AI image generation and children: the gap most filters miss

A student blocked from discussing a subject who can generate a picture of it has not been protected. They have been redirected — and this is the most common gap in filtered AI products.

If you test one thing in a filtered AI product before trusting it, test the image tool. Text filtering was built first and is usually the most mature part of any platform. Image, video and voice generation tend to arrive later, and the filtering does not always arrive with them.

The result is a specific and predictable gap: a school configures its blocked topics, tests them in chat, sees them work, and never checks whether the same rules reach the picture generator. Often they do not.

Why the gap happens

It is rarely negligence. It is how these products get built.

Filtering usually starts as a check in the chat path. When image generation is added, it is a different endpoint with a different request shape, and the check has to be added there deliberately. If the guardian rules live in one function and the image tool calls another, nothing fails loudly — the picture is simply generated. Nobody sees an error, so nobody investigates.

The same applies to video, voice narration, and any story or book generator. Each is a separate path, and each has to be wired to the same rules.

What makes images harder than text

The prompt and the result can diverge. An innocuous description can produce an unsuitable image, so screening only the request is not enough — the output has to be checked too.
Modesty standards are visual and specific. A rule about clothing or depiction has to be enforced in the image itself, not inferred from the words asked for.
Indirection is easy. A subject can be requested by describing a scene rather than naming it, which a text rule written in nouns may not catch.
Images circulate. A generated picture leaves the platform as a file and travels in a way a chat message usually does not.

How to test it in ten minutes

Set a blocked topic your school genuinely cares about.
Confirm it is refused in chat. It almost certainly will be.
Ask for an image of the same subject, named directly.
Ask again, describing the scene without naming the subject.
Repeat in every generation tool the product offers — video, story, book, presentation.
Do it all on a device the school does not manage.

If step 3 or 4 succeeds anywhere, the school's policy is not in force where it matters, whatever the settings screen shows.

What good looks like

The guardian's rules are evaluated in one place that every tool consults, rather than reimplemented per feature. The request is screened before generation and the result is screened after. A blocked attempt is recorded the same way regardless of which tool it came from, so a pattern in the image tool is as visible as one in chat.

And there is a switch for the category as a whole — a guardian who wants image and story generation off entirely should be able to turn it off, rather than trying to enumerate every subject in advance.

In Navōn the same guardian rules run on every tool, including image and video.

Common questions

Do AI content filters cover image generation?

Not always. Text filtering is usually built first and image, video and voice tools are separate paths that must be wired to the same rules deliberately. This is the most common gap in filtered AI products.

How do I test whether a filter covers images?

Set a blocked topic, confirm it is refused in chat, then request an image of the same subject — first naming it, then describing the scene without naming it. Repeat in every generation tool the product offers.

Why is filtering images harder than filtering text?

The prompt and the result can diverge, so the output must be screened as well as the request. Visual standards such as modesty have to be judged in the image itself rather than inferred from the words asked for.

Published by Navōn. How these guides are written and checked.