#FactCheck -Social Media Claim of ICC’s One-Year Ban on Pakistan Cricket Is Misleading

Introduction

Deepfakes are artificial intelligence (AI) technology that employs deep learning to generate realistic-looking but phoney films or images. Algorithms use large volumes of data to analyse and discover patterns in order to provide compelling and realistic results. Deepfakes use this technology to modify movies or photos to make them appear as if they involve events or persons that never happened or existed.The procedure begins with gathering large volumes of visual and auditory data about the target individual, which is usually obtained from publicly accessible sources such as social media or public appearances. This data is then utilised for training a deep-learning model to resemble the target of deep fakes.

Recent Cases of Deepfakes-‍

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In an unusual turn of events, a man from northern China became the victim of a sophisticated deep fake technology. This incident has heightened concerns about using artificial intelligence (AI) tools to aid financial crimes, putting authorities and the general public on high alert.
During a video conversation, a scammer successfully impersonated the victim’s close friend using AI-powered face-swapping technology. The scammer duped the unwary victim into transferring 4.3 million yuan (nearly Rs 5 crore). The fraud occurred in Baotou, China.

AI ‘deep fakes’ of innocent images fuel spike in sextortion scams
Artificial intelligence-generated “deepfakes” are fuelling sextortion frauds like a dry brush in a raging wildfire. According to the FBI, the number of nationally reported sextortion instances came to 322% between February 2022 and February 2023, with a notable spike since April due to AI-doctored photographs. And as per the FBI, innocent photographs or videos posted on social media or sent in communications can be distorted into sexually explicit, AI-generated visuals that are “true-to-life” and practically hard to distinguish. According to the FBI, predators often located in other countries use doctored AI photographs against juveniles to compel money from them or their families or to obtain actual sexually graphic images.

Deepfake Applications

• Lensa AI.
• Deepfakes Web.
• Reface.
• MyHeritage.
• DeepFaceLab.
• Deep Art.
• Face Swap Live.
• FaceApp.

Deepfake examples

There are numerous high-profile Deepfake examples available. Deepfake films include one released by actor Jordan Peele, who used actual footage of Barack Obama and his own imitation of Obama to convey a warning about Deepfake videos.
A video shows Facebook CEO Mark Zuckerberg discussing how Facebook ‘controls the future’ with stolen user data, most notably on Instagram. The original video is from a speech he delivered on Russian election meddling; only 21 seconds of that address were used to create the new version. However, the vocal impersonation fell short of Jordan Peele’s Obama and revealed the truth.

The dark side of AI-Generated Misinformation

• Misinformation generated by AI-generated the truth, making it difficult to distinguish fact from fiction.
• People can unmask AI content by looking for discrepancies and lacking the human touch.
• AI content detection technologies can detect and neutralise disinformation, preventing it from spreading.

Safeguards against Deepfakes-

Technology is not the only way to guard against Deepfake videos. Good fundamental security methods are incredibly effective for combating Deepfake.For example, incorporating automatic checks into any mechanism for disbursing payments might have prevented numerous Deepfake and related frauds. You might also:

• Regular backups safeguard your data from ransomware and allow you to restore damaged data.
• Using different, strong passwords for different accounts ensures that just because one network or service has been compromised, it does not imply that others have been compromised as well. You do not want someone to be able to access your other accounts if they get into your Facebook account.
• To secure your home network, laptop, and smartphone against cyber dangers, use a good security package such as Kaspersky Total Security. This bundle includes anti-virus software, a VPN to prevent compromised Wi-Fi connections, and webcam security.

What is the future of Deepfake –

Deepfake is constantly growing. Deepfake films were easy to spot two years ago because of the clumsy movement and the fact that the simulated figure never looked to blink. However, the most recent generation of bogus videos has evolved and adapted.
There are currently approximately 15,000 Deepfake videos available online. Some are just for fun, while others attempt to sway your opinion. But now that it only takes a day or two to make a new Deepfake, that number could rise rapidly.

Conclusion-

The distinction between authentic and fake content will undoubtedly become more challenging to identify as technology advances. As a result, experts feel it should not be up to individuals to discover deep fakes in the wild. “The responsibility should be on the developers, toolmakers, and tech companies to create invisible watermarks and signal what the source of that image is,” they stated. Several startups are also working on approaches for detecting deep fakes.

