Redaction reduces exposure by excluding information that a downstream system does not need. A pipeline can detect identifiers or restricted fields and replace, remove or tokenize them before data reaches a language model or analyst.
Effective redaction must consider indirect identification and unstructured text, not just obvious database columns. The process should be validated, logged and applied before sensitive content enters less trusted components or durable model traces.
ELI5
Data redaction removes or permanently hides sensitive parts of a record before the rest is shared or processed. The goal is to give a system only the information it actually needs.
For example, a support transcript can have names, account numbers and addresses removed before it is sent to an AI model for topic analysis. The redaction process should also look for identifying details hidden in free text and verify that the original values cannot be recovered.
