#FactCheck -Deepfake Audio Misuses Old Jaishankar Podcast to Fabricate False Claims About “Operation Sindoor”
Executive Summary
A deepfake video is being widely circulated on social media with a false claim that External Affairs Minister S. Jaishankar admitted in a podcast interview that India was surprised by Pakistan’s counter-response during “Operation Sindoor” and suffered some losses. However, a fact-check by CyberPeace Research Wing has found the claim to be fake. The research shows that AI-generated audio has been used to misrepresent the External Affairs Minister’s remarks.
Claim
A Facebook user shared the viral video claiming that in a recent podcast with journalist Smita Prakash, Jaishankar admitted that Pakistan’s aggressive response during Operation Sindoor had caught India off guard.
Fact Check
A review of the original interview on ANI’s YouTube channel shows that the conversation between Smita Prakash and S. Jaishankar was uploaded on May 24, 2024—well before Operation Sindoor.
Operation Sindoor reportedly began on May 7, 2025. In the original video, there is no mention of Operation Sindoor or any Pakistani counter-response, making the viral claim baseless. Further analysis using AI detection tools such as Hive Moderation and Hiya indicated that the audio in the viral clip is likely AI-generated, suggesting manipulation of the original content.
Conclusion
The viral video is fake. AI-generated audio has been used to alter an old interview and falsely attribute statements to External Affairs Minister S. Jaishankar.
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Introduction
India currently faces a crucial moment because its digital system experiences rapid growth while cyber criminals take advantage of this development to increase their fraudulent activities. The Department of Telecommunications (DoT) has implemented a new regulatory requirement that mandates all messaging and communication platforms to use SIM-binding technology as their primary security measure. The new rule, which starts on 1st March 2026, requires WhatsApp, Telegram, Signal and other similar applications to operate only when users have their registered SIM card present in their device. The telecom identifier restriction aims to prevent unauthorised access, but it creates significant privacy concerns, together with issues of proportionality and platform governance.
Understanding the SIM-Binding Directive
SIM-binding establishes direct links between communication platform accounts and the SIM cards used for registration. The application will stop working when users take out their SIM card, turn it off, or get a new SIM card. Users must re-authenticate their sessions through the main device because web-based sessions, including WhatsApp Web, will automatically log out after six hours of use.
The Telecommunications Act, 2023 and Telecom Cyber Security Rules serve as the base legal authority for this directive. The regulation requires Telecommunication Identity User Entities (TIUEs), which identify users through mobile numbers, to maintain service access based on verified telecommunications credentials.
Rationale: Addressing Cyber Fraud and Misuse
The policy exists because cyber fraud activities have reached a point where they require a more powerful response. Authorities have stated that messaging applications maintain their operational capacity even after users remove their SIM cards, which allows international scammers to use Indian phone numbers for their fraudulent activities. SIM-binding aims to:
- Restore traceability by linking active accounts to verified SIM-based identities.
- Reduce remote access abuse, which includes both account takeovers and impersonation scams.
- Stop fraudulent activities that require physical device access through the creation of permanent sessions.
- Build a system of accountability that extends throughout the telecommunications industry.
The government introduced this measure as an appropriate solution to deal with systemic vulnerabilities because reported cyber fraud losses in 2024 reached more than ₹22,800 crore.
Security with Responsibility
The system requires digital trust to be established through secure identity verification systems, which include official systems for verification and operational systems that enable governmental agencies to work together.
CyberPeace principles require security measures to maintain three essential conditions, which are:
- They must respond to existing dangers
- Their execution process must be open to public observation
- They need to protect user rights, which include their right to privacy and personal independence
- They must provide equal access while safeguarding against negative impacts on at-risk user groups.
Industry Response and Governance Challenges
The directive has received diverse responses from people who work in different fields. Some platforms are testing SIM presence verification features for their upcoming changes, according to reports, while industry groups representing major technology companies have raised legal issues. They argue that the mandate may exceed the regulatory scope of the DoT and potentially conflict with constitutional protections. The existing tension demonstrates how governments face difficulties because they must protect national security while managing international platform operations and legal systems. The situation requires multiple stakeholders to work together because governments, industry, and civil society need to design policies through their collective input.
Policy Insights and Recommendations
The successful balanced execution of this initiative depends on these two essential elements:
- Clear Implementation Guidelines: Organizations need to establish detailed technical standards together with compliance frameworks, which must be followed during their implementation process across various platforms.
- Privacy Safeguards: The telecom service provider must implement strong data protection measures that protect customer data from unauthorised access through SIM-binding technology.
- User Awareness and Transparency: Users should receive information about SIM-binding effects on their access rights, together with security controls, which will help them build trust and provide informed consent.
