#FactCheck-Viral Video Claiming a Shooting Incident at Patna High School is Actually From a Film Set

The European Union (EU) has made trailblazing efforts regarding protection and privacy, coming up with the most comprehensive and detailed regulation called the GDPR (General Data Protection Regulation). As countries worldwide continue to grapple with setting their laws, the EU is already taking on issues with tech giants and focusing on the road ahead. Its contentious issues with Meta and the launch of Meta’s AI assistant in the EU are thus seen as a complex process, shaped by stringent data privacy regulations, ongoing debates over copyright, and ethical AI practices.​ This development is considered important as previously, the EU and Meta have had issues (including fines and and also received a pushback concerning its services), which broadly include data privacy regarding compliance with GDPR, antitrust law concerns- targeting ads, facebook marketplace activities and content moderation with respect to the spread of misinformation. 

Privacy and Data Protection Concerns

A significant part of operating Large Language Models (LLMs) is the need to train them with a repository of data/ plausible answers from which they can source. If it doesn’t find relevant information or the request is out of its scope, programmed to answer, it shall continue to follow orders, but with a reduction in the accuracy of its response. Meta's initial plans to train its AI models using publicly available content from adult users in the EU received a setback from privacy regulators. The Irish Data Protection Commission (DPC), acting as Meta's lead privacy regulator in Europe, raised the issue and requested a delay in the rollout to assess its compliance with GDPR. It has also raised similar concerns with Grok, the AI tool of X, to assess whether the EU users’  data was lawfully processed for training it. 

In response, Meta stalled the release of this feature for around a year and agreed to exclude private messages and data from users under the age of 18 and implemented an opt-out mechanism for users who do not wish their public data to be used for AI training. This approach aligns with GDPR requirements, which mandate a clear legal basis for processing personal data, such as obtaining explicit consent or demonstrating legitimate interest, along with the option of removal of consent at a later stage, as the user wishes. The version/service available at the moment is a text-based assistant which is not capable of things like image generation, but can provide services and assistance which include brainstorming, planning, and answering queries from web-based information. However, Meta has assured its users of expansion and exploration regarding the AI features in the near future as it continues to cooperate with the regulators.

Regulatory Environment and Strategic Decisions

The EU's regulatory landscape, characterised by the GDPR and the forthcoming AI Act, presents challenges for tech companies like Meta. Citing the "unpredictable nature" of EU regulations, Meta has decided not to release its multimodal Llama AI model—capable of processing text, images, audio, and video—in the EU. This decision underscores the tension between innovation and regulatory compliance, as companies navigate the complexities of deploying advanced AI technologies within strict legal frameworks. 

Implications and Future Outlook

Meta's experience highlights the broader challenges faced by AI developers operating in jurisdictions with robust data protection laws. The most critical issue that remains for now is to strike a balance  between  leveraging user data for AI advancement while respecting individual privacy rights.. As the EU continues to refine its regulatory approach to AI, companies need to adapt their strategies to ensure compliance while fostering innovation.​ Stringent measures and regular assessment also keep in check the accountability of big tech companies as they make for profit as well as for the public.

Reference:

• https://thehackernews.com/2025/04/meta-resumes-eu-ai-training-using.html
• https://www.thehindu.com/sci-tech/technology/meta-to-train-ai-models-on-european-users-public-data/article69451271.ece
• https://about.fb.com/news/2025/04/making-ai-work-harder-for-europeans/ 
• https://www.theregister.com/2025/04/15/meta_resume_ai_training_eu_user_posts/ 
• https://noyb.eu/en/twitters-ai-plans-hit-9-more-gdpr-complaints 

• https://www.businesstoday.in/technology/news/story/meta-ai-finally-comes-to-europe-after-a-year-long-delay-but-with-some-limitations-468809-2025-03-21
• https://www.bloomberg.com/news/articles/2025-02-13/meta-opens-facebook-marketplace-to-rivals-in-eu-antitrust-clash
• https://www.nytimes.com/2023/05/22/business/meta-facebook-eu-privacy-fine.html#:~:text=Many%20civil%20society%20groups%20and,million%20for%20a%20data%20leak.
• https://ec.europa.eu/commission/presscorner/detail/en/ip_24_5801
• https://www.thehindu.com/sci-tech/technology/european-union-accuses-facebook-owner-meta-of-breaking-digital-rules-with-paid-ad-free-option/article68358039.ece
• https://www.theregister.com/2025/04/14/ireland_investigation_into_x/ 
• https://www.theverge.com/2024/7/18/24201041/meta-multimodal-llama-ai-model-launch-eu-regulations?utm_source=chatgpt.com
• https://www.axios.com/2024/07/17/meta-future-multimodal-ai-models-eu?utm_source=chatgpt.com 

