#FactCheck- Old Kerala church raid video falsely shared with exaggerated ₹7000 crore cash seizure claim

Introduction

According to Statista, the global artificial intelligence software market is forecast to grow by around 126 billion US dollars by 2025. This will include a 270% increase in enterprise adoption over the past four years. The top three verticals in the Al market are BFSI (Banking, Financial Services, and Insurance), Healthcare & Life Sciences, and Retail & e-commerce. These sectors benefit from vast data generation and the critical need for advanced analytics. Al is used for fraud detection, customer service, and risk management in BFSI; diagnostics and personalised treatment plans in healthcare; and retail marketing and inventory management.

The Chairperson of the Competition Commission of India’s Chief, Smt. Ravneet Kaur raised a concern that Artificial Intelligence has the potential to aid cartelisation by automating collusive behaviour through predictive algorithms. She explained that the mere use of algorithms cannot be anti-competitive but in case the algorithms are manipulated, then that is a valid concern about competition in markets.

This blog focuses on how policymakers can balance fostering innovation and ensuring fair competition in an AI-driven economy. 

What is the Risk Created by AI-driven Collusion?

AI uses predictive algorithms, and therefore, they could lead to aiding cartelisation by automating collusive behaviour. AI-driven collusion could be through:

• The use of predictive analytics to coordinate pricing strategies among competitors.
• The lack of human oversight in algorithm-induced decision-making leads to tacit collusion (competitors coordinate their actions without explicitly communicating or agreeing to do so).

AI has been raising antitrust concerns and the most recent example is the partnership between Microsoft and OpenAI, which has raised concerns among other national competition authorities regarding potential competition law issues. While it is expected that the partnership will potentially accelerate innovation, it also raises concerns about potential anticompetitive effects such as market foreclosure or the creation of barriers to entry for competitors and, therefore, has been under consideration in the German and UK courts. The problem here is in detecting and proving whether collusion is taking place. 

The Role of Policy and Regulation

The uncertainties induced by AI regarding its effects on competition create the need for algorithmic transparency and accountability in mitigating the risks of AI-driven collusion. It leads to the need to build and create regulatory frameworks that mandate the disclosure of algorithmic methodologies and establish a set of clear guidelines for the development of AI and its deployment. These frameworks or guidelines should encourage an environment of collaboration between competition watchdogs and AI experts. 

The global best practices and emerging trends in AI regulation already include respect for human rights, sustainability, transparency and strong risk management. The EU AI Act could serve as a model for other jurisdictions, as it outlines measures to ensure accountability and mitigate risks. The key goal is to tailor AI regulations to address perceived risks while incorporating core values such as privacy, non-discrimination, transparency, and security.

Promoting Innovation Without Stifling Competition

Policymakers need to ensure that they balance regulatory measures with innovation scope and that the two priorities do not hinder each other. 

1. Create adaptive and forward-thinking regulatory approaches to keep pace with technological advancements that take place at the pace of development and allow for quick adjustments in response to new AI capabilities and market behaviours.n
2. Competition watchdogs need to recruit domain experts to assess competition amid rapid changes in the technology landscape. Create a multi-stakeholder approach that involves regulators, industry leaders, technologists and academia who can create inclusive and ethical AI policies. 
3. Businesses can be provided incentives such as recognition through certifications, grants or benefits in acknowledgement of adopting ethical AI practices. 
4. Launch studies such as the CCI’s market study to study the impact of AI on competition. This can lead to the creation of a driving force for sustainable growth with technological advancements. 

Conclusion: AI and the Future of Competition

We must promote a multi-stakeholder approach that enhances regulatory oversight, and incentivising ethical AI practices. This is needed to strike a delicate balance that safeguards competition and drives sustainable growth. As AI continues to redefine industries, embracing collaborative, inclusive, and forward-thinking policies will be critical to building an equitable and innovative digital future. 

