# Privacy masking divergence It can happen that training data is masked for privacy reasons (e.g. PII), but the actual production data used for inference contains unmasked, clean values, which can produce weird side-effects. This implies the tools used to mask the data are not appropriately modeling the distribution or morphology of the data being masked.