A hospital has a patient form that they share with their patients over Gmail.

Fingerprint classification in Netskope DLP is designed to identify documents that are derivatives of a known baseline template. In this scenario, the blank patient form represents the template, and any form with patient data filled in would be considered a derivative. Netskope’s fingerprinting engine creates a hash of the blank form and then uses similarity matching to detect documents that share structural characteristics with the template but contain additional user-filled content. This makes fingerprinting the correct DLP rule type for this use case, as it can reliably distinguish between blank forms which are safe to share and completed forms which are confidential. Dictionary-based or Exact Match rules are designed to detect specific text patterns or known data values, not document template similarity or partial completion.