Data classification

The process of categorizing data by sensitivity level (public, internal, confidential, restricted). Determines how data should be stored, shared, and protected. Critical for AI use because pasting confidential data into a consumer AI tool is a classification violation. Regulated industries (HIPAA, GLBA, ITAR) have mandatory classifications that override any generic framework.

The step, taken before a model is designed, of labeling each data field by its regulatory category (protected health information, nonpublic personal financial information, personal data, or not regulated), because the classification, not the engineering, determines which vendors, processing locations, retention limits, and access controls are lawful.

The process of categorizing data by sensitivity level (public, internal, confidential, restricted) to determine appropriate handling, storage, and sharing practices. Essential for knowing what data is safe to enter into AI tools.

An organization's scheme for sorting information by sensitivity, commonly into public, internal, confidential, and regulated/restricted tiers; the foundation under most AI rules, because the data tier determines the permitted tool tier.