GDPR
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How Federated Learning Protects Privacy, and Where It Stops
Federated learning keeps raw training data on-device, but model updates still leak records. What the architecture protects, and what closes the gap.
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Pseudonymization vs Anonymization Under GDPR
Anonymized data leaves GDPR entirely, pseudonymized data does not. Where Recital 26, the CJEU SRB ruling and EDPB guidance put the line in practice.
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GDPR Compliance Tools for AI: 6 Platforms Compared
Compare OneTrust, BigID, Securiti, Privado, DataGrail, and Vanta for DPIAs, data mapping, DSARs, ROPA, and AI governance workflows.
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GDPR Compliance for Machine Learning Models: Practical Guide
GDPR requirements for machine learning models: a lawful basis and processing record at each entry point, DPIAs, Article 22 review, and vendor diligence.
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How to Anonymize Training Data: Methods and Compliance
How to anonymize training data in practice: PII scrubbing, k-anonymity, differential privacy, synthetic data, and what GDPR actually requires.
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Best Data Anonymization Tools: Open Source vs Enterprise
A practitioner's guide to the best data anonymization tools 2026: ARX, Microsoft Presidio, Tonic.ai, and K2View, plus how to pick for your threat model.
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How Membership Inference Attacks Work, and Why They Matter
Membership inference attacks reveal whether a person's record was in a model's training set. Here is the mechanism, the shadow-model method, and defenses.
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Training Data Privacy: GDPR Data Subject Rights
EDPB Opinion 28/2024 and CNIL guidance reshaped how GDPR applies to AI training data, from model anonymity to legitimate interest and erasure requests.
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Cross-Border LLM Data Transfers: SCCs After Schrems II
Most LLM deployments cross borders. How Standard Contractual Clauses, post-Schrems II case law, and supplementary measures apply to model inference.
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GDPR Article 22 and LLM Automated Decision-Making
The analysis explains when LLM workflows trigger Article 22, what counts as a significant effect, and how meaningful human review and appeals work.