The lawmakers and policymakers engaged in the drafting of the frameworks need to ensure that they are adaptive to change and foster innovation. It is necessary to note that fair competition and innovation are not mutually exclusive goals, they are complementary to each other. Therefore, a regulatory framework that promotes transparency, accountability, and fairness in AI deployment must be established.

References

• https://www.thehindu.com/sci-tech/technology/ai-has-potential-to-aid-cartelisation-fair-competition-integral-for-sustainable-growth-cci-chief/article69041922.ece 
• https://www.marketsandmarkets.com/Market-Reports/artificial-intelligence-market-74851580.html 
• https://www.ey.com/en_in/insights/ai/how-to-navigate-global-trends-in-artificial-intelligence-regulation#:~:text=Six%20regulatory%20trends%20in%20Artificial%20Intelligence&text=These%20include%20respect%20for%20human,based%20approach%20to%20AI%20regulation. ‍
• https://www.business-standard.com/industry/news/ai-has-potential-to-aid-fair-competition-for-sustainable-growth-cci-chief-124122900221_1.html

Introduction

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In real-time warfare scenarios of this modern age, where actions occur without delay, the relevance of edge computing emerges as paramount. By processing data close to the source in the battlefield with the help of a drone or through video imaging from any military vehicle or aircraft, the concept of edge computing allows the military to point targets faster and strike with accuracy. It also enables local processing to relay central data, helping ground troops get intelligence inputs to act rapidly in critical mission scenarios.  

As the global security landscape experiences a significant transformation in different corners of the world, it presents unprecedented challenges in the present scenario. In this article, we will try to understand how countries can maintain their military capabilities with the help of advanced technologies like edge computing. 

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Edge Computing in Modern Warfare

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Edge computing involves the processing and storage of data at the point of collection on the battlefield, for example, through vehicles and drones, instead of relying on centralized data centers. This enables faster decision-making in real-time. This approach creates a resilient and secure network by reducing reliance on potentially compromised external connections, supporting autonomous systems, precision-based targeting, and data sharing among military personnel, drones, and command centers amidst a challenging environment.

A report released by the US Department of Defence in March 2025 found a crucial reality surrounding the operation of hardware relying on outdated industrial-age processes in the digital era. In the case of applications with video, edge computing helps to deliver significant advantages to a wide range of crucial military operations, which include: 

• Situational awareness with real-time data processing that provides improved battlefield visibility and proper threat detection. 
• Autonomous warfare systems such as drones, which use a tactical edge cloud computing to get the capability to navigate faster. 
• Developing a strong communication and networking capability to secure low-latency communication for troops to stay connected in challenging environments. 
• Ensuring predictive maintenance with the help of effective sensors to carry out edge detection and attrition at an early point, thereby reducing equipment failures. 
• Developing effective targeting and weapons systems to ensure faster processing to enable precision-based targeting and response, besides a strong logistics and supply chain that can provide real-time tracking to improve delivery accuracy and resource management. 

This report also highlighted that the DoD is rapidly updating its software and investing in AI enablers like data sets or MLOps tools. This also stresses the breaking down of integration barriers by enforcing MOSA (Modular Open Systems Approaches), APIs (Application Programming Interface), and modular interfaces to ensure interoperability across platforms, sensors, and networks to make software-defined warfare an effective strategy.  

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Developing Edge with Artificial Intelligence for Future Warfare

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A significant insight from the work of the US Department of Defense is its emphasis on the importance of edge computing in shaping the future of warfare. In that context, the Annual Threat Assessment Report highlights a key limitation of traditional AI strategies that rely on centralised cloud computing, since these might not be suitable for modern battlefields with congested networks and limited bandwidth. The need for real-time data processing requires a distributed and edge-based AI solution to address contemporary threats. This report also directly supports the deployment of effective edge with AI in a defined, disrupted, intermittent, and limited-bandwidth (DDIL) environment. In that case, when the communication networks fail, the edge servers at the edge of the network offer crucial advantages that cloud-dependent systems cannot. This ability to analyse data and make decisions without consistent connectivity and operate with limited computational resources is a strategic necessity. 

