#FactCheck - Old Rajnath Singh Video Falsely Linked To NEET-UG 2026 Paper Leak Controversy

Introduction

Misinformation poses a significant challenge to public health policymaking since it undermines efforts to promote effective health interventions and protect public well-being. The spread of inaccurate information, particularly through online channels such as social media and internet platforms, further complicates the decision-making process for policymakers since it perpetuates public confusion and distrust. This misinformation can lead to resistance against health initiatives, such as vaccination programs, and fuels scepticism towards scientifically-backed health guidelines.

Before the COVID-19 pandemic, misinformation surrounding healthcare largely encompassed the effects of alcohol and tobacco consumption, marijuana use, eating habits, physical exercise etc. However, there has been a marked shift in the years since. One such example is the outcry against palm oil in 2024: it is an ingredient prevalent in numerous food and cosmetic products, and came under the scanner after a number of claims that palmitic acid, which is present in palm oil, is detrimental to our health. However, scientific research by reputable institutions globally established that there is no cause for concern regarding the health risks posed by palmitic acid. Such trends and commentaries tend to create a parallel unscientific discourse that has the potential to not only impact individual choices but also public opinion and as a result, market developments and policy conversations. 

A prevailing narrative during the worst of the Covid-19 pandemic was that the virus had been engineered to control society and boost hospital profits. The extensive misinformation surrounding COVID-19 and its management and care increased vaccine hesitancy amongst people worldwide. It is worth noting that vaccine hesitancy has been a consistent trend historically; the World Health Organisation flagged vaccine hesitancy as one of the main threats to global health, and there have been other instances where a majority of the population refused to get vaccinated anticipating unverified, long-lasting side effects. For example, research from 2016 observed a significant level of public skepticism regarding the development and approval process of the Zika vaccine in Africa. Further studies emphasised the urgent need to disseminate accurate information about the Zika virus on online platforms to help curb the spread of the pandemic. 

In India during the COVID-19 pandemic, despite multiple official advisories, notifications and guidelines issued by the government and ICMR, people continued to remain opposed to vaccination, which resulted in inflated mortality rates within the country. Vaccination hesitancy was also compounded by anti-vaccination celebrities who claimed that vaccines were dangerous and contributed in large part to the conspiracy theories doing the rounds. Similar hesitation was noted in misinformation surrounding the MMR vaccines and their likely role in causing autism was examined.  At the time of the crisis, the Indian government also had to tackle disinformation-induced fraud surrounding the supply of oxygens in hospitals. Many critically-ill patients relied on fake news and unverified sources that falsely portrayed the availability of beds, oxygen cylinders and even home set-ups, only to be cheated out of money. 

The above examples highlight the difficulty health officials face in administering adequate healthcare. The special case of the COVID-19 pandemic also highlighted how current legal frameworks failed to address misinformation and disinformation, which impedes effective policymaking. It also highlights how taking corrective measures against health-related misinformation becomes difficult since such corrective action creates an uncomfortable gap in an individual’s mind, and it is seen that people ignore accurate information that may help bridge the gap. Misinformation, coupled with the infodemic trend, also leads to false memory syndrome, whereby people fail to differentiate between authentic information and fake narratives. Simple efforts to correct misperceptions usually backfire and even strengthen initial beliefs, especially in the context of complex issues like healthcare. Policymakers thus struggle with balancing policy making and making people receptive to said policies in the backdrop of their tendencies to reject/suspect authoritative action. Examples of the same can be observed on both the domestic front and internationally. In the US, for example, the traditional healthcare system rations access to healthcare through a combination of insurance costs and options versus out-of-pocket essential expenses. While this has been a subject of debate for a long time, it hadn’t created a large scale public healthcare crisis because the incentives offered to the medical professionals and public trust in the delivery of essential services helped balance the conversation. In recent times, however, there has been a narrative shift that sensationalises the system as an issue of deliberate “denial of care,” which has led to concerns about harms to patients.

