Anonymisation par machine learning
Utilisez le machine learning pour l'anonymisation intelligente de documents. Les algorithmes ML apprennent en continu et améliorent la détection des données personnelles.
AI detection in the FOI workflow
Step 1
Document ingest
PDF from FOI case is analysed for entities and patterns.
Step 2
Generate proposals
AI flags names, IDs, contact details, and sensitive passages.
Step 3
Reviewer validation
Authorised staff accept, adjust, or reject each proposal.
Step 4
Logging and export
Decisions are logged for accountability and disclosure.
FAQ – AI detection
Does AI replace the reviewer?
No. AI proposes; your organisation makes the final redaction decision.
Is our data used to train models?
Contact us about processing and retention; default is EU hosting with limited retention.
Does it work on scanned PDFs?
Yes, with OCR for scans; native PDFs are faster.
Natural Language Processing
Advanced NLP algorithms understand context and detect sensitive information even in complex sentence structures.
Pattern Recognition
Recognize patterns across formats: ID numbers, email addresses, phone numbers, and more.
Continuous Learning
The system continuously learns from new data and automatically improves accuracy over time.
How Machine Learning Anonymization Works
Training on Document Data
Our ML models are trained on large datasets of documents, optimized for personal data, identification numbers, and government documents.
Contextual Analysis
Unlike simple pattern matching, our ML technology understands context. It distinguishes between real personal data and examples or test data.
Adaptive Improvement
The system adapts to new document types and formats. Feedback loops ensure continuous improvement in accuracy.