Document Redaction Automation
Black boxes that actually remove — finding every name, number, and identifier, and destroying it in the file, not just on the screen.
Document redaction automation is the AI-driven finding and true removal of sensitive content from documents: personal identifiers, financial details, health information, privileged passages, trade secrets — located by detection models and eliminated from the file itself, at scale no manual process reaches. The demand comes from every direction at once: privacy regimes requiring minimization before sharing, FOIA and disclosure obligations, discovery productions, data-subject requests, and the newest driver — sanitizing document corpora before they feed training sets, RAG indexes, or external AI services.
Two competencies must both hold. Detection: finding everything that qualifies — named entities in prose, identifiers in structured fields, faces and signatures in images, and the contextual cases rules miss (a person identifiable from "the claimant's sister, a nurse in the town's only clinic" without any name present). This is document AI proper: OCR bringing scanned content into scope, NER and language models reading meaning, coreference chasing the pronoun that leaks what the redacted name protected. Removal: actually destroying the content — a black rectangle drawn over text whose characters remain in the PDF layer is the classic, repeatedly litigated failure; correct redaction rewrites the content stream, scrubs metadata, OCR layers, and embedded objects, and verifies by re-extraction that nothing recoverable remains.
The operating model is risk-tiered rather than fully autonomous: detection proposes with confidence per finding, policy determines what categories redact for which disclosure purpose (the same document redacts differently for a regulator than a counterparty), human review concentrates on low-confidence findings and high-stakes releases, and sampling audits the automated tier. Every redaction is logged — what category, what policy, who approved — because a redaction decision is itself the kind of decision someone later asks about, and the unredacted original persists under its own access controls as the authoritative record.
Finding the personal data hiding in prose, tables, and scans — before it leaks, trains, or overstays its welcome.
Eighteen identifiers, one obligation — removing protected health information before a record can travel.
Stopping the sensitive file at the door — before it leaves in an email, an upload, or a copy-paste.
Proof Perimeter runs document AI inside your own perimeter — with a provenance record on every field.
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