How can you classify millions of records without a model doing the judging?

Manual classification fails above tens of thousands of records, which is well below the scale conversational and historical content arrives at. Automating with a model introduces probabilistic judgement and inference cost into a control that ought to be deterministic — and inherits the risk of the model classifying wrongly at volume.

Data Cleaner applies the organization's own dictionary — its terms, statutes and categories, imported through Data Transfer — so classification is repeatable and explainable, and no model is paid to decide what is sensitive. Because the dictionary carries version history, what was being applied in any past period is establishable rather than recalled.