couriers, and recruitment pitches on mainstream apps like WhatsApp and Facebook Messenger. Indian security agencies built entire surveillance architectures around that arc. Now, officials say, the architecture is being outflanked in a way few anticipated: through pornography and dating platforms.
The New Front: Chat Rooms Nobody Is Watching
According to officials cited in recent reporting, Pakistan-based terror handlers working in coordination with Pakistan's ISI have begun exploiting the real-time chat features built into pornography and dating websites to reach recruits in Jammu & Kashmir. These pornography platforms feature real-time chat tools that operate under the guise of helping users find dates nearby, and handlers are exploiting that feature to broadcast messages and coordinate activities. It's a strikingly mundane pivot for an organisation engaged in violent extremism, but that is precisely the point that nobody expects a counter-terror dragnet to be watching a dating chatbox.
Officials say the tactic is designed to evade the surveillance that has become standard on conventional social media platforms, allowing handlers to convey instructions to recruits while staying off the radar of established monitoring tools. WhatsApp, Signal and Facebook Messenger have all, in various ways, become known quantities to Indian intelligence subject to legal intercepts, metadata analysis and years of institutional familiarity. A chat window buried inside an adult content site is not.
Tor, Encrypted Nodes, and Apps Built to Disappear
Other than porn sites, investigators have also flagged a cluster of niche, privacy-first messaging apps that route traffic through Tor-based, encrypted nodes to mask user identity. Security agencies have placed a wide array of specialised digital tools under scrutiny, with terror handlers relying on Tor-based messaging applications like Coatex and Conion to route data through encrypted nodes and obscure user identities. Access to at least one of these apps' installation files is reportedly already restricted within India, though enforcement against sideloaded Android packages remains an uphill battle.
What makes these platforms attractive to handlers isn't unique code so much as the design philosophy behind privacy-first messaging generally. Some of these apps offer only basic encryption, while others go further with end-to-end encryption, self-destructing messages, and strong on-device encryption algorithms that keep data processing off any third-party server. Several reportedly allow account creation without a phone number or SIM verification, stripping away one of the most basic identity anchors that Indian telecom-linked surveillance depends on.
There's also an operational, almost logistical, reason for the shift: connectivity. Officials note that some of these applications provide end-to-end encryption, self-destructing messages and registration without a phone number or email, making it difficult for security agencies to trace users, even as terror networks also shift away from commonly used platforms. In the hilly, forested and often poorly connected terrain of Jammu's border districts, apps engineered to function on weak 2G or EDGE networks have an obvious tactical advantage over data-hungry mainstream platforms.
VPNs, Banned Apps, and a Cat-and-Mouse Game
Virtual Private Networks add another layer of obfuscation, letting operatives access apps banned in India and mask the geographic origin of their traffic. This isn't new tradecraft, but its pairing with adult-content chat infrastructure and Tor-routed messaging represents a genuinely novel combination in the Kashmir context, according to the officials describing the pattern to reporters.
The broader trend line, officials say, is a steady migration away from platforms Indian agencies have learned to monitor. Terror networks are increasingly moving away from mainstream, commonly used platforms in favour of more obscure alternatives, forcing intelligence agencies into a perpetual game of catch-up: each time a monitoring capability matures against one platform, handlers migrate to the next.
This is not the first time investigators have flagged this cat-and-mouse dynamic. Reporting on a recent case in Jammu's Bathindi area described a 19-year-old allegedly radicalised through the encrypted app. Session the same platform reportedly linked to suspects in a Delhi bomb plot investigation after months of contact with Pakistan-based handlers. Investigators in that case noted that terror organisations have increasingly turned to multi-layered encrypted messaging services specifically to evade monitoring by intelligence agencies.
The Virtual SIM Problem
Alongside app-layer evasion, foreign-issued virtual SIM cards remain a persistent headache for investigators. The most cited example remains the 2019 Pulwama attack investigation, in which agencies reportedly traced more than 40 virtual SIM cards to the Jaish-e-Mohammed suicide bomber and his network numbers that could be provisioned and abandoned without ever touching an Indian telecom's KYC system. That case became something of a template for how virtual and foreign-registered numbers can be used to build communication chains that are extremely difficult to map after the fact, since there is no physical SIM, no retail purchase record, and often no domestic carrier data trail at all.