Introduction
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How Generative Artificial Intelligence, or GenAI, is changing the employee workday is no longer limited to writing emails or debugging code, but now also includes analysing contracts, generating reports, and much more. The use of AI tools in everyday work has become commonplace, but the speed at which companies have adopted these technologies has created a new kind of risk. Unlike threats that come from an outside attacker, Shadow AI is created inside an organisation by a legitimate employee who uses unapproved AI tools to make their work more efficient and productive. In many cases, the employee is unaware of the potential security, data privacy, and compliance risks involved in using such tools to perform their job duties.
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What Is Shadow AI?

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Shadow AI is when individuals use AI tools at work that aren’t provided by the company, like tools or other software programs, without the knowledge or permission of the employer. Examples of shadow AI include:

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1. Using personal ChatGPT or other chatbot accounts to complete tasks at the office
2. Uploading business-related documents to online AI technologies for analysis or summarisation.
3. Copying proprietary source code into an online AI model for debugging
4. Installing browser extensions and add-ons that are not approved by IT or Security personnel.

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How Shadow AI Is Harmful

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1. Uncontrolled Data Exposure

When employees access or input information into their user-created AI, it becomes outside the controls of the company, such as both employee personal information and any third-party personal information, private company information (such as source code or contracts), and company internal strategies. After a user enters data into their user-created AIs, the company loses all ability to monitor how that data is stored, processed, or maintained. A data leak situation exists without a malicious cyberattack. The biggest risk of a data leak is not maliciousness but rather the loss of control and governance over sensitive data.

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2. Regulatory and Legal Non-Compliance

Data protection laws like GDPR, India’s Digital Personal Data Protection (DPDP) Act, HIPAA, and other relevant sectoral laws require businesses to process data in accordance with the law, to minimise the amount of data they use, and to be accountable for their actions. Shadow AI often results in the unlawful use of personal data due to a lack of a legal basis for the processing, unauthorised cross-border data transfers, and not having appropriate contractual protections in place with their AI service providers. Regulators do not see the convenience of employees as an excuse for not complying with the law, and therefore, the organisation is ultimately responsible for any violations that occur.

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3. Loss of Intellectual Property

Employees frequently use AI tools to speed up tasks involving proprietary information—debugging code, reviewing contracts, or summarising internal research. When done using unapproved AI platforms, this can expose trade secrets and intellectual property, eroding competitive advantage and creating long-term business risk.

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Real-Life Example: Samsung’s ChatGPT Data Leak

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In 2023, a case study exemplifying the Shadow AI risk occurred when Samsung Electronics placed a temporary ban on employee access to ChatGPT and other AI tools after reports from engineers revealed they were using ChatGPT to create debugging processes for internal source code and to summarise meeting notes. Consequently, confidential source code related to semiconductors was inadvertently uploaded onto a public AI platform. While there were no known incursions into the company’s system due to this incident, Samsung faced a significant challenge: once sensitive information is input into a public AI tool, it exists on external servers that are outside of the company’s purview or control.

As a result of this incident, Samsung restricted employee use of ChatGPT on corporate devices, issued a series of internal communications prohibiting the sharing of corporate data with public AI tools, and increased the urgency of their discussions regarding the adoption of secure, enterprise-level AI (artificial intelligence) solutions.

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What Organisations Are Doing Today

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Many organisations respond to Shadow AI risk by:

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1. Blocking access at the network level
2. Circulating warning emails or policies

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While these actions may reduce immediate exposure, they fail to address the root cause: employees still need AI to perform their jobs efficiently. As a result, bans often push AI usage underground, increasing Shadow AI rather than eliminating it.

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Why Blocking AI Does Not Work—Governance Does

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History has demonstrated that prohibition does not work - we see this when trying to block access to cloud storage, instant messaging and collaboration tools. Employees are forced to use personal devices and/or accounts when their employers block AI, which means employers do not have real-time visibility into how their employees are using these technologies, and creates friction with the security and compliance team as they try to enforce the types of tools their employees can use. Prohibiting AI adoption will not stop it from being adopted; it will just create a challenge for employers regarding how safe and responsible it is. The challenge for effective organisations is therefore to shift from denial and develop governance-first AI strategies aimed at controlling data usage, protection and security, rather than merely restricting access to a list of specific tools.

