Structure-Preserving Document Processing

Enterprise documents are not flat text. They contain tables, nested sections, cross-references, entity relationships, and formatting that carries semantic meaning. When flat masking tools process these documents, they treat every sensitive value identically — replacing it with a generic token regardless of its structural role.

A customer name in a table header serves a different function than the same name in a paragraph. A date in a contract preamble has different significance than a date in a payment schedule. Flat masking collapses these distinctions, degrading AI output quality.

Structure-preserving processing operates at multiple document layers:

• Layout preservation. Table structures, column headers, row relationships, and nested sections are maintained. AI models can accurately extract tabular data because the schema remains intact.
• Entity consistency. When the same entity appears multiple times in a document, all instances are mapped to the same replacement. AI models can track entity references across sections — "the Borrower" in clause 1 refers to the same encapsulated entity in clause 7.
• Semantic context. Context-aware data control distinguishes between sensitive data that must be protected and contextual information that AI needs for comprehension. Job titles, section headers, and document type indicators remain readable.
• Cross-document consistency. When processing document sets — such as a contract and its amendments — entity mappings remain consistent across files, enabling AI to perform accurate cross-document analysis.