Anonymization

True anonymization is a high bar and frequently claimed for processing that does not meet it. Removing direct identifiers leaves quasi-identifiers — dates, locations, rare conditions, role titles — whose combination can re-identify individuals in small populations. In an AI context the risk compounds, because retrieval can assemble fragments from several records that were each individually safe. The practical consequence is that anonymization is rarely sufficient on its own for sensitive corpora, and exclusion is often the more honest control.

Centralpoint applies redaction during ingestion using the organization's own dictionary, and where anonymization cannot be relied upon, exclusion from the index is available as a classification decision — material remains fully usable in the business process it was collected for while never entering the retrieval surface.