Remove sensitive data from text before sharing
Reduce disclosure risk before sharing text by identifying personal and confidential data, replacing what is necessary and performing a human review.
Removing sensitive data can reduce disclosure risk, but automated detection should never be treated as exhaustive. Context matters: several harmless-looking details can identify a person when combined.
Use automated pattern detection as one layer, then perform a human review of the final document.
Table of contents
Define what is sensitive in this context
Emails and phone numbers are obvious examples, but client names, internal references, locations or dates can also matter. Check names, numbers and technical strings separately. If the content contains names, numbers or technical strings, check those separately before keeping the resulting version.
Use detection as a first pass
Pattern matching can surface likely data, but unusual formats may be missed and false positives need review. Compare the result with the original before reusing it. Keep the original nearby whenever the change could alter how a passage is read or how several elements relate to one another.
Replace rather than delete when context matters
Neutral labels such as [CLIENT] can preserve readability while removing the real value. Compare the result with the original before reusing it. Check names, numbers and technical strings separately. If the content contains names, numbers or technical strings, check those separately before keeping the resulting version.
Look for indirect identifiers
Role, city, date and event details can sometimes identify someone without a name. File names and visible metadata also deserve attention. Check names, numbers and technical strings separately. If the content contains names, numbers or technical strings, check those separately before keeping the resulting version.
Run a human validation before sharing
Read the cleaned version end to end and revisit every passage that contained sensitive data in the source. Check names, numbers and technical strings separately. A focused review is usually more useful than rereading everything when the operation only changes one specific part of the text.
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