Policy Recommendations

The hindrances posed by misinformation in policymaking are further exacerbated against the backdrop of policymakers relying on social media as a method to measure public sentiment, consensus and opinions. If misinformation about an outbreak is not effectively addressed, it could hinder individuals from adopting necessary protective measures and potentially worsen the spread of the epidemic. To improve healthcare policymaking amidst the challenges posed by health misinformation, policymakers must take a multifaceted approach. This includes convening a broad coalition of central, state, local, territorial, tribal, private, nonprofit, and research partners to assess the impact of misinformation and develop effective preventive measures. Intergovernmental collaborations such as the Ministry of Health and the Ministry of Electronics and Information Technology should be encouraged whereby doctors debunk online medical misinformation, in the backdrop of the increased reliance on online forums for medical advice. Furthermore, increasing investment in research dedicated to understanding misinformation, along with the ongoing modernization of public health communications, is essential. Enhancing the resources and technical support available to state and local public health agencies will also enable them to better address public queries and concerns, as well as counteract misinformation. Additionally, expanding efforts to build long-term resilience against misinformation through comprehensive educational programs is crucial for fostering a well-informed public capable of critically evaluating health information.

From an individual perspective, since almost half a billion people use WhatsApp it has become a platform where false health claims can spread rapidly. This has led to a rise in the use of fake health news. Viral WhatsApp messages containing fake health warnings can be dangerous, hence it is always recommended to check such messages with vigilance. This highlights the growing concern about the potential dangers of misinformation and the need for more accurate information on medical matters.

Conclusion

The proliferation of misinformation in healthcare poses significant challenges to effective policymaking and public health management. The COVID-19 pandemic has underscored the role of misinformation in vaccine hesitancy, fraud, and increased mortality rates. There is an urgent need for robust strategies to counteract false information and build public trust in health interventions; this includes policymakers engaging in comprehensive efforts, including intergovernmental collaboration, enhanced research, and public health communication modernization, to combat misinformation. By fostering a well-informed public through education and vigilance, we can mitigate the impact of misinformation and promote healthier communities.

References

1. van der Meer, T. G. L. A., & Jin, Y. (2019), “Seeking Formula for Misinformation Treatment in Public Health Crises: The Effects of Corrective Information Type and Source” Health Communication, 35(5), 560–575. https://doi.org/10.1080/10410236.2019.1573295 
2. “Health Misinformation”, U.S. Department of Health and Human Services. https://www.hhs.gov/surgeongeneral/priorities/health-misinformation/index.html 
3. Mechanic, David, “The Managed Care Backlash: Perceptions and Rhetoric in Health Care Policy and the Potential for Health Care Reform”, Rutgers University. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2751184/pdf/milq_195.pdf 
4. “Bad actors are weaponising health misinformation in India”, Financial Express, April 2024.

https://www.financialexpress.com/life/entertainment-bad-actors-are-weaponising-health-misinformation-in-india-3448871/ 

5. “Role of doctors in eradicating misinformation in the medical sector.”, Times of India, 1 July 2024. https://timesofindia.indiatimes.com/life-style/health-fitness/health-news/national-doctors-day-role-of-doctors-in-eradicating-misinformation-in-the-healthcare-sector/articleshow/111399098.cms 

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Introduction

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Artificial Intelligence (AI) has transcended its role as a futuristic tool; it is already an integral part of the decision-making process in various sectors, including governance, the medical field, education, security, and the economy, worldwide. On the one hand, there are concerns about the nature of AI, its advantages and disadvantages, and the risks it may pose to the world. There are also doubts about the technology’s capacity to provide effective solutions, especially when threats such as misinformation, cybercrime, and deepfakes are becoming more common.

Recently, global leaders have reiterated that the use of AI should continue to be human-centric, transparent, and governed responsibly. The issue of offering unbridled access to innovators, while also preventing harm, is a dilemma that must be resolved.

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AI as a Global Public Good

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In earlier times only the most influential states and large corporations controlled the supply and use of advanced technologies, and they guarded them as national strategic assets. In contrast, AI has emerged as a digital innovation that exists and evolves within a deeply interconnected environment, which makes access far more distributed than before. Usage of AI in a specific country will not only bring its pros and cons to that particular place, but the rest of the world as well. For instance, deepfake scams and biased algorithms will not only affect the people in the country where they are created but also in all other countries where such people might be doing business or communicating. 

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The Growing Threat of AI Misuse

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• Deepfakes, Crime, and Digital Terrorism
  The application of artificial intelligence in the wrong way is quickly becoming one of the main security problems. Deepfake technology is being used to carry out electoral misinformation spread, communicate lies, and create false narratives. Cybercriminals are now making use of AI to make phishing attacks faster and more efficient, hack into security systems, and come up with elaborate social engineering tactics. In the case of extremist groups, AI has the power to give a better quality of propaganda, recruitment, and coordination.

