Your Data, Their AI: What Tech Companies Know About You

Key points

User prompts may train models
Public posts used for AI training
Operating-system AI raises privacy stakes

ISLAMABAD: AI assistants feel personal because they speak like humans and remember context. But behind the friendly interface is a simple reality: many AI systems improve by collecting, storing and analysing user inputs — and those inputs can reveal far more about you than you intended.

As generative AI spreads into search, phones, PCs and social platforms, the privacy question is shifting from “Is my data collected?” to “Which data, used how, and with what controls?”

What companies can learn from “ordinary” AI use

Even basic prompts can expose sensitive information: what job you have, where you live, what you’re worried about, what you’re planning to buy, and the names of colleagues or family members. When users upload files, screenshots, images or audio, the potential sensitivity increases sharply — and these are now common features across major AI products. Google’s Gemini privacy hub, for example, lists data a user may provide (prompts, files, photos, page content) and data gathered through usage (generated content, device information, interaction logs), alongside optional “connected apps” context like Search or YouTube history.

“Improve the model” often means using user content

OpenAI’s help documentation spells out that people on personal ChatGPT plans can opt out of their conversations being used for model training via a Data Controls toggle (“Improve the model for everyone”). OpenAI also describes “Temporary Chat” as a mode where chats won’t appear in history and won’t be used to train models, positioning it as a privacy-conscious option for sensitive queries.

The point is not that companies are hiding data use — it is that many users assume a chatbot works like a private conversation. In practice, default settings and product tiers can matter, and the safest approach is to treat AI prompts like you would treat a message you might later regret sending.

Social platforms: your public posts can become training fuel

Social media brings a different kind of privacy risk: not just what you tell an AI assistant, but what you’ve already shared publicly over years. Meta has said it will resume training AI models using public content from adult users in the EU, including posts and comments, while stating it will not use private messages with friends and family for this purpose. It has also said it will notify users and provide an opt-out mechanism.

That debate has sharpened concerns about “consent by default” and how easy (or hard) it is for ordinary users to object — especially when data is spread across multiple products and long timelines.

AI at the operating-system level raises new questions

When AI shifts from an app to an operating-system feature, the privacy stakes rise. Microsoft’s “Recall” — a Copilot+ PC feature designed to help users find what they previously saw — prompted criticism because it involves capturing “snapshots” of activity. Microsoft later said Recall is opt-in and framed it around user control and security design principles.

Even with opt-in systems, the concern is practical: people may click through setup screens, not fully grasping what is being stored, where it’s stored, and how it could be exposed if a device is compromised.

A privacy counter-model is emerging — but it’s not universal

Some companies are pitching privacy as a product feature. Apple’s security team describes “Private Cloud Compute” as a system intended to extend device-level privacy to cloud processing, claiming that personal data sent to the system is not accessible to anyone other than the user — “not even to Apple”. Whether competitors can match that standard consistently across services remains an open question — especially where ad-targeting ecosystems are involved.