How to redact text before using ChatGPT

A useful prompt does not need every detail in the original email or error log. If you want to know how to redact text before using ChatGPT, start by deciding what the task actually requires. Remove unnecessary identifiers and credentials, preserve the facts needed for the question and review the exact text you will send. This guide describes a practical editing workflow. The process has limits. It cannot guarantee anonymity or find every hidden detail. After you send the prompt, the service controls its own copy.

Start with the question, not the whole document

Write the question in one sentence before copying source material. If you need help understanding an error, the relevant message and environment may be enough. If you want to improve an email's tone, the recipient's home address is unlikely to matter. A short extract is easier to inspect. It is less likely to contain details you did not mean to share.

Separate technical facts from identifiers. An error code, library version or sequence of events can be important. An access token, customer name or internal hostname may not be. Keep enough to explain the problem. Leave out details that do not help, even if they appear in the source. Each retained field should serve the task.

The ICO's AI data-minimisation guidance provides the official framework for considering personal data in AI use. Apply your organisation's approved process to the actual material. An editing tool can help with that process. It does not grant permission to share the text.

What needs removing before the paste?

Look for direct identifiers, indirect identifiers and secrets. Direct identifiers include names and contact details. Indirect identifiers include combinations of role, location, date or unusual events that could reveal a person. Secrets include passwords, access tokens and connection strings. Those categories can overlap, and removing one does not establish that the rest are safe to share.

OWASP's logging guidance lists “Access tokens” among data that should normally be excluded from logs. Keep that in mind when copying a log. It may contain things that should never have been recorded. Do not assume that a diagnostic file is ready for wider distribution just because a system generated it.

Also check comments, quoted replies, file paths and URLs. A path can contain a person's name. A URL can contain a signed access parameter. An email thread may include an earlier message unrelated to your question. Read the whole extract from start to end. Do not check only the paragraph you meant to discuss.

Use placeholders that preserve relationships

Replace repeated entities consistently. If one person appears three times, use the same neutral label each time. Keep different people distinct when the sequence matters. A prompt about who approved a request will become misleading if every name is replaced with the same label. The reader still needs to follow the problem. A page full of identical hidden blocks will not help.

Keep units, version numbers and error codes when they are necessary and not themselves sensitive. Generalise dates or locations only as far as the task allows. Suppose the issue depends on a two-hour gap. Keep that gap. You may not need the actual meeting date. Explain any purposeful simplification to the recipient so it is not mistaken for original evidence.

Table: illustrative prompt editing choices. These examples are review decisions, not a promise that a cleaner detects every category.

Never place a real credential into a public example to test whether a cleaner catches it. Use a clearly inert example or a local test fixture. Has a live secret already been shared? Hiding it in a new copy does not revoke the old one. Follow the secrets guide and the relevant service's incident process.

A worked example: the support email

Imagine an illustrative support email describing an export failure. It includes a person's name, an email address, an internal project title and the line “Export failed: E204”. Your question is whether the message explains the next step clearly. The name and contact details add little. The error line and the action the user tried are relevant.

A reviewed prompt could describe “a customer exporting a report” and retain the error code and the proposed response. Keep any limitation that changes the advice, such as whether the export is still running or the file was never created. Remove the unrelated quoted thread. Read the result as if you had never seen the original: is the problem still understandable?

The final read can reveal another issue. Replacing a project name may leave a sentence saying “it” with no clear subject. Repair the grammar without adding facts. Do not invent a success message or a cause to make the story smoother. A well-redacted prompt can still say that the cause is unknown.

How does local cleaning fit into this?

Lass0's text cleaning workflow operates after extraction and before copying when a cleaner is enabled. The cleaner documentation describes the controls, order and retained original. Use it to make a draft. Then read the cleaned text. A cleaner may miss clues that identify a person. A smooth rewrite can also change what a sentence means.

Read both the cleaned version and the source. Check that needed facts remain. Remove details you do not need. Use the same label for each repeated person or thing. Check the destination field after pasting as well. Check which version you pasted. It may be an old clipboard item or the original text. The exact text visible before submission is the material you are about to share.

Local cleaning does not mean the destination runs locally. Once you send it, the service handles the prompt. Its rules and your account settings apply. Check those current settings through the service's own controls and your organisation's guidance. A local clipboard tool cannot tell you how long the service keeps a later paste. Check that separately.

Does removing names make text anonymous?

Not necessarily. A rare job title, location and incident date may identify someone without a name. The same label can link several clues. Together, those clues may still reveal the person. The anonymisation guide explains the distinction between removing fields, using pseudonyms and achieving anonymity. Keep that distinction visible when deciding whether the extract can be shared.

For private or regulated text, use the approved process. Ask the person responsible if you are unsure. The useful default is to reduce the extract and keep the purpose specific. A tool label or clean preview is not enough. Even with no names, you must check whether sharing is allowed.

Make the final review a small habit

Before sending, check the purpose and facts. Look for names, IDs and secrets. Confirm where the text will go. First read the prompt on its own. Does it still make sense? Then compare it with the source to check the meaning. If there is a critical uncertainty, leave it explicit or stop and obtain the right information.

Try the workflow on a non-sensitive passage first. Review the clipboard privacy checks if you use history or synchronisation tools. The aim is a useful question with the minimum necessary context, prepared through a process you can explain and repeat. That is more dependable than trusting a single “clean” button to make every sharing decision for you.

Questions, answered

Can a text cleaner guarantee that no personal data reaches ChatGPT?

No. A cleaner can miss clues that identify someone. Read the final paste. You also need the right to share it, and must check the service you plan to use.

Should I remove error codes and software versions?

Keep facts needed for the task, after checking what they reveal. Remove secrets and personal details that do not help explain the problem.

Does redacting a leaked API key make the original safe?

No. The old key may still work. Only its issuing service can revoke or expire it. Follow the right process for a leak.