The scenario of warfare is a phenomenon that requires maintaining a strong strategic and tactical approach, which, in the present times, is being examined through the domain of digital platforms. Modern warfare patterns demand faster decision-making and edge computing deliveries by shifting the power of distant servers to the frontlines. The US military is already moving in the direction of deploying edge-enabled systems to prove the nature of sensors and networks to compute at the tactical edge to transform warfighting.

However, it can be understood with the help of an example, as creating fusion in the skies with F-35s. As they have showcased the capability of edge computing by fusing sensor data with MADL (Multi-Functional Advanced Data Link) to create a unified picture, making the squadrons a force multiplier. An example of this was visible when an F-35 relayed real-time tracking data, enabling a navy ship to neutralise a missile beyond its range.

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Conclusion: The Way Ahead 

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As the changing nature of warfare moves towards adopting software-defined systems, where edge computing thrives as a futuristic military technology, it calls for the need for integration across all domains of warfighting. But at the same time, several imperatives do emerge, such as: 

• Developing an open architecture that enables both flexibility and innovation. 
• Ensuring an effective connectivity that actually combines a confluence of legacy systems. 
• Developing interoperability among the systems that can function in synergy with all platforms and can function across all domains. 
• Prioritising edge-native AI development systems, where it is also necessary to ensure the shift to adopting cloud-based AI models to create solutions optimised from the ground up for edge deployment. 
• Investing in edge infrastructure to establish a robust edge computing infrastructure that enables rapid deployment by testing and updating AI capabilities across diverse hardware platforms. Like the way the military training academies in India are developing training infrastructures for training officer cadets or personnel to handle drones and all forms of advanced warfare tactics emerging in this age. 
• Fostering talent and expertise by embracing commercial solutions where software talent could be enabled across the enterprises with expertise in edge computing capabilities and AI. In this case, the role of the commercial sector can help to drive innovations in edge AI, and the only way to move in this direction is by leveraging these advances through partnerships and collaborative efforts. 

Taking the example of the ARPANET, which once seeded the modern internet, edge computing can also help to create a transformative network effect within the digital battlespace. In conclusion, future conflicts will be defined by the speed and accuracy provided by the edge, as nations integrating AI and robust edge infrastructures can hold a strong advantage in the multi-domain battlefields in the future.

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References 

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• https://www.idsa.in/mpidsanews/rk-narangs-article-what-the-regions-first-drone-warfare-taught-us-published-in-the-new-indian-express
• https://latentai.com/blog/software-defined-warfare-why-edge-ai-is-critical-to-americas-defense-future/
• https://www.boozallen.com/s/insight/blog/how-the-us-military-is-using-edge-computing.html
• https://capsindia.org/wp-content/uploads/2022/08/RK-Narang-3.pdf
• https://www.newindianexpress.com/opinions/2025/May/12/what-the-regions-first-drone-warfare-taught-us
• https://www.maris-tech.com/blog/edge-computing-in-the-military-challenges-and-solutions/#:~:text=In%20modern%20warfare%2C%20decisions%20need,enables%20precision%20targeting%20and%20response ‍
• https://cassindia.com/digital-soldiers/

Introduction

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In today’s hyper-connected world, information spreads faster than ever before. But while much attention is focused on public platforms like Facebook and Twitter, a different challenge lurks in the shadows: misinformation circulating on encrypted and closed-network platforms such as WhatsApp and Telegram. Unlike open platforms where harmful content can be flagged in public, private groups operate behind a digital curtain. Here, falsehoods often spread unchecked, gaining legitimacy because they are shared by trusted contacts. This makes encrypted platforms a double-edged sword. It is essential for privacy and free expression, yet uniquely vulnerable to misuse.

As Prime Minister Narendra Modi rightly reminded,

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‍“Think 10 times before forwarding anything,” warning that even a “single fake news has the capability to snowball into a matter of national concern.”

