How it works in a data room
Instead of searching for terms one at a time, the user selects a folder and the categories to remove, such as people’s names, email addresses, bank details or salary figures. The model reads each file, including scanned pages once OCR has run, and marks likely matches with a confidence level. Reviewers accept, reject or add marks, then the system burns the redactions into a new version so the hidden text cannot be recovered. The original stays available to authorized users only, and every step is logged.
Why it matters in a deal
Manual redaction of thousands of contracts and HR files can take weeks and still miss items. AI tools make wide redaction realistic, which supports data minimization and lowers privacy risk. They also miss things, especially in handwriting, tables, unusual layouts and languages the model handles less well, so human review remains essential. Check whether AI redaction is billed separately before relying on it across a large room.
Example
A seller needs to remove employee names and pay figures from 4,500 HR documents before the first round. AI redaction proposes about 38,000 marks overnight. Two paralegals review a structured sample, find a recurring miss in scanned payslips and fix those by hand. The folder is released three days later instead of the three weeks the team had planned.