The requests for access from AI apps have grown broader and more aggressive. Where once people questioned why a flashlight app needed their location or contacts, similar requests are now made under the banner of productivity. Only now, the data these apps ask for cuts far deeper.
One recent case involved a web browser called Comet, developed by Perplexity. It includes an AI system designed to handle tasks like reading calendar entries or drafting emails. To do that, it asks users to connect their Google account. But the list of permissions it seeks goes far beyond what many would expect. It asks for the ability to manage email drafts, send messages, download contact lists, and view or edit every event across calendars. In some cases, it even tries to access entire employee directories from workplace accounts.
Perplexity claims that this data remains on a user’s device, but the terms still hand over a wide range of control. The fine print often includes the right to use this information to improve their AI systems. That benefit flows back to the company, not necessarily to the person who shared their data in the first place.
Other apps are following similar patterns. Some record voice calls or meetings for transcription. Others need access to real-time calendars, contacts, and messaging apps. Meta has also tested features that sift through a phone’s camera roll, including photos that haven’t been shared.
The permissions these tools request aren't always obvious, yet once granted, the decision is hard to reverse. From a single tap, an assistant can view years of emails, messages, calendar entries, and contact history. All of that gets absorbed into a system designed to learn from what it sees.
Security experts have flagged this trend as a risk. Some liken it to giving a stranger keys to your entire life, hoping they won’t open the wrong door. There’s also the issue of reliability. AI tools still make mistakes, sometimes guessing wrong or inventing details to fill in gaps. And when that happens, the companies behind the technology often scan user prompts to understand what went wrong, putting even private interactions under review.
Some AI products even act on behalf of users. That means the app could open web pages, fill in saved passwords, access credit card info, and use the browser history. It might also mark dates on a calendar or send a booking to someone in your contact list. Each of these actions requires trust, both in the technology and the company behind it.
Even when companies promise that your personal data stays on the device, the reality is more complicated, as highlighted by u/robogame_dev or Reddit. Most people assume this means photos, messages, or location logs remain untouched. But what often slips under the radar is how that raw information gets transformed into something else, something just as personal.
Modern AI tools extract condensed representations from your data. These might look like numerical vectors, interest segments, or hashed signals. While the raw voice clip or image may stay local, the fingerprint it generates, a voice embedding, a cohort ID, or a face vector, often gets sent back to the server. These compact data points can still identify you or be linked with other datasets across apps, devices, and even companies.
Over time, that creates a shadow profile. It doesn’t need your full browsing history or photo albums to be useful. A few attributes, like the categories of content you read, the way you speak, or your heart rate trends, can reveal more than expected. Advertisers, insurers, or third-party brokers may use this information to shape pricing, predict preferences, or infer sensitive traits.
So while on-device processing helps limit exposure, it doesn’t erase the risk. Much like measuring your face without keeping the photo, what gets extracted and exported can still follow you around the digital world.
If an app/tool asks for too much, it may be worth stepping back. The logic is simple: just because a tool can help with a task doesn’t mean it should get full access to your digital life. Think about the trade. What you’re getting is usually convenience. What you’re giving up is your data, your habits, and sometimes, control.
When everyday tools become entry points for deep data collection, it's important to pause and ask whether the exchange feels fair. As more of these apps blur the line between helpful and invasive, users may need to draw that line themselves.
Notes: This post was edited/created using GenAI tools. Image: DIW-Aigen.
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by Irfan Ahmad via Digital Information World