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The Moderation Challenge with End-to-End Encryption

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Encrypted messaging platforms were built to protect personal communication. Yet, the same end-to-end encryption that shields users’ privacy also creates a blind spot for moderation. Authorities, researchers, and even the platforms themselves cannot view content circulating in private groups, making fact-checking nearly impossible.

Trust within closed groups makes the problem worse. When a message comes from family, friends, or community leaders, people tend to believe it without questioning and quickly pass it along. Features like large group chats, broadcast lists, and “forward to many” options further speed up its spread. Unlike open networks, there is no public scrutiny, no visible counter-narrative, and no opportunity for timely correction.

During the COVID-19 pandemic, false claims about vaccines spread widely through WhatsApp groups, undermining public health campaigns. Even more alarming, WhatsApp rumors about child kidnappers and cow meat in India triggered mob lynchings, leading to the tragic loss of life.

Encrypted platforms, therefore, represent a unique challenge: they are designed to protect privacy, but, unintentionally, they also protect the spread of dangerous misinformation.

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Approaches to Curbing Misinformation on End-to-End Platforms

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• Regulatory: Governments worldwide are exploring ways to access encrypted data on messaging platforms, creating tensions between the right to user privacy and crime prevention. Approaches like traceability requirements on WhatsApp, data-sharing mandates for platforms in serious cases, and stronger obligations to act against harmful viral content are also being considered.
• Technological Interventions: Platforms like WhatsApp have introduced features such as “forwarded many times” labels and limits on mass forwarding. These tools can be expanded further by introducing AI-driven link-checking and warnings for suspicious content.

• Community-Based Interventions: Ultimately, no regulation or technology can succeed without public awareness. People need to be inoculated against misinformation through pre-bunking efforts and digital literacy campaigns. Fact-checking websites and tools also have to be taught.

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Best Practices for Netizens

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Experts recommend simple yet powerful habits that every user can adopt to protect themselves and others. By adopting these, ordinary users can become the first line of defence against misinformation in their own communities:

• Cross-Check Before Forwarding: Verify claims from trusted platforms & official sources.
• Beware of Sensational Content: Headlines that sound too shocking or dramatic probably need checking. Consult multiple sources for a piece of news. If only one platform/ channel is carrying sensational news, it is likely to be clickbait or outright false.
• Stick to Trusted News Sources: Verify news through national newspapers and expert commentary. Remember, not everything on the internet/television is true.
• Look Out for Manipulated Media: Now, with AI-generated deepfakes, it becomes more difficult to tell the difference between original and manipulated media. Check for edited images, cropped videos, or voice messages without source information. Always cross-verify any media received.
• Report Harmful Content: Report misinformation to the platform it is being circulated on and PIB’s Fact Check Unit.

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Conclusion

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In closed, unmonitored groups, platforms like WhatsApp and Telegram often become safe havens where people trust and forward messages from friends and family without question. Once misinformation takes root, it becomes extremely difficult to challenge or correct, and over time, such actions can snowball into serious social, economic and national concerns.

Preventing this is a matter of shared responsibility. Governments can frame balanced regulations, but individuals must also take initiative: pause, think, and verify before sharing. Ultimately, the right to privacy must be upheld, but with reasonable safeguards to ensure it is not misused at the cost of societal trust and safety.

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References

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• India WhatsApp ‘child kidnap’ rumours claim two more victims (BBC) The people trying to fight fake news in India (BBC)
• Press Information Bureau – PIB Fact Check
• Brookings Institution – Encryption and Misinformation Report (2021)
• Curtis, T. L., Touzel, M. P., Garneau, W., Gruaz, M., Pinder, M., Wang, L. W., Krishna, S., Cohen, L., Godbout, J.-F., Rabbany, R., & Pelrine, K. (2024). Veracity: An Open-Source AI Fact-Checking System. arXiv.
• NDTV – PM Modi cautions against fake news (2022)
• Times of India – Govt may insist on WhatsApp traceability (2019)
• Medianama – Telegram refused to share ISIS channel data (2019)

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