#FactCheck - Viral Image of Bridge claims to be of Mumbai, but in reality it's located in Qingdao, China

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by the US Department of Defence in March 2025 found a crucial reality surrounding the operation of hardware relying on outdated industrial-age processes in the digital era.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/

In a recent ruling, a U.S. federal judge sided with Meta in a copyright lawsuit brought by a group of prominent authors who alleged that their works were illegally used to train Meta’s LLaMA language model. While this seems like a significant legal victory for the tech giant, it may not be so. Rather, this is a good case study for creators in the USA to refine their legal strategies and for policymakers worldwide to act quickly to shape the rules of engagement between AI and intellectual property.

The Case: Meta vs. Authors

In Kadrey v. Meta, the plaintiffs alleged that Meta trained its LLaMA models on pirated copies of their books, violating copyright law. However, U.S. District Judge Vince Chhabria ruled that the authors failed to prove two critical things: that their copyrighted works had been used in a way that harmed their market and that such use was not “transformative.” In fact, the judge ruled that converting text into numerical representations to train an AI was sufficiently transformative under the U.S. fair use doctrine. He also noted that the authors’ failure to demonstrate economic harm undermined their claims. Importantly, he clarified that this ruling does not mean that all AI training data usage is lawful, only that the plaintiffs didn’t make a strong enough case.

Meta even admitted that some data was sourced from pirate sites like LibGen, but the Judge still found that fair use could apply because the usage was transformative and non-exploitative.

A Tenuous Win

Chhabria’s decision emphasised that this is not a blanket endorsement of using copyrighted content in AI training. The judgment leaned heavily on the procedural weakness of the case and not necessarily on the inherent legality of Meta’s practices.

Policy experts are warning that U.S. courts are currently interpreting AI training as fair use in narrow cases, but the rulings may not set the strongest judicial precedent. The application of law could change with clearer evidence of commercial harm or a more direct use of content. 

Moreover, the ruling does not address whether authors or publishers should have the right to opt out of AI model training, a concern that is gaining momentum globally.

Implications for India

The case highlights a glaring gap in India’s copyright regime: it is outdated. Since most AI companies are located in the U.S., courts have had the opportunity to examine copyright in the context of AI-generated content. India has yet to start. Recently, news agency ANI filed a case alleging copyright infringement against OpenAI for training on its copyrighted material. However, the case is only at an interim stage. The final outcome of the case will have a significant impact on the legality of these language models being able to use copyrighted material for training.

Considering that India aims to develop “state-of-the-art foundational AI models trained on Indian datasets” under the IndiaAI Mission, the lack of clear legal guidance on what constitutes fair dealing when using copyrighted material for AI training is a significant gap.  

Thus, key points of consideration for policymakers include: 

• Need for Fair Dealing Clarity: India’s fair-dealing provisions under the Copyright Act, 1957, are narrower than U.S. fair use. The doctrine may have to be reviewed to strike a balance between this law and the requirement of diverse datasets to develop foundational models rooted in Indian contexts. A parallel concern regarding data privacy also arises. 
• Push for Opt-Out or Licensing Mechanisms: India should consider whether to introduce a framework that requires companies to license training data or provide an opt-out system for creators, especially given the volume of Indian content being scraped by global AI systems.
• Digital Public Infrastructure for AI: India’s policymakers could take this opportunity to invest in public datasets, especially in regional languages, that are both high quality and legally safe for AI training.
• Protecting Local Creators: India needs to ensure that its authors, filmmakers, educators and journalists are protected from having their work repurposed without compensation, since power asymmetries between Big Tech and local creators can lead to exploitation of the latter.

Conclusion

The ruling in Meta’s favour is just one win for the developer. The real questions about consent, compensation and creative control remain unanswered. Meanwhile, the lesson for India is urgent: it needs AI policies that balance innovation with creator rights and provide legal certainty and ethical safeguards as it accelerates its AI ecosystem. Further, as global tech firms race ahead, India must not remain a passive data source; it must set the terms of its digital future. This will help the country move a step closer to achieving its goal of building sovereign AI capacity and becoming a hub for digital innovation.

References

• https://www.theguardian.com/technology/2025/jun/26/meta-wins-ai-copyright-lawsuit-as-us-judge-rules-against-authors 
• https://www.wired.com/story/meta-scores-victory-ai-copyright-case/ 
• https://www.cnbc.com/2025/06/25/meta-llama-ai-copyright-ruling.html 
• https://www.mondaq.com/india/copyright/1348352/what-is-fair-use-of-copyright-doctrine 
• https://www.pib.gov.in/PressReleasePage.aspx?PRID=2113095#:~:text=One%20of%20the%20key%20pillars,models%20trained%20on%20Indian%20datasets. ‍
• https://www.ndtvprofit.com/law-and-policy/ani-vs-openai-delhi-high-court-seeks-responses-on-copyright-infringement-charges-against-chatgpt

Introduction

In the wake of the Spy Loan scandal, more than a dozen malicious loan apps were downloaded on Android phones from the Google Play Store, However, the number is significantly higher because they are also available on third-party marketplaces and questionable websites. 

Unmasking the Scam

When a user borrows money, these predatory lending applications capture large quantities of information from their smartphone, which is then used to blackmail and force them into returning the total with hefty interest levels. While the loan amount is disbursed to users, these predatory loan apps request sensitive information by granting access to the camera, contacts, messages, logs, images, Wi-Fi network details, calendar information, and other personal information. These are then sent to loan shark servers.

