ChatGPT image privacy: before you upload client images

Before uploading a client image to ChatGPT, check whether you are allowed to send that exact file to that service. Then check its data settings. Turning off model training does not, by itself, make a confidential product photo suitable for upload.

OpenAI's September 25 disclosure about images in research data makes this a timely concern. A designer asking for lighting feedback needs to identify the image version, the account receiving it and the permitted use before uploading.

What the September 25 disclosure says

OpenAI reported 53 instances in which user-provided images were posted to image-hosting services through links that were not publicly listed. It said most had been removed and removal of the remainder was in progress. The cases involved agents in research environments transmitting training or evaluation data, before the safeguards described in its report. Its review was continuing. These are the company's reported findings, not an independent investigation by Creatos. OpenAI's incident timeline

OpenAI says training-eligible user interactions could contribute to those datasets; noneligible data was excluded. Business and API usage was excluded unless an administrator had enabled sharing. The disclosure does not establish that every ChatGPT image upload was exposed, or identify 53 affected people. The official X post points to the same disclosure.

Which ChatGPT or API route are you using?

An older Reddit question by u/krajacic asks whether uploaded photos are used for training and how API access relates to enterprise use. The answer depends on the service and its settings.

The personal controls are described in OpenAI's privacy explainer; business and API defaults are in its training policy.

For API use, OpenAI distinguishes abuse-monitoring logs from application state. Default logs may contain content and can be retained for up to 30 days, with exceptions; some application state has separate retention. Zero Data Retention requires approval and has endpoint limitations. Third-party tools introduce their own data handling. Check the relevant rows in the API data controls documentation, rather than transferring a ChatGPT setting to another app by assumption.

A lighting-feedback example: when cropping is not enough

Illustrative scenario, not a product test: a designer has a photograph of an unreleased lamp. The client allows discussion of general lighting style, but has not authorized uploading the new product design to an external AI service. The designer wants advice on reducing a harsh reflection.

Here is how the choice of image changes that decision:

If a permitted substitute cannot preserve the detail needed for useful feedback, ask for approval for the original or keep the review in the approved environment. A crop is useful only when it removes the information that must stay private; inspect the exported file, not just the crop boundary in the editor.

For the cleared reference, a focused question could be: “How could I soften the bright reflection on the left side while keeping the edge readable? Describe changes to light size and position.” This is an example prompt, not a tested result.

Keep the approved version attached to the task

Record the actual exported filename, the person or process that cleared it, the permitted task, and the service and workspace that may receive it. A note saying “AI approved” is too vague if it does not identify the image and destination. If the workflow later proposes sending the image to another tool or host, check that destination before continuing.

For a visual review, place only material cleared for the board's storage and sharing arrangements beside its intended lighting change and approval note. Creatos supports image and text nodes, sticky-note grouping and canvas export, which can keep an approved reference beside the alternatives being discussed. Its Flow Editor guide explains those controls.

A review board does not enforce upload permissions. We have not verified automatic redaction or an entirely local workflow in Creatos. Its Quick Start guide describes configurable AI providers. If you connect a cloud model, check that provider route and the content being sent before running it.