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Executive Summary 

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A video showing stone pelting on vehicles is being widely shared on social media and falsely linked to a campaign called the “Cockroach Janata Party.” The claim suggests that people have taken to the streets and are attacking VIP vehicles by throwing stones. CyberPeace Research Wing  research found that the video is from March and shows stone pelting on a police vehicle in Mathura following the killing of “Farasa Wale Baba.” 

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Claim

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An 18-second viral clip circulating online shows a crowd throwing stones at passing vehicles. The video also contains overlaid text reading,“Cockroach sarkon par utar chuke hain, ab koi VIP nahi, ab sab barabar hain.”Users are sharing it as part of a supposed “Cockroach Janata Party” campaign.

• https://www.facebook.com/reel/2472412446597619 

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Fact Check

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A reverse image search of key frames shows that the clip was originally uploaded on multiple social media accounts on March 22–23, where it was described as an incident from Mathura following the death of a local figure known as “Farasa Wale Baba.” In those posts, the incident was reported as stone pelting on a police vehicle by miscreants.

• https://public.app/video/sp_3xmzm15dp4050

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Further verification found the same visuals in a video posted by NDTV India on March 21, 2026, with a caption stating that it showed stone pelting on police during unrest in Mathura after the death of “Farasa Wale Baba.”

• https://x.com/ndtvindia/status/2035228921206382808 

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Conclusion

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The researchclearly confirms that the viral video has been misrepresented. The footage is from a March incident in Mathura involving unrest following a local death, and it has no connection to any “Cockroach Janata Party” campaign or VIP-targeted violence claims.

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Introduction

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In the hyperconnected world, cyber incidents can no longer be treated as sporadic disruptions; such incidents have become an everyday occurrence. The attack landscape today is very consequential and shows significant multiplication in its frequency, with ransomware attacks incapacitating a health system, phishing attacks hitting a financial institution, or state-sponsored attacks on critical infrastructures. Towards counteracting such threats, traditional ways alone are not enough, they gravely rely on manual research and human intellect. Attackers exercise speed, scale, and stealth, and defenders are always four steps behind. With such a widening gap, it is deemed necessary to facilitate incident response and crisis management with the intervention of automation and artificial intelligence (AI) for faster detection, context-driven decision-making, and collaborative response beyond human capabilities.

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Incident Response and Crisis Management

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Incident response is the structured way in which organisations deal with responding to detecting, segregating, and recovering from security incidents. Crisis management takes this even further, dealing not only with the technical fallout of a breach but also its business, reputation, and regulatory implications. Echelon used to depend on manual teams of people sorting through logs, cross-correlating alarms, and generating responses, a paradigm effective for small numbers but quickly inadequate in today's threat climate. Today's opponents attack at machine speed, employing automation to launch attacks. Under such circumstances, responding with slow, manual methods means delay and draconian consequences. The AI and automation introduction is a paradigm change that allows organisations to equate the pace and precision with which attackers initiate attacks in responding to incidents.

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How Automation Reinvents Response

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Cybercrime automation liberates cybercrime analysts from boring and repetitive tasks that consume time. An analyst manually detects potential threats from a list of hundreds each day, while automated systems sift through noise and focus only on genuine threats. Malware can automatically cause infected computers to be disconnected from the network to avoid spreading or may automatically have its suspicious account permissions removed without human intervention. The security orchestration systems move further by introducing playbooks, predefined steps describing how incidents of a certain type (e.g., phishing attempts or malware infections) should be handled. This ensures fast containment while ensuring consistency and minimising human error amid the urgency of dealing with thousands of alerts.

