What data do AI assistants collect and how to protect your privacy

Artificial intelligence has gone from promise to routine in record time, and with it, very specific doubts have arisen: What data do AI assistants collect?How they use them and what we can do to keep our information safe. If you use chatbots, browser assistants, or generative models, it's a good idea to take control of your privacy as soon as possible.

Besides being tremendously useful tools, these systems feed on large-scale data. The volume, origin, and treatment of that information They introduce new risks: from inferring personal traits to the accidental exposure of sensitive content. Here you will find, in detail and without beating around the bush, what they capture, why they do it, what the law says, and How to protect your accounts and your activity. Let's learn all about What data do AI assistants collect and how to protect your privacy. 

What data do AI assistants actually collect?

Modern assistants process much more than just your questions. Contact information, identifiers, usage and content These are usually included in the standard categories. We're talking about name and email, but also IP addresses, device information, interaction logs, errors, and, of course, the content you generate or upload (messages, files, images, or public links).

Within the Google ecosystem, Gemini's privacy notice accurately describes what it collects information from connected applications (for example, Search or YouTube history, Chrome context), device and browser data (type, settings, identifiers), performance and debugging metrics, and even system permissions on mobile devices (such as access to contacts, call logs and messages or on-screen content) when authorized by the user.

They also deal location data (approximate device location, IP address, or addresses saved in the account) and subscription details if you use paid plans. Additionally, the following are stored: own content that the models generate (text, code, audio, images or summaries), something key to understanding the footprint you leave when interacting with these tools.

It should be noted that the data collection is not limited to training: Attendees can record activity in real time During use (for example, when you rely on extensions or plugins), this includes telemetry and application events. This explains why controlling permissions and reviewing activity settings is crucial.

What do they use that data for and who can see it?

Companies often invoke broad and recurring purposes: To provide, maintain and improve the service, personalize the experience, and develop new featuresto communicate with you, measure performance, and protect the user and the platform. All of this also extends to machine learning technologies and the generative models themselves.

A sensitive part of the process is the human reviewVarious vendors acknowledge that internal staff or service providers review interaction samples to improve security and quality. Hence the consistent recommendation: avoid including confidential information that you wouldn't want a person to see or that would be used to refine models.

In known policies, some services indicate that they do not share certain data for advertising purposes, although Yes, they can provide information to authorities. under legal requirement. Others, by their nature, share with advertisers or partners identifiers and aggregated signals for analytics and segmentation, opening the door to profiling.

The treatment also includes, retention for predefined periodsFor example, some providers set a default automatic deletion period of 18 months (adjustable to 3, 36, or indefinite), and retain reviewed conversations for longer periods for quality and security purposes. It's advisable to review the retention periods and activate automatic deletion if you want to minimize your digital footprint.

Privacy risks throughout the AI ​​lifecycle

Privacy is not at stake at a single point, but throughout the entire chain: data ingestion, training, inference, and application layerIn mass data collection, sensitive data can be inadvertently included without proper consent; during training, it's easy for the original usage expectations to be exceeded; and during inference, models can infer personal traits starting from seemingly trivial signals; and in the application, APIs or web interfaces are attractive targets for attackers.

With generative systems, the risks multiply (for example, AI toys). Datasets extracted from the Internet without explicit permission They may contain personal information, and certain malicious prompts (prompt injection) seek to manipulate the model to filter sensitive content or execute dangerous instructions. On the other hand, many users They paste confidential data without considering that they could be stored or used to adjust future versions of the model.

Academic research has brought specific problems to light. A recent analysis on browser assistants It detected widespread tracking and profiling practices, with the transmission of search content, sensitive form data, and IP addresses to the provider's servers. Furthermore, it demonstrated the ability to infer age, gender, income, and interests, with personalization persisting across different sessions; in that study, Only one service showed no evidence of profiling.

The history of incidents reminds us that the risk is not theoretical: security breaches They have exposed chat histories or user metadata, and attackers are already leveraging modeling techniques to extract training information. To make matters worse, AI pipeline automation It makes it difficult to detect privacy problems if safeguards are not designed from the outset.

What do the laws and frameworks say?

Most countries already have privacy standards in force, and although not all are specific to AI, they do apply to any system that processes personal data. In Europe, the GDPR It requires legality, transparency, minimization, purpose limitation, and security; furthermore, the AI Act European introduces risk categories, prohibits high-impact practices (such as the social scoring public) and imposes strict requirements on high-risk systems.

In the U.S., state regulations such as CCPA or Texas law They grant rights to access, delete, and opt out of the sale of data, while initiatives such as the Utah law They demand clear notifications when the user interacts with generative systems. These normative layers coexist with social expectations: opinion polls show a notable distrust towards responsible use of data by companies, and a discrepancy between users' self-perception and their actual behavior (for example, accepting policies without reading them).

To ground risk management, the framework of NIST (AI RMF) It proposes four ongoing functions: Govern (responsible policies and oversight), Map (understanding the context and impacts), Measure (assessing and monitoring risks with metrics), and Manage (prioritizing and mitigating). This approach helps adapt controls according to the system's risk level.

Who collects the most: an X-ray of the most popular chatbots

Recent comparisons place different assistants on a collection spectrum. Google's Gemini tops the ranking by collecting the largest number of unique data points across various categories (including mobile contacts, if permissions are granted), something that rarely appears in other competitors.

In the middle range, solutions include such as Claude, Copilot, DeepSeek, ChatGPT and Perplexity, with between ten and thirteen types of data, varying the mix between contact, location, identifiers, content, history, diagnoses, usage and purchases. Grok It is located in the lower part with a more limited set of signals.

