Smarter at Scale: Why AI-Native Classification Techniques Outperform Exhaustive Scanning

This report argues that exhaustive, full-content scanning fails at modern, multi-petabyte scale due to time drift, thin coverage, low signal, privacy exposure, and cost, and promotes “smart representation” instead—modeling repetitive data into families, inspecting representative samples, and escalating to targeted deep reads only when narrowly scoped, high-stakes questions arise. It specifies where this method fits (machine-generated data lakes, object stores, and column-level views in structured stores) versus where full reads remain necessary (human-generated files), and prescribes program-owned assurance standards, scheduled re-verification, and end-to-end auditability to keep outcomes fresh, accurate, and defensible.