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Shadow AI: A Silent Legal Liability Under the GDPR

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Shadow AI isn't a problem for the Information Technology Department; it is a failure of Governance, Compliance and Law. By using AI tools that have not been approved as a result, the organisation processes personal data without a lawful basis (Article 6 of the General Data Protection Regulation (GDPR)), repurposes data for use beyond its original intent and in breach of the Purpose Limitation (Article 5(1)(b)), and routinely exceeds necessity and in breach of Data Minimisation (Article 5(1)(c)). The outcome of these actions is the use of tools that involve International Data Transfers Without Authorisation and are therefore in breach of Chapter V, and violate Article 32 because there are no enforceable safeguards in place. Most significantly, the failure to demonstrate Oversight, Logging and Control under Articles 5(2) and 24 constitutes a failure in Accountability. Therefore, from a Regulatory perspective, Shadow AI is not accidental and is not defensible.

The Right Solution: Secure and Governed AI Adoption

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1. Provide Approved AI Tools

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Employers have an obligation to supply business-approved AI technology for helping workers to be productive while maintaining maximum protections, like storing data separately and not using employees' data for training a model; defining how long data is kept, and the rules around deleting that data. When employees are provided with verified and secure AI options that align with their work processes, they will rely significantly less on Shadow AI.

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2. Enforce Zero-Trust Data Access

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The governance of AI systems must follow the principles of "zero trust," granting access to data only through the principle of "least privilege," which means that data access will only be allowed by the system user, and providing continuous verification of user-identity and context; this supports and helps establish context-aware controls to monitor and track all user activities, which will be especially important as agent-like AI systems become increasingly autonomous and are capable of operating at machine-speed where even small errors in configuration, will result in rapid and large expose to data.

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3. Apply DLP and Audit Logging

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It is important to have robust data loss prevention measures in place to protect sensitive data that is sent outside an organisation. The first end user or machine that accesses the data should be detailed in a comprehensive audit log that indicates when and how the data is accessed. In combination with other controls, these measures create accountability, comply with regulations, and assist with appropriately detecting and responding to incidents.

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4. Maintain Visibility Across AI, Cloud, and SaaS

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Security teams need unified visibility across AI tools, personal cloud applications, and SaaS platforms. Risks move across systems, and controls must follow the data wherever it flows.

Conclusion

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This new threat exposes an organisation to the risk of data loss through leaks, regulatory fines, liability for the loss of intellectual property, and reputational damage, all of which can occur without any intent to cause harm. The way forward is not to block AI, but to adopt a clear framework built on governance, visibility, and secure enablement. This approach allows organisations to use AI with confidence, while ensuring trust, accountability, and effective oversight to protect data and support AI in reaching its full transformative potential. AI use is encouraged, but it must be done responsibly, ethically, and securely.

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References 

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1. https://bronson.ai/resources/shadow-ai/
2. https://www.varonis.com/blog/shadow-ai
3. https://www.waymakeros.com/learn/gdpr-hipaa-shadow-ai-compliance-nightmare
4. https://www.forbes.com/sites/siladityaray/2023/05/02/samsung-bans-chatgpt-and-other-chatbots-for-employees-after-sensitive-code-leak/
5. https://www.usatoday.com/story/special/contributor-content/2025/05/23/shadow-ai-the-hidden-risk-in-todays-workplace/83822081007

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Introduction

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The recent cyber-attack on Jaguar Land Rover (JLR), one of the world's best-known car makers, has revealed extensive weaknesses in the interlinked character of international supply chains. The incident highlights the increasing cybersecurity issues of industries going through digital transformation. With its production stopped in several UK factories, supply chain disruptions, and service delays to its customers worldwide, this cyber-attack shows how cyber events can ripple into operation, finance, and reputation risks for large businesses.

The Anatomy of a Breakdown

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Jaguar Land Rover, a Tata Motors subsidiary, was forced to disable its IT infrastructure because of a cyber-attack over the weekend. This shut down was already an emergency shut down to mitigate damage and the disruption to business was serious. 

• No Production - The car plants at Halewood (Merseyside) and Solihull (West Midlands) and the engine plant (Wolverhampton) were all completely shut down.
• Sales and Distribution: Car sales were significantly impaired during a high-volume registration period in September, although certain transactions still passed through manual procedures.
• Global Effect: The breakdown did not reach only the UK, dealers and fix experts across the world, including in Australia, suffered with inaccessible parts databases.

JLR called the recovery process "extremely complex" as it involved a controlled recovery of systems and implementing alternative workarounds for offline services. The overall effects include the immediate and massive impact to their suppliers and customers, and has raised larger questions regarding the sustainability of digital ecosystems in the automobile value chain.

