Information Governance Frameworks
Who may know what, kept where, for how long — the policy architecture documents live inside.
Information governance frameworks are the policy architectures through which organizations manage information as an asset and a liability: classification schemes that grade sensitivity and record status, access models that define who may see what, retention schedules and disposition rules, quality standards, privacy and residency requirements, and the accountability structures — owners, stewards, committees — that keep the policies real. Standards and models (ISO 15489 and the records-management canon, ARMA's principles, sectoral regimes layered on top) give the frameworks their common shape; each organization's version binds them to its regulatory footprint and risk appetite.
Document AI relates to governance twice over. As executor: governance was historically aspirational at document scale — policies presumed classification, and nobody could classify a hundred million accumulated files — and AI removed exactly that constraint: classification models grading sensitivity and record type across repositories, PII detection mapping where privacy rules bite, extraction capturing the dates retention clocks need, DLP enforcing handling rules at the boundaries. The framework's paper controls become running systems, and the perennial audit gap between policy and practice closes to the extent the models are accurate — which makes model quality itself a governance concern. As subject: the AI estate generates information the framework must govern — extracted datasets, embeddings, training corpora, model decision logs, review records — each needing classification, ownership, retention, and access rules like any record, and some (training data derived from customer documents, audit trails of automated decisions) needing them urgently.
The practical convergence is worth naming: the governance framework tells document AI what its constraints are (what may be processed, where, retained how long, seen by whom), and document AI gives the framework its enforcement layer and its evidence. Organizations that connect the two — governance requirements expressed as pipeline configuration, pipeline telemetry reported as governance evidence — stop running compliance and automation as parallel bureaucracies and start running one system that is both.
From creation to defensible destruction — governing every stage a document lives through.
Seven years, ten years, or 'delete on request' — the rules that say how long each record must, and may, live.
Classifying, retaining, and disposing at population scale — governance made executable across the whole archive.
Proof Perimeter runs document AI inside your own perimeter — with a provenance record on every field.
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