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12:43 pm February 26, 2025 By Julian Horsey

A recent study conducted by Surfshark has revealed that nearly one-third of popular AI chatbot applications share user data with third parties. This finding has raised significant concerns about privacy and data security, particularly as AI-driven technologies become increasingly integrated into daily life. The study underscores the urgent need for greater transparency in how these applications handle personal information and highlights the importance of user awareness in mitigating potential risks.
TL;DR Key Takeaways :
AI chatbots are widely used for tasks such as customer support, virtual assistance, and personalized recommendations. To perform these functions effectively, they collect substantial amounts of user data. According to the Surfshark study, these applications gather an average of 11 out of 35 possible data types. This includes sensitive information such as contact details, browsing history, and user-generated content. Notably, 40% of the analyzed apps also collect geolocation data, which can reveal users’ precise movements and behavioral patterns.
One of the most data-intensive applications identified in the study is Google Gemini, which collects 22 types of data. This includes precise location, browsing history, and contact information. The extensive nature of this data collection raises questions about its necessity and the potential risks associated with storing such detailed information. While some data collection may be essential for functionality, the sheer volume of data gathered by certain applications has sparked concerns about user privacy and security.
The study also highlights that 30% of AI chatbot applications share user data with third parties. This data is often shared for purposes such as targeted advertising or sale to data brokers. Applications like Copilot, Poe, and Jasper explicitly collect data for tracking, allowing advertisers to deliver highly personalized ads based on user behavior. While this practice may enhance user experience by tailoring content to individual preferences, it also increases the risk of data misuse.
A significant issue is the lack of transparency surrounding these practices. Many users remain unaware of how their data is being shared or who has access to it. This lack of clarity leaves users vulnerable to exploitation and underscores the need for developers to communicate more openly about how user information is handled. Without clear disclosures, users may unknowingly consent to data-sharing practices that compromise their privacy.
The risks associated with extensive data collection and sharing are further amplified by the potential for data breaches. One notable example is DeepSeek, an AI chatbot application that stores user data, including chat histories, on servers located in China. The platform suffered a significant breach, exposing over one million records. These records included sensitive chat content and API keys, creating opportunities for malicious actors to exploit the leaked data for phishing, spam, or financial fraud.
The more data an application collects and shares, the greater the likelihood of a breach. This reality highlights the importance of implementing robust cybersecurity measures and adhering to stringent data protection policies. Without these safeguards, both users and organizations face heightened risks of data exploitation.
The global nature of AI chatbot applications presents challenges for regulatory oversight. Many of these applications store data on servers located in countries with varying privacy laws, such as China or the United States. This raises questions about accountability and compliance with international standards. For instance, data stored in jurisdictions with weaker privacy protections may be more vulnerable to misuse or unauthorized access.
Although governments and regulatory bodies are increasingly scrutinizing data practices in AI technologies, the rapid pace of AI development often outstrips the creation and enforcement of regulations. This regulatory lag leaves critical gaps in user protection and accountability. Without clear and enforceable standards, users are left to navigate privacy risks largely on their own, often without sufficient knowledge or resources to do so effectively.
Given the privacy risks associated with AI chatbots, users can take several proactive measures to safeguard their information. These steps include:
By adopting these practices, users can take an active role in protecting their privacy and reducing the risks associated with AI chatbot usage.
The findings of the Surfshark study highlight the widespread data collection and sharing practices of AI chatbot applications. With 30% of these apps sharing user data with third parties and the ever-present risk of data breaches, the need for greater transparency and user vigilance is clear. Users must take proactive steps to understand how their data is handled and adopt measures to safeguard their information.
At the same time, regulatory bodies and developers must prioritize the establishment and enforcement of standards that protect user privacy. As AI technologies continue to evolve, striking a balance between innovation and robust data protection will be essential. Building trust in AI systems requires not only technological advancements but also a commitment to ethical data practices and user security.
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