- Flexibility for Edge Cases: Provisions should exist for legitimate use cases such as device changes, international travel, and accessibility needs.
- Global Interoperability Dialogue: India should engage with global stakeholders to ensure that such measures do not fragment the digital ecosystem.
Conclusion
The SIM-binding directive establishes India’s defence against cyber threats by solving a specific problem that exists in digital identity verification. The system establishes CyberPeace as its fundamental base through its shift from reactive cybersecurity practices toward preventive digital governance methods.
The system will achieve its desired results only if it effectively manages the three elements of security protection, privacy maintenance, and user convenience. SIM-binding and similar policies require ongoing assessment because their implementation affects both national security and the fundamental principles of trustworthiness, inclusiveness, and ethical digital governance.
References
- https://www.opindia.com/2026/02/sim-binding-to-be-implemented-from-1st-march-what-it-means-and-how-it-will-impact-users/
- https://www.ndtv.com/india-news/sim-binding-rule-set-to-change-how-whatsapp-telegram-work-in-india-from-march-1-11148903#:~:text=Under%20the%20new%20framework%2C%20messaging,is%20re%2Dinserted%20and%20authenticated.
- https://timesofindia.indiatimes.com/technology/tech-news/telecom-departments-sim-binding-rule-to-come-into-effect-from-tomorrow-march-1-what-is-sim-binding-how-it-works-and-what-it-means-for-whatsapp-users/articleshow/128879561.cms
- https://www.deccanherald.com/technology/whatsapp-begins-testing-sim-binding-in-india-3913963
Artificial intelligence is revolutionizing industries such as healthcare to finance to influence the decisions that touch the lives of millions daily. However, there is a hidden danger associated with this power: unfair results of AI systems, reinforcement of social inequalities, and distrust of technology. One of the main causes of this issue is training data bias, which appears when the examples on which an AI model is trained are not representative or skewed. To deal with it successfully, this needs a combination of statistical methods, algorithmic design that is mindful of fairness, and robust governance over the AI lifecycle. This article discusses the origin of bias, the ways to reduce it, and the unique position of fairness-conscious algorithms.
Why Bias in Training Data Matters
The bias in AI occurs when the models mirror and reproduce the trends of inequality in the training data. When a dataset has a biased representation of a demographic group or includes historical biases, the model will be trained to make decisions in ways that will harm the group. This is a fact that has a practical implication: prejudiced AI may cause discrimination during the recruitment of employees, lending, and evaluation of criminal risks, as well as various other spheres of social life, thus compromising justice and equity. These problems are not only technical in nature but also require moral principles and a system of governance (E&ICTA).
Bias is not uniform. It may be based on the data itself, the algorithm design, or even the lack of diversity among developers. The bias in data occurs when data does not represent the real world. Algorithm bias may arise when design decisions inadvertently put one group at an unfair advantage over another. Both the interpretation of the model and data collection may be affected by human bias. (MDPI)
Statistical Principles for Reducing Training Data Bias
Statistical principles are at the core of bias mitigation and they redefine the data-model interaction. These approaches are focused on data preparation, training process adjustment, and model output corrections in such a way that the notion of fairness becomes a quantifiable goal.
Balancing Data Through Re-Sampling and Re-Weighting
Among the aforementioned methods, a fair representation of all the relevant groups in the dataset is one way. This can be achieved by oversampling underrepresented groups and undersampling overrepresented groups. Oversampling gives greater weight to minority examples, whereas re-weighting gives greater weight to under-represented data points in training. The methods minimize the tendency of models to fit to salient patterns and improve coverage among vulnerable groups. (GeeksforGeeks)
Feature Engineering and Data Transformation
The other statistical technique is to convert data characteristics in such a way that sensitive characteristics have a lesser impact on the results. In one example, fair representation learning adjusts the data representation to discourage bias during the untraining of the model. The disparate impact remover adjust technique performs the adjustment of features of the model in such a way that the impact of sensitive features is reduced during learning. (GeeksforGeeks)
Measuring Fairness With Metrics
Statistical fairness measures are used to measure the effectiveness of a model in groups.
Fairness-Aware Algorithms Explained
Fair algorithms do not simply detect bias. They incorporate fairness goals in model construction and run in three phases including pre-processing, in-processing, and post-processing.
Pre-Processing Techniques
Fairness-aware pre-processing deals with bias prior to the model consuming the information. This involves the following ways:
- Rebalancing training data through sampling and re-weighting training data to address sample imbalances.
- Data augmentation to generate examples of underrepresented groups.
- Feature transformation removes or downplays the impact of sensitive attributes prior to the commencement of training. (IJMRSET)
These methods can be used to guarantee that the model is trained on more balanced data and to reduce the chances of bias transfer between historical data.
