Editorial desk
AI Privacy Report Editorial
AI Privacy Report Editorial is the publishing identity for AI Privacy Report. It is a desk, not a person: no named author, no biography, no professional certifications.
Articles published under this byline are researched from primary sources — vendor and project documentation, published standards and specifications, research papers, and measurements published by whoever took them — drafted with AI assistance, and edited against those cited sources before publication. Nothing here is based on first-hand testing in a private lab, and any figure that appears is attributed to the source it came from.
Corrections go to hello@aiprivacy.report. More detail is on the about page and the editorial disclosure.
Posts (16)
- Data Privacy
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.
- Data Privacy
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.
- Compliance
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.
- Compliance
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.
- Tools
Best Synthetic Data Generation Tools for Privacy
SDV, MOSTLY AI, Gretel, and Tonic.ai compared on the axis that matters most: whether the synthetic data output really protects the source records.
- Data Privacy
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.
- Data Privacy
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.
- Policy
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.
- Policy
CCPA and CPRA ADMT Rules for LLM Products
California's finalized ADMT rules add pre-use notice, opt-out, appeal, and risk-assessment duties to automated decisionmaking, catching many LLM products.
- Policy
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.
- Policy
AI Chat Assistant Privacy Risks: Training, Review, Retention
Consumer AI assistants increasingly default to using your conversations for training, human review and multi-year retention. Here is what actually changes.
- Policy
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.
- Policy
DPIA Template for LLM Deployment: A Working Structure
A working Data Protection Impact Assessment structure for LLM workflows, covering the Article 35 risk factors and where the AI Act overlaps with GDPR.
- Policy
EU AI Act Article 50: Transparency Obligations Explained
Article 50 of the EU AI Act has applied since 2 August 2026: disclosure duties for chatbots, synthetic content, deepfakes and emotion recognition.
- Policy
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.
- Policy
EU AI Act Articles 53 & 55: GPAI Provider Checklist
A practical checklist for GPAI providers under Articles 53 and 55, covering technical documentation, training-data summaries, copyright, and systemic risk.