Vision

Last updated August 9, 2026

Today we remove the name from the envelope. An AI provider answers your question without being told whose account it came from — no name, no email, no identifier, and nothing linking one question to the next.

That is worth having, and it is not enough. If you write my landlord Sarah Chen is withholding my deposit at 14 Bridge Street, the envelope is anonymous and the letter is not. Everything that actually identifies you is in the part we currently pass through untouched.

The goal is to close that gap. Not to make the product refuse the question, and not to hand the model something so stripped it answers uselessly — but to let you write exactly what you would write to a person you trust, and have the machine on the other side see a version of it with the identity taken out.

Substitute, don't delete

The obvious approach is to cut identifying details out. It breaks the product. Ask for help replying to your landlord with the name removed and you get an answer addressed to [name] throughout, which you then have to repair by hand — so you stop using the feature, and you have traded a real capability for a privacy gain you cannot see.

The approach that works is to swap each identifying detail for a stable placeholder, keep the mapping, and put the real values back in the answer:

You write

Write a reply to my landlord Sarah Chen about the deposit

The provider sees

Write a reply to my landlord PERSON_1 about the deposit

The provider answers

Hi PERSON_1, I'm writing about the deposit…

You read

Hi Sarah Chen, I'm writing about the deposit…

The provider gets a coherent question it can actually answer. You get your own words back. The mapping between the two is the only place the real name and the placeholder are connected — and the intent is that it lives on your device and never travels with the request at all.

What that has to cover

Anonymity in the contents of a message is not one problem. It is several, of very different difficulty, and honesty about which is which is the point of this table.

In what you type

Emails, phone numbers, cards, IBANs, ID numbers, addresses

Designed

Fixed formats. A card number can be checked arithmetically before it is treated as one, so false positives stay rare.

Names, employers, places

Research

No fixed shape — identity carried by the words themselves. Needs a model, and this is where the accuracy ceiling lives.

Identity implied without being written

Research

“My wife’s employer laid off half the Dublin office last Tuesday” names nobody and identifies several people. We do not believe this is solvable, and we will not claim it.

In what you upload

Where and when a photo was taken

Shipped

The coordinates, device and timestamp embedded in the file. Removed on your device as the picture is attached — the one part of this page that is already true.

Faces

Designed

Detected and obscured before the image is forwarded. Well served by existing models.

Text inside an image

Designed

A letterhead, a screenshot, a form. Read the text out of the picture, then put it through the same pipeline as anything you type.

Objects and surroundings

Research

Landmarks, uniforms, a screen in the background, a reflection. The hardest category and the least likely to be solved completely.

Where it runs decides what we may claim

The same feature is worth very different amounts depending on where the work happens, and we would rather explain that than let the strongest-sounding version be assumed.

On our infrastructure. Your message reaches us, we take the identity out, and we forward what is left. This is the version that ships first, because the chokepoint already exists. The honest sentence for it is removed before we forward it — and note what that does not say: the original reached us.

On your device. The identifying detail is replaced before anything leaves your phone, and the mapping never exists anywhere else. The honest sentence becomes identifying details never leave your device, which is a categorically stronger claim — and the destination we are building toward. It costs three separate client implementations and is limited by the least capable phone we support, which is why it is second rather than first.

We will say which one is running. When the second arrives, this page and Security change on the same day.

The order we are building it in

1

Strip location and device data from images on upload

Shipped
2

Placeholder substitution and restoration, proven end to end

Designed
3

Restoration inside a streaming response, without seams

Designed
4

Detection of structured identifiers — cards, emails, phones, IDs

Designed
5

Live on every outbound request, for structured data only

Designed
6

Detection of names, employers and places

Research
7

Faces and text inside images

Research

Steps 1 to 5 are a working product on their own, and they are the point at which what we are allowed to say about Secure AI changes. Everything after raises the ceiling.

What we will never claim

Detection of this kind is never complete, and a privacy product that implies otherwise is doing something worse than one that offers nothing. So, in advance of building it:

We will not say all identifying detail is removed. We will not promise that context — the things that identify you without naming you — is caught, because we do not believe it can be. We will not describe a stage as shipped on a platform where it is not running. And if detection cannot be performed on a request, that request will fail rather than quietly go through unprotected.

The strongest thing a company in this position can offer is a precise account of where its protection stops. That is what this page is for, and it is why it exists before the feature does.

Questions, or want to be told when a stage moves? Email privacy@secureai.one.

© 2026 Secure Artificial Intelligence, Inc.