There are also differences in subsequent useIt has been documented that some services share certain identifiers (such as encrypted emails) and signals for segmentation with advertisers and business partners, while others state that they do not use data for advertising purposes or sell it, although they reserve the right to respond to legal requests or use it for improve the system, unless the user requests deletion.

From the end user's perspective, this translates into one clear piece of advice: Review each provider's policiesAdjust the app's permissions and consciously decide what information you give away in each context, especially if you are going to upload files or share sensitive content.

Essential best practices to protect your privacy

First of all, carefully configure the settings for each assistant. Explore what is stored, for how long, and for what purpose.and enable automatic deletion if available. Review policies periodically, as they change frequently and may include new control options.

Avoid sharing personal and sensitive data In your prompts: no passwords, credit card numbers, medical records, or internal company documents. If you need to handle sensitive information, consider anonymization mechanisms, closed environments, or on-premises solutions. strengthened governance.

Protect your accounts with strong passwords and Two-factor authentication (2FA)Unauthorized access to your account exposes your browsing history, uploaded files, and preferences, which can be used for highly credible social engineering attacks or for the illicit sale of data.

If the platform allows it, disable chat history Or use temporary modalities. This simple measure reduces your exposure in the event of a breach, as demonstrated by past incidents involving popular AI services.

Don't blindly trust the answers. Models can to hallucinate, to be biased, or to be manipulated through malicious prompt injection, which leads to erroneous instructions, false data, or the extraction of sensitive information. For legal, medical, or financial matters, contrast with official sources.

Exercise extreme caution with links, files, and code that is delivered by AI. There may be malicious content or vulnerabilities deliberately introduced (data poisoning). Verify URLs before clicking and scan files with reputable security solutions.

Distrust extensions and plugins of dubious origin. There's a sea of ​​AI-based add-ons, and not all of them are reliable; install only the essential ones from reputable sources to minimize the risk of malware.

In the corporate sphere, bring order to the adoption process. Define AI-specific governance policiesIt limits data collection to what is necessary, requires informed consent, audits suppliers and datasets (supply chain), and deploys technical controls (such as DLP, monitoring of traffic to AI apps, and granular access controls).

Awareness is part of the shield: form your team in AI risks, advanced phishing, and ethical use. Industry initiatives that share information on AI incidents, such as those driven by specialized organizations, foster continuous learning and improved defenses.

Configure privacy and activity in Google Gemini

If you use Gemini, log into your account and check “Activity in Gemini AppsThere you can view and delete interactions, change the automatic deletion period (default 18 months, adjustable to 3 or 36 months, or indefinite) and decide if they are used for improve AI of Google.

It's important to know that, even with saving disabled, Your conversations are used to respond and maintain system security, with support from human reviewers. Reviewed conversations (and associated data such as language, device type, or approximate location) may be retained. up to three years.

On mobile, Check the app permissionsLocation, microphone, camera, contacts, or access to on-screen content. If you rely on dictation or voice activation features, remember that the system may be activated by mistake by sounds similar to the keyword; depending on settings, these snippets could to be used to improve models and reduce unwanted activations.

If you connect Gemini with other apps (Google or third parties), keep in mind that each one processes data according to its own policies. their own policiesIn features like Canvas, the app creator can see and save what you share, and anyone with the public link could view or edit that data: share only with trusted apps.

In regions where applicable, upgrading to certain experiences may Import call and message history From your Web and App Activity to Gemini-specific activity, to improve suggestions (for example, contacts). If you don't want this, adjust the controls before continuing.

Mass use, regulation and trend of “shadow AI”

Adoption is overwhelming: recent reports indicate that The vast majority of organizations already deploy AI modelsEven so, many teams lack sufficient maturity in security and governance, especially in sectors with strict regulations or large volumes of sensitive data.

Studies in the business sector reveal shortcomings: a very high percentage of organizations in Spain It is not prepared to protect AI-powered environmentsand most lack essential practices to safeguard cloud models, data flows, and infrastructure. In parallel, regulatory actions are tightening and new threats are emerging. penalties for non-compliance of the GDPR and local regulations.

Meanwhile, the phenomenon of shadow AI It's growing: employees are using external assistants or personal accounts for work tasks, exposing internal data without security controls or contracts with providers. The effective response isn't to ban everything, but enable safe uses in controlled environments, with approved platforms and monitoring of information flow.

On the consumer front, major suppliers are adjusting their policies. Recent changes explain, for example, how the activity with Gemini to “improve services”offering options such as Temporary Conversation and activity and customization controls. At the same time, messaging companies emphasize that Personal chats remain inaccessible to AIs by default, although they advise against sending information to the AI ​​that you don't want the company to know.

There are also public corrections: services of file transfer They clarified that they do not use user content to train models or sell it to third parties, after raising concerns about changes in terms. This social and legal pressure is pushing them to be clearer and give the user more control.

Looking to the future, technology companies are exploring ways to reduce dependence on sensitive dataSelf-improving models, better processors, and synthetic data generation. These advances promise to alleviate data shortages and consent issues, although experts warn of emerging risks if AI accelerates its own capabilities and is applied to areas such as cyber intrusion or manipulation.

AI is both a defense and a threat. Security platforms already integrate models for detect and respond faster, while attackers use LLMs to persuasive phishing and deepfakesThis tug-of-war requires sustained investment in technical controls, supplier evaluation, continuous auditing, and constant equipment updates.

AI assistants collect multiple signals about you, from the content you type to device data, usage, and location. Some of this information may be reviewed by humans or shared with third parties, depending on the service. If you want to leverage AI without compromising your privacy, combine fine-tuning (history, permissions, automatic deletion), operational prudence (don't share sensitive data, verify links and files, limit file extensions), access protection (strong passwords and 2FA), and active monitoring for policy changes and new features that may affect your privacy. how your data is used and stored.