#FactCheck: AI Video made by Pakistan which says they launched a cross-border airstrike on India's Udhampur Airbase

Introduction

Empowering today’s youth with the right skills is more crucial than ever in a rapidly evolving digital world. Every year on July 15th, the United Nations marks World Youth Skills Day to emphasise the critical role of skills development in preparing young people for meaningful work and resilient futures. As AI transforms industries and societies, equipping young minds with digital and AI skills is key to fostering security, adaptability, and growth in the years ahead.

Why AI Upskilling is Crucial in Modern Cyber Defence

Security in the digital age remains a complex challenge, regardless of the presence of Artificial Intelligence (AI). It is one of the biggest modern ironies, and not only that, it is a paradox wrapped in code, where the cure and the curse are written in the same language. The very hand that protects the world from cyber threats can very well be used for the creation of that threat. This being said, the modern-day implementation of AI has to circumvent the threats posed by it or any other advanced technology. A solid grasp of AI and machine learning mechanisms is no longer optional; it is fundamental for modern cybersecurity. The traditional cybersecurity training programs employ static content, which can often become outdated and inadequate for the vulnerabilities. AI-powered solutions, such as intrusion detection systems and next-generation firewalls, use behavioural analysis instead of just matching signatures. AI models are susceptible, nevertheless, as malevolent actors can introduce hostile inputs or tainted data to trick computers into incorrect classification. Data poisoning is a major threat to AI defences, according to Cisco's evidence.

As threats surpass the current understanding of cybersecurity professionals, a need arises to upskill them in advanced AI technologies so that they can fortify the security of current systems. Two of the most important skills for professionals would be AI/ML Model Auditing and Data Science. Skilled data scientists can sift through vast logs, from pocket captures to user profiles, to detect anomalies, assess vulnerabilities, and anticipate attacks. A news report from Business Insider puts it correctly: ‘It takes a good-guy AI to fight a bad-guy AI.’ The technology of generative AI is quite new. As a result, it poses fresh security issues and faces security risks like data exfiltration and prompt injections. 

Another method that can prove effective is Natural Language Processing (NLP), which helps machines process this unstructured data, enabling automated spam detection, sentiment analysis, and threat context extraction. Security teams skilled in NLP can deploy systems that flag suspicious email patterns, detect malicious content in code reviews, and monitor internal networks for insider threats, all at speeds and scales humans cannot match. 

The AI skills, as aforementioned, are not only for courtesy’s sake; they have become essential in the current landscape. India is not far behind in this mission; it is committed, along with its western counterparts, to employ the emerging technologies in its larger goal of advancement. With quiet confidence, India takes pride in its remarkable capacity to nurture exceptional talent in science and technology, with Indian minds making significant contributions across global arenas.

AI Upskilling in India

As per a news report of March 2025, Jayant Chaudhary, Minister of State, Ministry of Skill Development & Entrepreneurship, highlighted that various schemes under the Skill India Programme (SIP) guarantee greater integration of emerging technologies, such as artificial intelligence (AI), cybersecurity, blockchain, and cloud computing, to meet industry demands. The SIP’s parliamentary brochure states that more than 6.15 million recipients have received training as of December 2024. Other schemes that facilitate educating and training professionals, such as Data Scientist, Business Intelligence Analyst, and Machine Learning Engineer are,

• Pradhan Mantri Kaushal Vikas Yojana 4.0 (PMKVY 4.0)
• Pradhan Mantri National Apprenticeship Promotion Scheme (PM-NAPS)
• Jan Shikshan Sansthan (JSS)

Another report showcases how Indian companies, or companies with their offices in India such as Ernst & Young (EY), are recognising the potential of the Indian workforce and yet their deficiencies in emerging technologies and leading the way by internal upskilling and establishing an AI Academy, a new program designed to assist businesses in providing their employees with essential AI capabilities, in response to the increasing need for AI expertise. Using more than 200 real-world AI use cases, the program offers interactive, organised learning opportunities that cover everything from basic ideas to sophisticated generative AI capabilities. 

In order to better understand the need for these initiatives, a reference is significant to a report backed by Google.org and the Asian Development Bank; India appears to be at a turning point in the global use of AI. As per the research, “AI for All: Building an AI-Ready Workforce in Asia-Pacific,” India urgently needs to provide accessible and efficient AI upskilling despite having the largest workforce in the world. According to the paper, by 2030, AI could boost the Asia-Pacific region’s GDP by up to USD 3 trillion. The key to this potential is India, a country with the youngest and fastest-growing population.

