EU AI REGULATION
Datenmaske is a system with a minimal risk class.
The EU AI Regulation (AI Act) classifies AI systems by risk. Datenmaske does not fall under the Annex III high-risk categories (no biometrics, no law enforcement, none of the critical use areas listed there). PII detection in text documents is tool-supportive and always subject to human approval.
Classification: minimal risk class (comparable to spam filters). We do not claim an “AI Act certificate” — no such thing exists for minimal risk classes. The correct statement is: minimal risk + voluntary transparency disclosures.
MODEL CARDS
The models in detail
spaCy NER (de_core_news_lg)
- Purpose
- Detection of German proper names (people, places, organisations) in the text layer
- Origin
- Open-source model (Explosion / spaCy), pre-trained on German corpora
- Inference location
- Self-hosted on spacy.mokka-dev.de (EU)
- Training on customer documents
- No — never.
- Updates
- Version-pinned; no automatic re-training on customer data
GLiNER (self-hosted)
- Purpose
- Zero-shot Named Entity Recognition to extend the spaCy detection
- Origin
- Open-source model
- Inference location
- Self-hosted in the EU
- Training on customer documents
- No — never.
- Updates
- Version-pinned; no training on customer data
urchade/gliner_multi_pii-v1 (PII-NER)
- Purpose
- PII-specific zero-shot NER to extend the detection of special data categories (health, financial data, identity documents)
- Origin
- Open-source model (urchade/gliner_multi_pii-v1, mDeBERTa), available on HuggingFace / onnx-community
- Inference location
- Self-hosted in the EU (onnx-community ONNX runtime)
- Training on customer documents
- No — never.
- Updates
- Version-pinned; no training on customer data
OpenMed (medical NER)
- Purpose
- Medical Named Entity Recognition for doctor's letters, diagnoses and other health data (Art. 9 GDPR)
- Origin
- Open-source model (HuggingFace), medical NER corpus
- Inference location
- Self-hosted in the EU
- Training on customer documents
- No — never.
- Updates
- Version-pinned; no training on customer data
Azure Document Intelligence (OCR)
- Purpose
- Text recognition exclusively for scanned pages (PDF images)
- Origin
- Microsoft Ireland Operations Ltd.
- Inference location
- Region Germany (Germany West Central); EU Data Boundary
- Training on customer documents
- No — never.
- Updates
- Microsoft-side; no use for model training (contractual)
Further components of the detection pipeline (rule-based patterns/gazetteers, string matchers) are not learning models and are documented in the pipeline overview at /forschung. The model cards above describe the production NER and OCR stack of Datenmaske Web; the Desktop edition (§ 203) uses a partly different, locally bundled model selection.
ASSURANCES
What we assure — and what we do not.
- EU inference. All model inference runs in the EU — spaCy/GLiNER self-hosted (Germany), Azure OCR in the Germany region (Germany West Central). No transfer to third countries takes place (see DPA § 2 Abs. 3).
- No training on customer documents. We never use your documents to train, fine-tune or improve our models. Models are version-pinned and maintained independently of customer data.
- Human-in-the-Loop, fail-closed. Every redaction requires approval by the user. Export is blocked until verification (no detected PII string remaining in the text layer) has passed.
- Audit trail. Every detection and redaction is traceably documented in the redaction log (with SHA-256 hashes) — Art. 5 Abs. 2 GDPR accountability.
- No LLM chat feature at present. Datenmaske currently does not deploy any generative Large Language Model that directly “interacts” with end users (Art. 50 Abs. 1 AI Act). The AI runs as background processing with human approval. Should an LLM feature be added, the Art. 50 transparency obligation will be fulfilled in advance.
- No guarantee of detection completeness. AI-based suggestions must be reviewed by the user. Datenmaske supports manual review; it does not replace it.