#FactCheck - AI‑Manipulated Audio Used to Falsely Claim Trump Warned India Over Russia Ties

Introduction

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Cyberwarfare has evolved into one of the most decisive instruments of statecraft and conflict. The increasing digitisation of critical infrastructure like power grids, water systems, transportation systems, healthcare networks, and energy sources has made these systems new targets in the war of algorithms. Military logic is evolving to paralyse the nation’s critical infrastructure to keep its resources engaged in repairing them and thereby break the nation’s ability to deter and counter attacks, all without firing a single bullet. 

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From Ransomware to an Invisible Sabotage: The changing nature of warfare

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The operational technology (OT) landscape has become the epicentre of cyber operations, all around the world. Once, which was insulated, related to industrial systems that controlled turbines, pipelines, or dams, they now stand connected to the Internet through supervisory control and data acquisition (SCADA) and the Internet of Things. These connections have also become gateways for attackers, besides enhancing the efficiency of the infrastructural lifelines of the nation.   

Groups like Volt Typhoon, Sandworm, Laurionite, and Cyberavengers have transformed the art of digital infiltration into a strategic shift. Volt Typhoon, which is linked to China, has used “living-off-the-land” techniques to exploit the legitimate administrative tools to remain invisible while scanning the critical infrastructures in the US. Sandworm, which is aligned with Russia’s GRU (Glavnoye Razvedyvatelnoye Upravlenie) or Main Intelligence Directorate (in English), has demonstrated the power of cyber sabotage in real time, as its attacks on Ukraine’s power grids in 2015 and 2021 had left millions in darkness, coinciding with kinetic missile strikes. Meanwhile, the Iranian-affiliated Cyberavengers group, which has weaponised the AI-assisted malware, such as IOCONTROL, that are capable of hijacking water and energy control systems. Each of these systems used in these operations reflects a shift from direct espionage activities to a state of strategic paralysis. 

In comparison to the traditional cybercrime activities that are aimed at stealing data and extortion of money, these campaigns repeatedly target the physical systems, which consist of the machinery that sustains civilian life and military preparedness.  

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The Military Logic behind Cyber Targeting: A Web of Vulnerabilities 

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A critical infrastructure is a complex ecosystem that covers power generation, transportation, communication, and manufacturing are all interconnected, which means a single compromised node can cascade into a national paralysis. For instance, a breach in the systems of the dam can flood an entire city, a grid shutdown can halt water supply to hospitals, and even affect air traffic. The 2015 Black Energy Malware attack in Ukraine has proved this possibility when three utilities were hacked, plunging thousands of homes into darkness. The Iranian hackers once again gained access to the Bowman Avenue Dam of New York and controlled its floodgates, which gave a chilling demonstration of the destructive reality of digital manipulation. 

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The systems remain vulnerable mainly for 3 reasons such as- 

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• Legacy Architectures: Many of these industrial systems were designed decades ago with no built-in cybersecurity mechanisms. 
• Slow Patching and Segmentation Gaps: All updates and segmentation between IT and TO networks often lag, providing open entry points for attackers. 
• Converging with IoT: The integration of smart sensors and cloud-based management tools has expanded the attack surface exponentially. 

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This interconnected fragility has turned our critical infrastructures into both a weapon and a target or a tool for coercion in modern hybrid warfare. Between 2023 and 2024, over 420 cyberattacks were witnessed in several critical global infrastructures, which averaged to 13 attacks per second, according to a news report. These were not just random acts of digital vandalism; they were deliberate and coordinated operational attempts by state-led actors from China, Russia, and Iran. 

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Developing a new Resilience as the new tool of Deterrence 

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Cyber deterrence no longer rests on the fear of retaliation, it relies on the need for resilience. Nations that can absorb attacks, maintain continuity, and recover rapidly would be the true superpowers of this digital age. Segmentation, real-time threat detection, and AI-assisted recovery models are vital pillars of this model of resilience. The logic of modern cyberwarfare is clear, which means that the more a nation digitizes, the more it will need to defend itself. 