Automation takes care of threat detection, prioritisation, and containment, allowing human analysts to refocus on more complex decision-making. Instead of drowning in the sea of trivial alerts, security teams can now devote their efforts to more strategic areas: threat hunting and longer-term resilience. Automation is a strong tool of defence, cutting response times down from hours to minutes.

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The Intelligence Layer: AI in Action

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If automation provides speed, then AI is what allows the brain to be intelligent and flexible. Working with old and fixed-rule systems, AI-enabled solutions learn from experiences, adapt to changes in threats, and discover hidden patterns of which human analysts themselves would be unaware. For instance, machine learning algorithms identify normal behaviour on a corporate network and raise alerts on any anomalies that could indicate an insider attack or an advanced persistent threat. Similarly, AI systems sift through global threat intelligence to predict likely attack vectors so organisations can have their vulnerabilities fixed before they are exploited.

AI also boosts forensic analysis. Instead of searching forever for clues, analysts let AI-driven systems trace back to the origin of an event, identify vulnerabilities exploited by attackers, and flag systems that are still under attack. During a crisis, AI is a decision support that predicts outcomes of different scenarios and recommends the best response. In response to a ransomware attack, for example, based on context, AI might advise separating a single network segment or restoring from backup or alerting law enforcement. 

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Real-World Applications and Case Studies

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Already, this mitigation has been provided in the form of real-world applications of automation and AI. Consider, for example, IBM Watson for Cybersecurity, which has been applied in analysing unstructured threat intelligence and providing analysts with actionable results in minutes, rather than days. Like this, systems driven by AI in DARPA's Cyber Grand Challenge demonstrated the ability to automatically identify an instant vulnerability, patch it, and reveal the potential of a self-healing system. AI-powered fraud detection systems stop suspicious transactions in the middle of their execution and work all night to prevent losses. What is common in all these examples is that automation and AI lessen human effort, increase accuracy, and in the event of a cyberattack, buy precious time.

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Challenges and Limitations

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While promising, the technology is still not fully mature. The quality of an AI system is highly dependent on the training data provided; poor training can generate false positives that drown teams or worse false negatives that allow attackers to proceed unabated. Attackers have also started targeting AI itself by poisoning datasets or designing malware that does not get detected. Aside from risks that are more technical, the operational and financial costs involved in implementing advanced AI-based systems present expensive threats to any company. Organisations will have to make expenditures not only on technology but also for the training of staff to best utilise these tools. There are some ethical and privacy issues to consider as well because systems may be processing sensitive personal data, so global data protection laws such as the GDPR or India's DPDP Act could come into conflict.

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Creating a Human-AI Collaboration

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The future is not going to be one of substitution by machines but of creating human-AI synergy. Automation can do the drudgery, AI can provide smarts, and human professionals can use judgment, imagination, and ethical decisions. One would want to build AI-fuelled Security Operations Centres where technology and human experts work in tandem. Continuous training must be provided to AI models to reduce false alarms and make them most resistant against adversarial attacks. Regular conduct of crisis drills that combine AI tools and human teams can ensure preparedness for real-time events. Likewise, it is worth integrating ethical AI guidelines into security frameworks to ensure a stronger defence while respecting privacy and regulatory compliance.

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Conclusion

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Cyber-attacks are an eventuality in this modern time, but the actual impact need not be so harsh. The organisations can maintain the programmatic method of integrating automation and AI into incident response and crisis management so that the response against the very threat can be shifted from reactive firefighting to proactive resilience. Automation gives speed and efficiency while AI gives intelligence and foresight, hence putting the defenders on par and possibly exceeding the speed and sophistication of the attackers. But an utmost system without human inquisitiveness, ethical reasoning, and strategic foresight would remain imperfect. The best defence is in that human-machine relationship symbiotic system wherein automation and AI take care of how fast and how many cyber threats come in, whereas human intellect ensures that every response is aligned with larger organizational goals. This synergy is where cybersecurity resiliency will reside in the future-the defenders won't just be reacting to emergencies but will rather be driving the way.

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References

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• https://www.sisainfosec.com/blogs/incident-response-automation/
• https://stratpilot.ai/role-of-ai-in-crisis-management-and-its-critical-importance/
• https://www.juvare.com/integrating-artificial-intelligence-into-crisis-management/
• https://www.motadata.com/blog/role-of-automation-in-incident-management/

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