Artificial intelligence lives and breathes data.Every interaction with an AI assistant, chatbot, or browser extension feeds models that learn from our behavior. This "hunger" for information makes these tools so useful for summarizing texts, answering emails, or helping at work, but it also opens the door to serious risks to privacy, security, and even commercial or geopolitical manipulation.
Today, AI assistants have access to corners of our digital lives that were previously off the radar.: browsing historyForms containing banking or health information, private messaging conversations, work documents, photos, and even mobile contact lists are all being collected. While major tech companies are looking for ways to continue training powerful models despite data restrictions, users navigate a complex landscape where consent is often unclear, with lengthy, ambiguous, and constantly changing privacy policies.
Why AI assistants need so much data and what it means for your privacy
AI agents and chatbots are not just “programs that answer”These are systems that make decisions, make recommendations, prioritize content, and increasingly act on our behalf (buying, booking, managing schedules, answering emails, etc.). To be effective, they need a very rich context about who you are, what you do, and what interests you, which drives them to collect personal data on a massive scale.
Recent research has shown that many AI-powered browser assistants access extremely sensitive information.In tests conducted with popular extensionsThe researchers found that some plugins sent the full content of visited pages to their servers, including forms with bank details, health information or any data visible on the screen, in addition to the IP address and metadata that allows inferences about age, sex, income level or interests.
The risk is not just that they know a lot about you, but what they can do with that information.Cybersecurity experts warn of possible scenarios of commercial manipulation (biased recommendations based on your profile), discrimination or exclusion (for example, limiting offers or services to certain groups), extortion if the data ends up being leaked, and even identity theft if different sources of information are combined.
The big underlying problem is opacity.Many users are aware that search engines and social networks collect data for advertising, but they don't imagine that a browser-integrated assistant could monitor a significant portion of their online activity. In many cases, this data collection occurs without truly informed consent and, sometimes, skirts the edges of data protection regulations or directly contradicts the company's own terms of service, which many They accept without reading.
What data do AI assistants collect, and what are the differences between platforms?
The data collected by AI assistants ranges from basic information to intimate details.The most common types of data include contact information (name, email, phone number), precise or approximate location, device identifiers, usage history, conversation content, uploaded files, purchase data, and, in some cases, the user's contact list.
Comparative studies place some attendees as particularly "gluttonous"In the field of conversational chatbots, it has been noted that certain solutions can collect more than twenty different types of data per user, spread across numerous categories: contact, location, content you write or upload, activity history, unique identifiers, diagnostic information, usage patterns, purchases made, and even the phone's contact list, something that almost no other chatbot does today.
At the opposite end of the spectrum are more reserved attendees.These only record a handful of basic attributes related to communication and the technical operation of the service (such as identifiers or diagnostic data). In between are a wide variety of tools such as Claude, Copilot, DeepSeek, ChatGPT, and others. Perplexitywhich differ in how many types of data they collect, what categories they cover, and what they use them for, especially regarding advertising or sharing with third parties.
Not all assistants behave the same when integrated into the browser. In extensions for Chrome, Edge or other browsers Particularly intrusive practices have been detected: plugins that capture banking and health forms, others that send user questions along with identifiers to analytics services such as Google Analytics, allowing cross-site tracking, and several that build detailed profiles (age, gender, income, hobbies) to personalize responses across different sessions.
One striking case is that of certain tools that, according to the analyses, show hardly any signs of profiling or personalization. Unlike other extensions that massively track browsing activity, these extensions are precisely why some researchers highlight them as an example of how it's technically possible to provide a useful AI service without fully exploiting the user's personal data.
Meta AI and the leap to the massive exploitation of sensitive data
By integrating its assistant into Facebook, Instagram, and other services, Meta is crossing a new line in the use of personal data.The business model is no longer focused solely on capturing attention to display ads, but rather on exploiting direct interactions with the user, in very specific contexts and loaded with intimate information.
An analysis of data collection practices from various chatbots shows that Meta AI is by far one of the most voracious.All the apps reviewed collected user data; almost half tracked geolocation, and nearly a third practiced advertising tracking, cross-referencing information with other services or selling it to data intermediaries. But Meta AI stands out because, according to the study, it is the only one that explicitly collects financial, health, and fitness information.
But it doesn't stop there: Meta AI also collects particularly sensitive categoriesThis includes data such as racial or ethnic origin, sexual orientation, pregnancy or childbirth data, disability, religious or philosophical beliefs, trade union membership, political opinions, as well as genetic and biometric information. This type of data is especially protected under regulations such as the GDPR, because its misuse can lead to discrimination, persecution, or serious risks to fundamental rights.
Additionally, Meta AI shares certain information with third parties in the context of targeted advertisingAlong with Copilot, it is among the few assistants that use data related to user identity for commercial campaigns, but it clearly differs in the volume and diversity of the information involved, with more than twenty types of data used compared to much lower figures in other services.