Conclusion and CyberPeace Resolution

As the world stands at the crossroads of innovation and insecurity, India finds itself uniquely poised, with its vast young population and growing technologies. But to truly safeguard its digital future and harness the promise of AI, the country must think beyond flagship schemes. Imagine classrooms where students learn not just to code but to question algorithms, workplaces where AI training is as routine as onboarding. 

India’s journey towards digital resilience is not just about mastering technology but about cultivating curiosity, responsibility, and trust. CyberPeace is committed to this future and is resolute in this collective pursuit of an ethically secure digital world. CyberPeace resolves to be an active catalyst in AI upskilling across India. We commit to launching specialised training modules on AI, cybersecurity, and digital ethics tailored for students and professionals. It seeks to close the AI literacy gap and develop a workforce that is both morally aware and technologically proficient by working with educational institutions, skilling initiatives, and industry stakeholders. 

References

1. https://www.helpnetsecurity.com/2025/03/07/ai-gamified-simulations-cybersecurity/ 
2. https://www.businessinsider.com/artificial-intelligence-cybersecurity-large-language-model-threats-solutions-2025-5?utm
3. https://apacnewsnetwork.com/2025/03/ai-5g-skills-boost-skill-india-targets-industry-demands-over-6-15-million-beneficiaries-trained-till-2024/ ‍
4. https://indianexpress.com/article/technology/artificial-intelligence/india-must-upskill-fast-to-keep-up-with-ai-jobs-says-new-report-10107821/

Introduction

Generative AI models are significant consumers of computational resources and energy required for training and running models. While AI is being hailed as a game-changer, however underneath the shiny exterior, cracks are present which significantly raises concerns for its environmental impact. The development, maintenance, and disposal of AI technology all come with a large carbon footprint. The energy consumption of AI models, particularly large-scale models or image generation systems, these models rely on data centers powered by electricity, often from non-renewable sources, which exacerbates environmental concerns and contributes to substantial carbon emissions.

As AI adoption grows, improving energy efficiency becomes essential. Optimising algorithms, reducing model complexity, and using more efficient hardware can lower the energy footprint of AI systems. Additionally, transitioning to renewable energy sources for data centers can help mitigate their environmental impact. There is a growing need for sustainable AI development, where environmental considerations are integral to model design and deployment.

A breakdown of how generative AI contributes to environmental risks and the pressing need for energy efficiency:

• Gen AI during the training phase has high power consumption, when vast amounts of computational power which is often utilising extensive GPU clusters for weeks or at times even months, consumes a substantial amount of electricity. Post this phase, the inference phase where the deployment of these models takes place for real-time inference, can be energy-extensive especially when we take into account the millions of users of Gen AI.
• The main source of energy used for training and deploying AI models often comes from non-renewable sources which then contribute to the carbon footprint. The data centers where the computations for Gen AI take place are a significant source of carbon emissions if they rely on the use of fossil fuels for their energy needs for the training and deployment of the models. According to a study by MIT, training an AI can produce emissions that are equivalent to around 300 round-trip flights between New York and San Francisco. According to a report by Goldman Sachs, Data Companies will use 8% of US power by 2030, compared to 3% in 2022 as their energy demand grows by 160%.
• The production and disposal of hardware (GPUs, servers) necessary for AI contribute to environmental degradation. Mining for raw materials and disposing of electronic waste (e-waste) are additional environmental concerns. E-waste contains hazardous chemicals, including lead, mercury, and cadmium, that can contaminate soil and water supplies and endanger both human health and the environment. 

Efforts by the Industry to reduce the environmental risk posed by Gen AI 

There are a few examples of how companies are making efforts to reduce their carbon footprint, reduce energy consumption and overall be more environmentally friendly in the long run. Some of the efforts are as under:

• Google's TPUs in particular the Google Tensor are designed specifically for machine learning tasks and offer a higher performance-per-watt ratio compared to traditional GPUs, leading to more efficient AI computations during the shorter periods requiring peak consumption. 
• Researchers at Microsoft, for instance, have developed a so-called “1 bit” architecture that can make LLMs 10 times more energy efficient than the current leading system. This system simplifies the models’ calculations by reducing the values to 0 or 1, slashing power consumption but without sacrificing its performance.
• OpenAI has been working on optimizing the efficiency of its models and exploring ways to reduce the environmental impact of AI and using renewable energy as much as possible including the research into more efficient training methods and model architectures.