In-Processing Techniques
The in-processing techniques alter the learning algorithm. These include:
- Fairness constraints that penalize the model for making biased predictions during training.
- Adversarial debiasing, where a second model is used to ensure that sensitive attributes are not predicted by the learned representations.
- Fair representation learning that modifies internal model representations in favor of
Post-Processing Techniques
Fairness may be enhanced after training by changing the model outputs. These strategies comprise:
- Threshold adjustments to various groups to meet conditions of fairness, like equalized odds.
- Calibration techniques such that the estimated probabilities are fair indicators of the actual probabilities in groups. (GeeksforGeeks)
Challenges
Mitigating bias is complex. The statistical bias minimization may at times come at the cost of the model accuracy, and there is a conflict between predictive performance and fairness. The definition of fairness itself is potentially a difficult task because various applications of fairness require various criteria, and various criteria can be conflicting. (MDPI)
Gaining varied and representative data is also a challenge that is experienced because of privacy issues, incomplete records, and a lack of resources. The auditing and reporting done on a continuous basis are needed so that mitigation processes are up to date, as models are continually updated. (E&ICTA)
Why Fairness-Aware Development Matters
The outcomes of the unfair treatment of some groups by AI systems are far-reaching. Discriminatory software in recruitment may support inequality in the workplace. Subjective credit rating may deprive deserving people of opportunities. Unbiased medical forecasts might result in the flawed allocation of medical resources. In both cases, prejudice contravenes the credibility and clouds the greater prospect of AI. (E&ICTA)
Algorithms that are fair and statistical mitigation plans provide a way to create not only powerful AI but also fair and trustworthy AI. They admit that the results of AI systems are social tools whose effects extend across society. Responsible development will necessitate sustained fairness quantification, model adjustment, and upholding human control.
Conclusion
AI bias is not a technical malfunction. It is a mirror of real-world disparities in data and exaggerated by models. Statistical rigor, wise algorithm design, and readiness to address the trade-offs between fairness and performance are required to reduce training data bias. Fairness-conscious algorithms (which can be implemented in pre-processing, in-processing, or post-processing) are useful in delivering more fair results. As AI is taking part in the most crucial decisions, it is necessary to consider fairness at the beginning to have a system that serves the population in a responsible and fair manner.
References
- Understanding Bias in Artificial Intelligence: Challenges, Impacts, and Mitigation Strategies: E&ICTA, IITK
- Bias and Fairness in Artificial Intelligence: Methods and Mitigation Strategies: JRPS Shodh Sagar
- Fairness and Bias in Artificial Intelligence: A Brief Survey of Sources, Impacts, and Mitigation Strategies: MDPI
- Ensuring Fairness in Machine Learning Algorithms: GeeksforGeeks
Bias and Fairness in Machine Learning Models: A Critical Examination of Ethical Implications: IJMRSET - Bias in AI Models: Origins, Impact, and Mitigation Strategies: Preprints
- Bias in Artificial Intelligence and Mitigation Strategies: TCS
- Survey on Machine Learning Biases and Mitigation Techniques: MDPI
Executive Summary
A post claiming that former Indian cricketer Sachin Tendulkar praised Congress leader Rahul Gandhi and urged people to elect him as Prime Minister is being widely circulated on social media.The viral poster falsely attributes a political statement to Sachin Tendulkar, suggesting that he has endorsed Rahul Gandhi for the post of Prime Minister. However, CyberPeace Research Wing research found the claim to be fake. Sachin Tendulkar has not made any such appeal or statement supporting Rahul Gandhi for Prime Minister.
Claim
On X (formerly Twitter), a verified user “Queen” shared a viral poster claiming:“Sachin Tendulkar has always supported education and never promoted superstition. Rahul Gandhi always predicts what Narendra Modi will do next. It is time to choose Rahul Gandhi again.”
Fact Check
To verify the claim, we first searched for any news reports, interviews, or credible references linking Sachin Tendulkar to such a political statement. However, we found no evidence in any reliable media source or public record suggesting that he made any such remark about Rahul Gandhi or the Prime Ministership. We also reviewed Sachin Tendulkar’s official social media accounts, but found no post, video, or statement endorsing any political leader in this manner.
Finally, the viral poster was analysed using the AI detection tool Hive Moderation. The analysis indicated a 96.8% probability that the poster was digitally created or manipulated, suggesting possible AI-generated or edited content.
Conclusion
CyberPeace Research Wing research found the claim to be fake. Sachin Tendulkar has not made any appeal to elect Rahul Gandhi as Prime Minister. The viral poster appears to be digitally fabricated and is being shared to spread misinformation.