More recent J&K cases echo the same pattern in a different form. Police investigating a cross-border radicalisation network noted that intelligence agencies now suspect unauthorised SIM card distribution is being used by terrorists to communicate with handlers across the border, part of a broader push to choke off the logistical and communication backbone that keeps sleeper modules alive even when direct physical contact with a local handler is minimal or non-existent.
How Agencies Are Responding
To their credit, security agencies aren't standing still. Officials say cyber-surveillance frameworks are actively being redesigned to map and intercept these "off-grid" communication channels, a phrase that itself signals how far outside traditional monitoring territory this recruitment method has moved. Agencies say they continue to adapt their cyber-surveillance frameworks specifically to map and intercept these off-grid communication channels. That has included moving to restrict access to specific APKs, tightening scrutiny of virtual number providers, and, as seen in recent CIK (Counter Intelligence Kashmir) operations, proactively disrupting online propaganda networks before recruitment pitches can mature into operational plots. One recent CIK operation, for instance, intercepted attempts to recruit two teenage boys who were allegedly being fed terror content in the direction of a Pakistan-based handler, underlining how young the target pool for these campaigns has become.
Conclusion
What this episode really illustrates isn't a single clever trick but a structural truth about counter-terror surveillance: it is inherently reactive. Every time agencies build competence around a platform, handlers find a low-attention, high-friction-to-monitor alternative: first fringe messaging apps, then Tor-routed clients, and now the sprawling, largely unregulated back-end of adult content platforms, which few people would ever think to associate with national security. It's a reminder that the fight against radicalisation online is no longer confined to obviously "extremist" corners of the internet; it can hide in plain sight, inside the most ordinary-looking corners of the web.
Sources
- New J-K terror tactic: Pornography apps, Tor network used for secret messaging — The Tribune
- New J&K terror tactic: Handlers turn to porn sites, encrypted apps to contact recruits — Deccan Herald
- Terrorists using porn website, encrypted apps for chats with recruits — Organiser
- New J&K Terror Tactic: Pornography Apps, Tor Network Used For Secret Messaging — Kashmir Dot Com
- From WhatsApp to Porn Sites: Terror Groups Adopt New Digital Tactic in J&K — Jammu Kashmir Now
- Jammu teenager's arrest exposes cross-border radicalisation network — The Tribune
- After OGW network, J&K cops target communication channel of terrorists — The Tribune
- CIK busts online radical network, foils recruitment of two minors — The Tribune
Executive Summary:
A photo circulating on social media shows a stage with the words “Hindu Sammelan” (Hindu Conference) written in large letters. In front of the stage, rows of chairs appear largely empty, with only a few people seated while most seats remain vacant.
Users sharing the image claim that the event, held under the banner of a “Hindu Sammelan,” was in fact a “Brahmin Sammelan,” and that indigenous communities chose to stay away, resulting in poor attendance.
It is noteworthy that, on the occasion of the centenary year of the Rashtriya Swayamsevak Sangh (RSS), various “Hindu Sammelan” events are being organized across the country. The viral image is being linked to this broader context.
However, research conducted by the CyberPeace found the viral claim to be false. Our research revealed that the image being shared on social media is not authentic but AI-generated and is being circulated with a misleading narrative.
Claim
On February 21, 2026, a Facebook user shared the viral image. The original and archived links are provided below
- https://www.facebook.com/photo?fbid=935049042540479&set=gm.2425972001215469&idorvanity=465387370607285
- https://ghostarchive.org/archive/sxC6d
Fact Check:
A keyword search on Google confirmed that several “Hindu Sammelan” events have indeed been organized across the country as part of the RSS centenary year. For instance, media reports have covered such events in different cities, including Nagpur.
However, upon closely examining the viral image, we observed certain visual inconsistencies and unnatural elements that raised suspicion of AI generation. We first analyzed the image using the AI detection tool Hive Moderation, which indicated a 79.3 percent probability that the image was AI-generated.
To further verify, we scanned the image using another AI detection platform, Sightengine. The results showed a 97 percent likelihood that the image was AI-generated.
Conclusion
Our research confirms that the image circulating on social media is not genuine. It has been artificially created using AI technology and is being shared with a misleading claim.
