#FactCheck-Old 2020 Lockdown Video of PM Modi Resurfaces as Recent

What are Wi-Fi attacks?

Wi-fi is an important area of cyber security and there is no need for physical cable for the network. Wi-Fi has access to a network signal radius everywhere. The devices and systems can have a network without physical access due to Wi-fi. But everything comes with cons and pros, and if we talk about cybersecurity, it has been established that Wi-fi networks are extremely vulnerable to security breaches and it is very easy to be hacked by hackers. Wi-Fi can be accessed by almost every device in the modern day: it can be smartphones, tablets, computers, and laptops. To know whether someone has been tampering with your personal Wi-Fi there are certain signs that can prove it. The first and most important sign is that your internet speed gets slower, as someone else is using your Wi-Fi surf.

Why would anyone hack someone’s Wi-Fi network?

Usually, hackers hack the network because they want access to the confidential data of someone and they can observe all the online activities and data that have been sent through a network. An unauthorize hacker will pretty much be able to see everything you do online. Wi-Fi allows hackers o view information on sites. Any financial information which is saved in the browser can be accessed by hackers and they can alter it and can alter the content you see online. And all the information saved in Wi-fi networks can be used by hackers for their own benefit, they can sell it, impersonate you, or even take money out of your bank through Wi-Fi.

Avoiding vulnerable Wi-Fi networks

The first and foremost rule of protection is that you should not use public networks if that network is easily open to you then that is also available to others and from others, and someone can who wishes to use your confidential and sensitive information, can access that.  If you really need to access the public network in an urgent situation, then you must make sure to limit your activities while connected. And avoid accessing your online banking or pages that require login information. Also, a good measure to take as well is to always delete your cookies after using public WIFI.

How To Secure Your Home Wi-Fi Network

Your home’s wireless internet connection is your Wi-Fi network. Typically, a wireless router is used, which broadcasts a signal into the atmosphere. You can connect to the internet using that signal. However, if your network is not password-protected, any nearby device can grab the signal off the air and connect to your internet. The benefit of Wi-Fi? Wireless access to the internet is possible. The negative? Your internet activity, including your personal information, may be visible to neighboring users who connect to your unprotected network. Furthermore, if someone uses your network to conduct a crime or send out unauthorized spam, you might be held accountable.

Wi-Fi or Li-Fi? –

The common consensus is that Li-Fi technology is more secure than Wi-Fi. Li-Fi systems can be made more secure by integrating a variety of security features. Although these qualities might appear when Li-Fi is widely used in the near future, it is already thought to be safer because of a number of security features. Since the connection’s characteristics make it simpler to lock connections, limit access, and track users even in the absence of encryption and other security features, Li-Fi is seen as being safer. Li-Fi systems will be able to support new security protocols, which will not only enable high-speed networking but also open the door for innovative security techniques to strengthen connections.

Conclusion

A hacker can sniff the network packets without having to be in the same building where the network is located. As wireless networks communicate through radio waves, a hacker can easily sniff the network from a nearby location. Most attackers use network sniffing to find the SSID and hack a wireless network.

Any wireless network can theoretically be attacked in a number of different ways. Use of the default SSID or password, WPS pin authentication, insufficient access control, and leaving the access point available in open locations are all examples of potential vulnerabilities that could allow for the theft of sensitive data. Kismet’s architecture in WIDS mode may guard against DOS, MiTM, and MAC spoofing attacks. routine software updates on the other hand, the use of firewalls may help defend the network against outside intrusion. The act of finding infrastructure issues that could allow harmful code to be injected into a service, system, or organization is known as ethical hacking. They use this technique to prevent invasions by lawfully breaking into networks and looking for weak spots.

The Expanding Governance Challenge of Artificial Intelligence
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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).
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The Lifecycle of an AI System
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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.

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Where AI Risks Emerge
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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.

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AI Governance Risk Landscape: Core Risk Categories Under International Frameworks
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 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. 
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AI System Lifecycle: Key Risks at Each Stage
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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.
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Policy Tools for Mitigating AI Risks
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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. 
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Governance Overlay: Policy Interventions Across the AI Lifecycle
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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.
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The Policy Gap in AI Governance
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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
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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.
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Recommendations
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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.
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Conclusion - The Need for Global AI Governance
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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.
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References
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• 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

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Introduction

Since users are now constantly retrieving critical data on their mobile devices, fraudsters are now focusing on these devices. App-based, network-based, and device-based vulnerabilities are the three main ways of attacking that Mobile Endpoint Security names as mobile threats. Composed of the following features: program monitoring and risk, connection privacy and safety, psychological anomaly and reconfiguration recognition, and evaluation of vulnerabilities and management, this is how Gartner describes Mobile Threat Defense (MTD).

The widespread adoption and prevalence of cell phones among consumers worldwide have significantly increased in recent years. Users of these operating system-specific devices can install a wide range of software, or "apps," from online marketplaces like Google Play and the Apple App Store. The applications described above are the lifeblood of cell phones; they improve users' daily lives and augment the devices' performance. The app marketplaces let users quickly search for and install new programs, but certain malicious apps/links/websites can also be the origin of malware hidden among legitimate apps. These days, there are many different security issues and malevolent attacks that might affect mobile devices.