The approach is reinforced by an ecosystem of data brokers. that buy and sell personal information compiled from apps, websites, and public databases. Companies like Acxiom, Experian, Epsilon, and Oracle Data Cloud handle enormous volumes of profiles, which can end up in the hands of advertisers, insurers, employers, and even government agencies, in a global market that remains largely unregulated despite some legislative progress.
Google Gemini, policy changes and activity control
Google has also made a move by updating its privacy policies to incorporate the use of interactions with its AI ecosystem.Through Gemini, the company indicates that it can use the queries, uploaded files, screenshots, and photos you share to improve its services and train generative AI models, including audio and recordings from features like Gemini Live.
In response to the criticism, Google has introduced a feature called “Temporary Conversation”This feature is designed to limit the use of your recent searches for personalization or training purposes. However, the user must activate it and be proactive in configuring options such as disabling activity retention or managing and deleting history items; otherwise, a significant portion of their digital life will remain accessible to the company.
The company acknowledges that when it uses user activity to improve its services, it also uses human reviewers.To this end, it claims to unlink conversations from the account before sending them to service providers. Even so, it expressly admits that "as before" it has been using this personal data and sharing it with third parties for certain tasks, which raises doubts about the true extent of anonymity and the effective protection of information.
This approach raises uncomfortable questions about consentMany users accept new privacy terms almost automatically, without reading them, out of sheer inertia or fear of losing access to the service. In doing so, they grant broad permissions to use personal data without fully understanding the implications, which some experts consider "suspicious" when the clauses are expanded precisely to cover the training of AI models.
In the regulatory sphere, all of this intersects with compliance requirements such as the European GDPR.These regulations require justifying the legal basis for processing (consent, legitimate interest, legal obligation, etc.) and guaranteeing rights of access, rectification, objection, and erasure. The debate on whether it is acceptable to invoke “legitimate interest” to train AI systems with personal data without explicit consent is very much alive among data protection authorities and consumer associations.
Private messaging, file storage, and where your data actually ends up
Messaging apps are one of the most sensitive digital environmentsBecause they contain intimate conversations, photos, documents, and all sorts of confidential information. The idea that an AI assistant could snoop through these messages without explicit consent raises serious privacy concerns and undermines the trust of many users.
In the case of WhatsApp, the company insists that personal chats with friends and family are inaccessible to AI.They explain that their models are trained through direct interactions with the specific AI account: you have to actively open a chat with the AI or send it a message, and neither Meta nor WhatsApp can initiate that conversation for you. They also emphasize that interacting with the AI does not automatically link your WhatsApp account to Facebook, Instagram, or other apps in the group.
Even so, the company itself warns that what you send to that AI can be used to provide you with accurate answers.and explicitly recommends do not share information that you don't want Meta to know. This makes it clear that, even though formal barriers exist between services, any content you decide to introduce into an AI chat enters the processing circuit and is potentially analyzed for training purposes.
File storage and transfer services have also been embroiled in controversy.A recent example was the change in the terms of service of a well-known file transfer service, whose new clauses were interpreted as granting broad permission to use uploaded documents to improve future AI systems. The user backlash forced the company to clarify that the content remained the property of the sender, that its use was limited to the operation and improvement of the service, and that it would not be used to train AI models or sold to third parties. It was also important to remember that there are alternatives for managing your files and photos locally, such as [insert alternatives here]. Photoprism.
These kinds of incidents show the extent to which trust depends on transparency.If legal texts are ambiguous or give the impression of opening the door to unforeseen uses, the user assumes the worst. Furthermore, when legitimate purposes (security, performance, maintenance) are mixed with generic concepts like "improving our services," it becomes difficult to know whether your documents end up as mere technical traffic or as part of a gigantic training dataset.
The situation becomes more complicated when high-risk suppliers and sensitive storage locations come into play.In the case of some assistants developed outside the EU, significant leaks of conversations and logs have been documented, and the fact that the servers are hosted in jurisdictions with laxer data protection regulations increases user exposure. Here, it's not just how much data is collected that matters, but where it's stored and under what laws that processing is governed.
Cybersecurity risks, ubiquitous data, and the need for regulation
The combination of advanced AI and large volumes of personal data is a goldmine for cybercriminals.Attackers are increasingly using AI tools to refine social engineering campaigns, generate credible phishing emails, profile victims, and automate identity theft or financial fraud.
If an AI assistant stores conversation histories, documents, and sensitive data without sufficient security measuresA single breach can expose information on a massive scale. Unlike a password that you can change, data such as your medical history, political opinions, or sexual orientation, once leaked, are virtually impossible to "revoke."
Reports on cybersecurity resilience indicate that most organizations are not prepared. to protect AI-powered systems and processes. Many lack basic security and data governance practices, haven't defined clear policies on what can and cannot be introduced into AI tools, and haven't adapted their cloud infrastructures to manage this new type of risk. Furthermore, some threats originate from seemingly legitimate services; therefore, it's important to monitor providers and applications, including certain VPNs that steal datawhich can increase exposure.
Given this scenario, experts are calling for stricter and more specific regulations for AI.There is talk of strengthened transparency obligations (making it very clear what data is collected and for what purpose), explicit consent for sensitive uses, minimum security standards for smart devices and services, and additional restrictions for providers considered high-risk. European regulations on AI and data protection are attempting to move in this direction, but their practical application is still under development.