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• Solution - Human Oversight and Safety-by-Design
  To overcome these dangers, a global AI system must be developed based on the principles of safety-by-design. This means incorporating moral safeguards right from the development phase rather than reacting after the damage is done. Moreover, human control is just as vital. Artificial intelligence (AI) systems that influence public confidence, security, or human rights should always be under the control of human decision-makers. Automated decision-making where there is no openness or the possibility of auditing could lead to black-box systems being developed, where the assignment of responsibility is unclear.

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Three Pillars of a Responsible AI Framework 

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1. Equitable Access to AI Technologies
   One of the major hindrances to global AI development is the non-uniformity of access. The provision of high-end computing capability, data infrastructure, and AI research resources is still highly localised in some areas. A sustainable framework needs to be set up so that smaller countries, rural areas, and people speaking different languages will also be able to share the benefits of AI. The distribution of access fairly will be a gradual process, but at the same time, it will lead to the creation of new ideas and improvements in the different places where the local markets are. Thus, there would be no digital divide, and the AI future would not be exclusively determined by the wealthy economies.
2. Population-Level Skilling and Talent Readiness
   AI will have an impact on worldwide working areas. Thus, societies must not only equip their people with the existing job skills but also with the future technology-based skills. Massive AI literacy programs, digital competencies enhancement, and cross-disciplinary education are very important. Forecasting human resources for roles in AI governance, data ethics, cyber security, and modern technologies will help prevent large scale displacement while also promoting growth that is genuinely inclusive.
3. Responsible and Human-Centric Deployment
   Adoption of Responsible AI makes sure that technology is used for social good and not just for making profits. The human-centred AI directs its applications to the sectors like healthcare, agriculture, education, disaster management, and public services, especially the underserved regions in the world that are most in need of these innovations. This strategy guarantees that progress in technology will improve human life instead of making the situation worse for the poor or taking away the responsibility from humans.

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Need for a Global AI Governance Framework

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• Why International Cooperation Matters
  AI governance cannot be fragmented. Different national regulations lead to the creation of loopholes that allow bad actors to operate in different countries. Hence, global coordination and harmonisation of safety frameworks is of utmost importance. A single AI governance framework should stipulate: 

• Clear responsible prohibition on AI misuse in terrorism, deepfakes, and cybercrime .
• Transparency and algorithm audits as a compulsory requirement.
• Independent global oversight bodies.
• Ethical codes of conduct in harmony with humanitarian laws.

Framework like this makes it clear that AI will be shaped by common values rather than being subject to the influence of different interest groups.

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• Talent Mobility and Open Innovation
  If AI is to be universally accepted, then global mobility of talent must be made easier. The flow of innovation takes place when the interaction between researchers, engineers, and policymakers is not limited by borders. 

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• AI, Equity, and Global Development
  The rapid concentration of technology in a few hands poses the risk of widening the gap in equality among countries. Most developing countries are facing the problems of poor infrastructure, lack of education and digital resources. By regarding them only as technology markets and not as partners in innovation, they become even more isolated from the mainstream of development. An AI development mix of human-centred and technology-driven must consider that the global stillness is broken only by the inclusion of the participation of the whole world. For example, the COVID-19 pandemic has already demonstrated how technology can be a major factor in the building of healthcare and crisis resilience. As a matter of fact, when fairly used, AI has a significant role to play in the realisation of the Sustainable Development Goals.

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Conclusion

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AI is located at a crucial junction. It can either enhance human progress or increase the digital risks. Making sure that AI is a global good goes beyond mere sophisticated technology; it requires moral leadership, inclusion in governance, and collaboration between countries. Preventing misuse by means of openness, supervision by humans, and policies that are responsible will be vital in keeping public trust. Properly guided, AI can make society more resilient, speed up development, and empower future generations. The future we choose is determined by how responsibly we act today.

As PM Modi stated ‘AI should serve as a global good, and at the same time nations must stay vigilant against its misuse’. CyberPeace reinforces this vision by advocating responsible innovation and a secure digital future for all.

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References

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• https://www.hindustantimes.com/india-news/ai-a-global-good-but-must-guard-against-misuse-pm-101763922179359.html
• https://www.deccanherald.com/india/g20-summit-pm-modi-goes-against-donald-trumps-stand-seeks-global-governance-for-ai-3807928
• https://timesofindia.indiatimes.com/india/need-global-compact-to-prevent-ai-misuse-pm-modi/articleshow/125525379.cms

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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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