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The Human Impact: Beyond JLR's Factories

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The implications of the cyber-attack have extended beyond the production lines of JLR:

• Independent Garages: Repair centres such as Nyewood Express of West Sussex indicated that they could not use vital parts databases, which brought repair activities to a standstill and left clients waiting indefinitely.
• Global Dealers: Land Rover experts as distant as Tasmania indicated total system crashes, highlighting global dependency on centralized IT systems.
• Customer Frustration: Regular customers in need of urgent repairs were stranded by the inability to order replacement parts from original manufacturers.

This attack is an example of the cascading effect of cyber disruptions among interconnected industries, a single point of failure paralyzing complete ecosystems.

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The Culprit: The Hacker Collective

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The hack is justifiably claimed by a so-called hacker collective "Scattered Lapsus$ Hunters." The so-called hacking collective says that it consists of young English-speaking hackers and has previously targeted blue-chip brands like Marks & Spencer. While the attackers seem not to have publicly declared whether they exfiltrated sensitive information or deployed ransomware, they went ahead and posted screenshots of internal JLR documents-the kind of documents that probably are not supposed to see the light of day, including troubleshooting guides and system logs-implicating what can only be described as grossly unauthorized access into some of Jaguar Land Rover's core IT systems.

Jaguar Land Rover had gone on record to claim with no apropos proof or evidence that it probably did not see anyone getting into customer data; however, the very occurrence of this attack raises some very serious questions on insider threats, social engineering concepts, and how efficient cybersecurity governance architectures really are.

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Cybersecurity Weaknesses and Lessons Learned

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The JLR attack depicts some of the common weaknesses associated with large-scale manufacturing organizations:

1. Centralized IT Dependencies: Today's auto firms are based on worldwide IT systems for operations, logistics, and customer care. Compromise can lead to broad outages.
2. Supply Chain Vulnerabilities: Tier-2 and Tier-1 suppliers use OEM systems for placing and tracing components. Interrupting at the OEM level automatically stops their processes.
3. Inadequate Incident Visibility: Several suppliers complained about no clear information from JLR, which increased uncertainty and financial loss.
4. Rise of Youth Hacking Groups: Involvement of youth hacker groups highlight the necessity for active monitoring and community-level cybersecurity awareness initiatives.

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Broader Industry Context

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With ever-increasing cyber-attacks on the automotive industry, an area currently being rapidly digitalised through connected cars, IoT-based factories, and cloud-based operations, this series of incidents falls within such a context. In 2023, JLR awarded an £800 million contract to Tata Consultancy Services (TCS) for services in support of the company's digital transformation and cybersecurity enhancement. This attack shows that, no matter how much is spent, poorly conceptualised security programs can never stand up to ever-changing cyber threats.

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What Can Organizations Do? – Cyberpeace Recommendations

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To contain risks and develop a resilience against such events, organizations need to implement a multi-layered approach to cybersecurity:

1. Adopt Zero Trust Architecture - Presume breach as the new normal. Verify each user, device, and application before access is given, even inside the internal network.
2. Enhance Supply Chain Security - Perform targeted assessments on a routine basis to identify risk factors in diminishing suppliers. Include rigorous cybersecurity provisions in the agreements with suppliers, namely disclosure of vulnerabilities and the agreed period for incident response.
3. Durable Backups and Their Restoration - Backward hampers are kept isolated and encrypted to continue operations in case of ransomware incidents or any other occur in system compromise.
4. Periodic Red Team Exercises - Simulate cyber-attacks on IT and OT systems to examine if vulnerabilities exist and evaluate current incident response measures.
5. Employee Training and Insider Threat Monitoring - Social engineering being the forefront of attack vectors, continuous training and behavioural monitoring will have to be done to avoid credential disposal.
6. Public-Private Partnership - Interact with several government agencies and cybersecurity groups for sharing threat intelligence and enforcing best practices complementary to ISO/IEC 27001 and NIST Cybersecurity Framework.

Conclusion

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The hacking at Jaguar Land Rover is perhaps one of a thousand reminders that cybersecurity can no longer be seen as a back-office job but rather as an issue of business continuity at the very core of the organization. In the process of digital transformation, the attack surface grows, making the entities targeted by cybercriminals. Operation security demands that cybersecurity be ensured on a proactive basis through resilient supply chains and stakeholders working together. The JLR attack is not an isolated event; it is a warning for the entire automobile sector to maintain security at every level of digitalization.

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References

• https://www.bbc.com/news/articles/c1jzl1lw4y1o
• https://www.theguardian.com/business/2025/sep/07/disruption-to-jaguar-land-rover-after-cyber-attack-may-last-until-october
• https://uk.finance.yahoo.com/news/jaguar-factory-workers-told-stay-073458122.html

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