The importance of integrating privacy and security "by design" is also emphasized.Instead of viewing regulation as a hindrance, some specialists argue that including digital footprint protection from the start of each project makes solutions more robust and efficient in the long term, and avoids situations where patches have to be improvised after a privacy incident or a regulatory sanction.
Synthetic data, AI self-improvement, and the future of model training
Digital background depicting innovative technologies in security systems, data protection Internet technologies
The reliance on real-world data creates a bottleneck for the development of increasingly powerful models.Big tech companies know they can't indefinitely base their progress on exploiting personal information without limits, both for ethical reasons and due to increasing legal restrictions. That's why they're exploring alternative ways to train AI without relying so heavily on user data.
One of those paths is AI “self-improvement”.Systems capable of optimizing their own performance through improved algorithms, self-programming processes, and more efficient hardware (especially in the area of processors). Laboratories at companies like Meta and Google DeepMind are working on models that, in part, train or refine themselves, reducing the need for new, labeled human data.
Another key avenue is the generation of synthetic dataInstead of simply learning from what already exists, a model can create new experiences or examples based on what it has learned, and then use them to continue training. In this way, the system is no longer limited by the scarcity of real data and can produce almost unlimited amounts of simulated information to improve its performance on specific tasks.
Practical applications of this approach are already beginning to emerge.Specialized programming assistance tools, such as code assistants, demonstrate how a model can learn from its own output, correct errors, refine styles, and propose increasingly sophisticated solutions without requiring manual review of each human instance. Startups are experimenting with agents that modify their own code to better adapt to the tasks they face, creating a continuous cycle of testing and improvement.
However, this “autonomy” in training is not without risks.Organizations dedicated to AI risk assessment warn that if a system is able to rapidly amplify its own capabilities, it could be repurposed for malicious activities, from advanced hacking to weapons design or the mass manipulation of people through custom-generated content.
The reality is that, while companies explore synthetic data and self-improvement, current systems still rely heavily on real-world information.More than 80% of the organizations analyzed in some studies still lack mature practices for securing their AI models, protecting data traffic, or safeguarding their cloud infrastructure. The gap between the speed of AI adoption and defense capabilities translates into an ever-expanding attack surface.
Best practices for users: what not to share and how to protect yourself when using AI
The user's main defense remains common sense applied rigorously.However friendly a chatbot may seem, it's not your trusted colleague: it's an interface to servers that can record, analyze, and reuse what you say. Always assume that anything you enter could be stored longer than you imagine.
Avoid sharing personally identifiable information (PII) unless absolutely necessaryThis includes full name, postal address, personal email, phone number, date of birth, or identity documents. When several of these details are combined, it becomes much easier to profile you and link your conversations to a real identity.
Never enter financial information or security credentialsCredit card numbers, bank account details, passwords, PIN codes, or two-factor authentication codes should never be shared with an AI chatbot. Only manage this type of data on official platforms with end-to-end encryption and specific security measures.
Keep social security numbers, passport details, and other high-risk identifiers out of chatbotsThey are a favorite target for identity theft, and unlike a credit card, they are not easy to replace if compromised. Consider these credentials as "radioactive material" that should not be entered into systems whose inner workings you do not understand.
In academic, institutional, or corporate settings, be especially careful about what you share.Do not upload academic transcripts, databases with protected information, internal reports, strategic plans, sensitive financial documents, or unpublished intellectual property. Many organizations expressly prohibit sending this type of content to public AI services, and you could violate both internal rules and data protection laws.
Adopt a data minimization and anonymization strategy in your promptsProvide only the information necessary for the assistant to help you: remove names, addresses, and specific references to real people or projects, and replace identifiable elements with generic markers such as "Client A," "Company X," or "City Y." Review your messages before sending them to ensure nothing sensitive has been included.
Periodically review the privacy policy and control options of each service.Some platforms allow you to disable the use of your conversations for training purposes, delete old chat histories, or activate temporary chat modes that reduce retention time. Activate these features whenever possible and don't assume they are enabled by default.
From a technical point of view, it strengthens your basic securityUse strong, unique passwords for each AI account, enable two-step authentication whenever possible, avoid connecting from unsecured public Wi-Fi networks, and keep your devices updated. Adhere to your organization's policies regarding the use of AI tools, especially if you work with sensitive information.
Finally, it demands transparency and accountability from AI providers.The tools should clearly state that they are automated systems, explaining in an understandable way what data they collect and for what purposes, who can access that information, and how to exercise your rights of access, rectification, erasure, or objection. Ethical design also includes measures to mitigate bias, avoid dangerous recommendations, and appropriately escalate high-risk cases.
In an ecosystem where AI is increasingly influencing our daily lives, understanding what data assistants collect, how they are used, and what control options we have is key to continuing to reap their benefits without surrendering our privacy.With a combination of stricter regulations, responsible companies, and well-informed users, it is possible to benefit from these systems without losing control over the information that best defines us.