However, as the line between war and peace blurs, safeguarding critical infrastructure is no longer just an IT priority; rather, it is a national security doctrine. In this silent theatre of cyberwarfare, survival will depend not only on firepower, but on firewalls. 

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References

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• https://rmcglobal.com/critical-infrastructure-under-siege-the-top-ot-threats-of-2025/
• https://ccdcoe.org/uploads/2018/10/Geers2009_The-Cyber-Threat-to-National-Critical-Infrastructures.pdf
• https://www.researchgate.net/publication/335752979_Cybersecurity_of_Critical_Infrastructure 
• https://arxiv.org/html/2510.04118v1 ‍
• https://www.anapaya.net/blog/top-5-critical-infrastructure-cyberattacks

Introduction 

In the interconnected world of social networking and the digital landscape, social media users have faced some issues like hacking. Hence there is a necessity to protect your personal information and data from scammers or hackers. In case your email or social media account gets hacked, there are mechanisms or steps you can utilise to recover your email or social media account. It is important to protect your email or social media accounts in order to protect your personal information and data on your account. It is always advisable to keep strong passwords to protect your account and enable two-factor authentication as an extra layer of protection. Hackers or bad actors can take control of your account, they can even change the linked mail ID or Mobile numbers to take full access to your account.

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Recent Incident

Recently, a US man's Facebook account was deleted or disabled by Facebook. He has sued Facebook and initiated a legal battle. He has contended that there was no violation of any terms and policy of the platform, and his account was disabled. In the first instance, he approached the platform. However, the platform neglected his issue then he filed a suit, where the court ordered Facebook's parent company, Meta, to pay $50,000 compensation, citing ignorance of the tech company. 

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Social media account recovery using the ‘Help’ Section

If your Facebook account has been disabled, when you log in to your account, you will see a text saying that your account is disabled. If you think that your account is disabled by mistake, in such a scenario, you can make a request to Facebook to ‘review’ its decision using the help centre section of the platform. To recover your social media account, you can go to the “Help” section of the platform where you can fix a login problem and also report any suspicious activity you have faced in your account. 

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Best practices to stay protected

1. Strong password: Use strong and unique passwords for your email and all social media accounts.
2. Privacy settings: You can utilise the privacy settings of the social media platform, where you can set privacy as to who can see your posts and who can see your contact information, and you can also keep your social media account private. You might have noticed a few accounts on which the user's name is unusual and isn’t one which you recognise. The account has few or no friends, posts, or visible account activity.
3. Avoid adding unknown users or strangers to your social networking accounts: Unknown users might be scammers who can steal your personal information from your social media profiles, and such bad actors can misuse that information to hack into your social media account. 
4. Report spam accounts or posts: If you encounter any spam post, spam account or inappropriate content, you can report such profile or post to the platform using the reporting centre. The platform will review the report and if it goes against the community guidelines or policy of the platform. Hence, recognise and report spam, inappropriate, and abusive content.
5. Be cautious of phishing scams: As a user, we encounter phishing emails or links, and phishing attacks can take place on social media as well. Hence, it is important that do not open any suspicious emails or links. On social media, ‘Quiz posts’ or ‘advertisement links’ may also contain phishing links, hence, do not open or click on such unauthenticated or suspicious links.

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Conclusion

We all use social media for connecting with people, sharing thoughts, and lots of other activities. For marketing or business, we use social media pages. Social media offers a convenient way to connect with a larger community. We also share our personal information on the platform. It becomes important to protect your personal information, your email and all your social media accounts from hackers or bad actors. Follow the best practices to stay safe, such as using strong passwords, two-factor authentication, etc. Hence contributing to keeping your social media accounts safe and secure.

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References:

• https://www.gpb.org/news/2023/07/11/facebook-wrongfully-deleted-his-account-he-sued-the-company-and-won
• https://consumer.ftc.gov/articles/how-recover-your-hacked-email-or-social-media-account

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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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