GDPR Data Extraction Compliance
The document contains personal data; the extraction is processing — GDPR follows both, everywhere they go.
GDPR data extraction compliance is the application of European data protection law to document AI: when documents contain personal data — and most business documents do — every extraction, classification, embedding, and model inference over them is "processing" under the GDPR, requiring a lawful basis, bounded purposes, minimization, security, and the ability to honor data subjects' rights. The regulation doesn't mention OCR or language models; it doesn't need to — its definitions capture them, and European regulators have made clear that AI pipelines are processing like any other.
The obligations map concretely onto pipeline design. Lawful basis and purpose limitation: the KYC pack processed for onboarding cannot be quietly repurposed as model training data without a basis for that too — training-data governance is a GDPR question, not just an ML one. Minimization: extract the fields the purpose needs, not everything readable; retention limits reach derived data — the extracted values, embeddings, caches, and logs that carry personal data must expire with their purpose, and a deletion request must find them all (data lineage as privacy infrastructure). Cross-border transfer rules make inference location material: documents sent to a third-country model API are transfers, needing valid mechanisms — a major driver of in-perimeter and EU-resident processing architectures. And data-subject rights become engineering requirements: access and portability need the ability to find everything held on a person (PII detection across the document estate), rectification and erasure need it actionable.
Automated decision-making adds Article 22's layer: decisions with legal or similar effect based solely on automated processing — the credit decline computed from extracted documents — trigger rights to human intervention and explanation, aligning with the human-in-the-loop and provenance patterns this glossary treats as good practice anyway. That convergence is the practical takeaway: the architecture GDPR compels — minimal, resident, traceable, human-supervised where it matters — is largely the architecture careful document AI builds regardless.
Finding the personal data hiding in prose, tables, and scans — before it leaks, trains, or overstays its welcome.
The document is stored in Frankfurt — but where did the model that read it run? Residency's newest question.
Black boxes that actually remove — finding every name, number, and identifier, and destroying it in the file, not just on the screen.
Proof Perimeter runs document AI inside your own perimeter — with a provenance record on every field.
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