Safety & Security · Software component
NER PII Detector
Software componentSafety & SecuritySafety, Security & Governancearc:NERPIIDetector
A PII detector that uses a named-entity recognition model to label person names, organizations, locations and other semantic entities as PII.
Responsibility. Detects entity-type PII lacking a fixed format.
Also known as: Named Entity Recognition detection, NER-based detection
Variant of PII Detector abstract
When to choose. Choose for unstructured PII such as person names, organizations and locations that regex cannot distinguish from normal text; costlier than patterns.
Relationships
hosts structural
is configured by structural
invokes dependency
is invoked by dependency
alternative to variability
Design guidance
- SHOULD consult an allowlist so public figures' names are not redacted.
- SHOULD flag medium-confidence NER matches for human review.
Quantitative guidance
As stated by the sources; verify before use.
- Catches the remaining 20-30% of PII that patterns miss; example confidence 0.75 (Ch6.4).
Classification
- Patterns
- Named entity recognition
- Technologies
- spaCyPresidio
- Quality attributes
- Functional suitability: correctness and validity (ISO/IEC 25010 | NIST AI RMF: valid)
- Risks mitigated
- Exposure of patient, staff and customer names
Sources
- Ch6.4: T. Nguyen, "Data Quality Fundamentals," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 6.4. ISBN: 9798244538229.
- Ch7.5: T. Nguyen, "NeMo Curator, Riva Speech AI & Multimodal Integration," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 7.5. ISBN: 9798244538229.