The researchers have disclosed facts about the applications used by loan sharks to mislead consumers, as well as the numerous techniques used to circumvent some of the limitations imposed on the Play Store. Malware is often created with appealing user interfaces and promotes simple and rapid access to cash with high-interest payback conditions. The revelation of the Spy Loan scandal has triggered an immediate response from law enforcement agencies worldwide. There is an urgency to protect millions of users from becoming victims of malicious loan apps, it has become extremely important for law enforcement to unmask the culprits and dismantle the cyber-criminal network.

Aap’s banned: here is the list of the apps banned by Google Play Store :

• AA Kredit: इंस्टेंट लोन ऐप (com.aa.kredit.android)
• Amor Cash: Préstamos Sin Buró (com.amorcash.credito.prestamo)
• Oro Préstamo – Efectivo rápido (com.app.lo.go)
• Cashwow (com.cashwow.cow.eg)
• CrediBus Préstamos de crédito (com.dinero.profin.prestamo.credito.credit.credibus.loan.efectivo.cash)
• ยืมด้วยความมั่นใจ – ยืมด่วน (com.flashloan.wsft)
• PréstamosCrédito – GuayabaCash (com.guayaba.cash.okredito.mx.tala)
• Préstamos De Crédito-YumiCash (com.loan.cash.credit.tala.prestmo.fast.branch.mextamo)
• Go Crédito – de confianza (com.mlo.xango)
• Instantáneo Préstamo (com.mmp.optima)
• Cartera grande (com.mxolp.postloan)
• Rápido Crédito (com.okey.prestamo)
• Finupp Lending (com.shuiyiwenhua.gl)
• 4S Cash (com.swefjjghs.weejteop)
• TrueNaira – Online Loan (com.truenaira.cashloan.moneycredit)
• EasyCash (king.credit.ng)
• สินเชื่อปลอดภัย – สะดวก (com.sc.safe.credit)
  

Risks with several dimensions

SpyLoan's loan application violates Google's Financial Services policy by unilaterally shortening the repayment period for personal loans to a few days or any other arbitrary time frame. Additionally, the company threatens users with public embarrassment and exposure if they do not comply with such unreasonable demands. 

Furthermore, the privacy rules presented by SpyLoan are misleading. While ostensibly reasonable justifications are provided for obtaining certain permissions, they are very intrusive practices. For instance, camera permission is ostensibly required for picture data uploads for Know Your Customer (KYC) purposes, and access to the user's calendar is ostensibly required to plan payment dates and reminders. However, both of these permissions are dangerous and can potentially infringe on users' privacy.

Prosecution Strategies and Legal Framework

The law enforcement agencies and legal authorities initiated prosecution strategies against the individuals who are involved in the Spy Loan Scandal, this multifaced approach involves international agreements and the exploration of innovative legal avenues. Agencies need to collaborate with International agencies to work on specific cyber-crime, leveraging the legal frameworks against digital fraud furthermore, the cross-border nature of the spy loan operation requires a strong legal framework to exchange information, extradition requests, and the pursuit of legal actions across multiple jurisdictions.

Legal Protections for Victims: Seeking Compensation and Restitution

As the legal battle unfolds in the aftermath of the Spy loan scam the focus shifts towards the victims, who suffer financial loss from such fraudulent apps. Beyond prosecuting culprits, the pursuit of justice should involve legal safeguards for victims. Existing consumer protection laws serve as a crucial shield for Spy Loan victims. These laws are designed to safeguard the rights of individuals against unfair practices.

Challenges in legal representation

As the legal hunt for justice in the Spy Loan scam progresses, it encounters challenges that demand careful navigation and strategic solutions. One of the primary obstacles in the legal pursuit of the Spy loan app lies in the jurisdictional complexities. Within the national borders, it’s quite challenging to define the jurisdiction that holds the authority, and a unified approach in prosecuting the offenders in various regions with the efforts of various government agencies.

Concealing the digital identities

One of the major challenges faced is the anonymity afforded by the digital realm poses a challenge in identifying and catching the perpetrators of the scam, the scammers conceal their identity and make it difficult for law enforcement agencies to attribute to actions against the individuals, this challenge can be overcome by joint effort by international agencies and using the advance digital forensics and use of edge cutting technology to unmask these scammers.

Technological challenges

The nature of cyber threats and crime patterns are changing day by day as technology advances this has become a challenge for legal authorities, the scammers explore vulnerabilities, making it essential, for law enforcement agencies to be a step ahead, which requires continuous training of cybercrime and cyber security.

Shaping the policies to prevent future fraud

As the scam unfolds, it has become really important to empower users by creating more and more awareness campaigns. The developers of the apps need to have a transparent approach to users.

Conclusion

It is really important to shape the policies to prevent future cyber frauds with a multifaced approach. Proposals for legislative amendments, international collaboration, accountability measures, technology protections, and public awareness programs all contribute to the creation of a legal framework that is proactive, flexible, and robust to cybercriminals' shifting techniques. The legal system is at the forefront of this effort, playing a critical role in developing regulations that will protect the digital landscape for years to come.

Safeguarding against spyware threats like SpyLoan requires vigilance and adherence to best practices. Users should exclusively download apps from official sources, meticulously verify the authenticity of offerings, scrutinize reviews, and carefully assess permissions before installation.

References

• https://www.bleepingcomputer.com/news/security/spyloan-android-malware-on-google-play-downloaded-12-million-times/#google_vignette

• https://www.gadgets360.com/apps/news/spyloan-malware-blackmail-extort-users-detected-play-store-4639194

• https://thehackernews.com/2023/12/spyloan-scandal-18-malicious-loan-apps.html

• https://www.the420.in/exposed-18-deceptive-loan-apps-google-play/