Policy Recommendations

We advocate for the sustainable product development process and press the need for Energy Efficiency in AI Models to counter the environmental impact that they have. These improvements would not only be better for the environment but also contribute to the greater and sustainable development of Gen AI.  Some suggestions are as follows:

• AI needs to adopt a Climate justice framework which has been informed by a diverse context and perspectives while working in tandem with the UN’s (Sustainable Development Goals) SDGs.
• Working and developing more efficient algorithms that would require less computational power for both training and inference can reduce energy consumption. Designing more energy-efficient hardware, such as specialized AI accelerators and next-generation GPUs, can help mitigate the environmental impact. 
• Transitioning to renewable energy sources (solar, wind, hydro) can significantly reduce the carbon footprint associated with AI. The World Economic Forum (WEF) projects that by 2050, the total amount of e-waste generated will have surpassed 120 million metric tonnes.
• Employing techniques like model compression, which reduces the size of AI models without sacrificing performance, can lead to less energy-intensive computations. Optimized models are faster and require less hardware, thus consuming less energy.
• Implementing scattered learning approaches, where models are trained across decentralized devices rather than centralized data centers, can lead to a better distribution of energy load evenly and reduce the overall environmental impact.
• Enhancing the energy efficiency of data centers through better cooling systems, improved energy management practices, and the use of AI for optimizing data center operations can contribute to reduced energy consumption.

Final Words

The UN Sustainable Development Goals (SDGs) are crucial for the AI industry just as other industries as they guide responsible innovation. Aligning AI development with the SDGs will ensure ethical practices, promoting sustainability, equity, and inclusivity. This alignment fosters global trust in AI technologies, encourages investment, and drives solutions to pressing global challenges, such as poverty, education, and climate change, ultimately creating a positive impact on society and the environment. The current state of AI is that it is essentially utilizing enormous power and producing a product not efficiently utilizing the power it gets. AI and its derivatives are stressing the environment in such a manner which if it continues will affect the clean water resources and other non-renewable power generation sources which contributed to the huge carbon footprint of the AI industry as a whole.  

References

• https://cio.economictimes.indiatimes.com/news/artificial-intelligence/ais-hunger-for-power-can-be-tamed/111302991
• https://earth.org/the-green-dilemma-can-ai-fulfil-its-potential-without-harming-the-environment/ 
• https://www.technologyreview.com/2019/06/06/239031/training-a-single-ai-model-can-emit-as-much-carbon-as-five-cars-in-their-lifetimes/
• https://www.scientificamerican.com/article/ais-climate-impact-goes-beyond-its-emissions/ ‍
• https://insights.grcglobalgroup.com/the-environmental-impact-of-ai/

Executive Summary 

A video is being shared on social media with the claim that it shows a recent Houthi attack on King Khalid International Airport in Riyadh, the capital of Saudi Arabia. The video shows people running inside a building amid chaos, followed by a loud explosion that appears to shatter glass. The post claims that the visuals show the Riyadh airport during a Houthi missile attack. However, CyberPeace Research Wing’s research found that the video is actually from Russia.

Claim:

A social media post is being shared with the claim that the video shows a Houthi attack on Riyadh Airport in Saudi Arabia.

https://www.facebook.com/reel/1491107586179716 

https://ghostarchive.org/archive/O9X4A

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Fact Check

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To verify the truth behind the video being shared with the claim that it shows a Houthi attack on an airport in Riyadh, Saudi Arabia, we conducted a reverse image search of its keyframes. During the search, we found a video uploaded on June 19, 2026, by the Kazakh YouTube account @AzattyqAsia, which contains the same visuals seen in the viral video. According to the report, the video shows Russia’s Sadovod Market, where a massive fire broke out following a drone attack.

https://www.youtube.com/shorts/D2-m4l5Obc8

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Additionally, we found another video shot from a different angle on the Facebook account Yenisafak.com/ru, uploaded on June 18, 2026. The video contains the same visuals seen in the viral clip. The post states that the video was filmed at Moscow’s Sadovod Market, where a massive fire broke out at a complex following a drone attack.

https://www.facebook.com/reel/980820447900927

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Conclusion

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The evidence gathered during our research makes it clear that the viral video does not show a Houthi attack on an airport in Saudi Arabia, but is actually from Moscow, the capital of Russia.

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