Data protection & tokenization

AI data leakage rarely looks like a breach. It happens through legitimate work: an employee gives an AI tool a contract, a customer record, source code or unreleased figures to get a useful answer, and the data reaches infrastructure the organization doesn't control. It goes unnoticed because no transfer looks unusual and most monitoring sees access, not content.

Redaction protects sensitive data by destroying it, which breaks the structure an AI model needs to give a useful answer; tokenization replaces each sensitive value with a consistent, reversible token before the query leaves the perimeter, then restores the originals in the response. These articles cover how leakage happens, tokenization against redaction, protection at the moment AI is used, THEMIS's allow, mask, block or escalate decisions, and the tools that combine detection with protection.