Unveiling Malware Landscape

The word "malware" refers to a comprehensive category of spyware intended to infiltrate networks, steal confidential data, cause disruptions, or grant illegal access. Malware can take many forms, such as Trojan horses, worms, ransomware, infections, spyware, and adware. Because each type has distinct goals and features, security specialists face a complex problem. Malware is a serious risk to both people and businesses. Security incidents, monetary losses, harm to one's credibility, and legal repercussions are possible outcomes. Understanding malware's inner workings is essential to defend against it effectively. Malware analysis is helpful in this situation. The practice of deconstructing and analysing dangerous software to comprehend its behaviour, operation, and consequences is known as malware analysis.Major threats targeting mobile phones

Viruses: Viruses are self-renewing programs that can steal data, launch denial of service assaults, or enact ransomware strikes. They spread by altering other software applications, adding malicious code, and running it on the target's device. Computer systems all over the world are still infected with viruses, which attack different operating systems like Mac and Microsoft Windows, even though there is a wealth of antiviral programs obtainable to mitigate their impacts.

Worms: Infections are independent apps that propagate quickly and carry out payloads—such as file deletion or the creation of botnets—to harm computers. Worms, in contrast to viruses, usually harm a computer system, even if it's just through bandwidth use. By taking advantage of holes in security or other vulnerabilities on the target computer, they spread throughout computer networks.

Ransomware: It causes serious commercial and organisational harm to people and businesses by encrypting data and demanding payment to unlock it. The daily operations of the victim organisation are somewhat disrupted, and they need to pay a ransom to get them back. It is not certain, though, that the financial transaction will be successful or that they will receive a working translation key.

Adware: It can be controlled via notification restrictions or ad-blockers, tracks user activities and delivers unsolicited advertisements. Adware poses concerns to users' privacy even though it's not always malevolent since the information it collects is frequently combined with information gathered from other places and used to build user profiles without their permission or knowledge.

Spyware: It can proliferate via malicious software or authentic software downloads, taking advantage of confidential data. This kind of spyware gathers data on users' actions without their authorisation or agreement, including:Internet activityBanking login credentialsPasswordsPersonally Identifiable Information (PII)

Navigating the Mobile Security Landscape

App-Centric Development: Regarding mobile security, app-centric protections are a crucial area of focus. Application authorisations should be regularly reviewed and adjusted to guarantee that applications only access the knowledge that is essential and to lower the probability of data misuse. Users can limit hazards and have greater oversight over their confidentiality by closely monitoring these settings. Installing trustworthy mobile security apps also adds another line of protection. With capabilities like app analysis, real-time protection, and antivirus scanning, these speciality apps strengthen your gadget's protection against malware and other harmful activity.

Network Security: Setting priorities for secure communication procedures is crucial for safeguarding confidential data and thwarting conceivable dangers in mobile security. Avoiding unprotected public Wi-Fi networks is essential since they may be vulnerable to cyberattacks. To lessen the chance of unwelcome entry and data surveillance, promote the usage of reliable, password-protected networks instead. Furthermore, by encrypting data transfer, Virtual Private Networks (VPNs) provide additional protection and make it more difficult for malevolent actors to corrupt information. To further improve security, avoid using public Wi-Fi for essential transactions and hold off until a secure network is available. Users can strengthen their handheld gadgets against possible privacy breaches by implementing these practices, which can dramatically lower the risk of data eavesdropping and illegal access.

Constant development: Maintaining a robust mobile security approach requires a dedication to constant development. Adopt a proactive stance by continuously improving and modifying your security protocols. By following up on recurring outreach and awareness campaigns, you can stay updated about new hazards. Because cybersecurity is a dynamic field, maintaining one step ahead and utilising emerging technologies is essential. Stay updated with security changes, implement the newest safeguards, and incorporate new industry standard procedures into your plan. This dedication to ongoing development creates a flexible barrier, strengthening your resistance to constantly evolving mobile security threats.

Threat emergency preparedness: To start, familiarise yourself with the ever-changing terrain associated with mobile dangers to security. Keep updated on new threats including malware, phishing, and illegal access.

Sturdy Device Management: Put in place a thorough approach to device management. This includes frequent upgrades, safe locking systems, and additional safeguarding capabilities like remote surveillance and erasing.

Customer Alertness: Emphasise proper online conduct and acquaint yourself and your team with potential hazards, such as phishing efforts.

Dynamic Measures for a Robust Wireless Safety Plan

In the dynamic field of mobile assurances, taking a proactive strategy is critical. To strengthen safeguards, thoroughly research common risks like malware, phishing, and illegal access. Establish a strong device management strategy that includes frequent upgrades, safe locking mechanisms, and remote monitoring and deletion capabilities for added security.

Promoting user awareness by educating people so they can identify and block any hazards, especially regarding phishing attempts. Reduce the dangers of data eavesdropping and illegal access by emphasising safe communication practices, using Virtual Private Networks (VPNs), and avoiding public Wi-Fi for essential transactions.

Pay close attention to app-centric integrity by periodically checking and modifying entitlements. Downloading trustworthy mobile security apps skilled at thwarting malware and other unwanted activity will enhance your smartphone's defenses. Lastly, create an atmosphere of continuous development by keeping up with new threats and utilising developing technology to make your handheld security plan more resilient overall.

Conclusion

Mobile privacy threats grow as portable electronics become increasingly integrated into daily activities. Effective defense requires knowledge of the various types of malware, such as worms, ransomware, adware, and spyware. Tools for Mobile Threat Defense, which prioritise vulnerability assessment, management, anomaly detection, connection privacy, and program monitoring, are essential. App-centric development, secure networking procedures, ongoing enhancement, threat readiness, strong device control, and user comprehension are all components of a complete mobile security strategy. People, as well as organisations, can strengthen their defenses against changing mobile security threats by implementing dynamic measures and maintaining vigilance, thereby guaranteeing safe and resilient mobile surrounding.

References

‍https://www.titanfile.com/blog/types-of-computer-malware/

‍https://www.simplilearn.com/what-is-a-trojan-malware-article

‍https://www.linkedin.com/pulse/latest-anti-analysis-tactics-guloader-malware-revealed-ukhxc/?trk=article-ssr-frontend-pulse_more-articles_related-content-card