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+# Task 3: PII Protector Implementation - Summary
+
+## Completed: Task 3.1 - PIIProtector Class with Hybrid Detection
+
+### Implementation Overview
+
+Successfully implemented the `PIIProtector` class in `src/pipeline_mco_pmsi/processors/pii_protector.py` with the following features:
+
+#### Core Functionality
+
+1. **Hybrid Detection Approach (Regex + NER)**
+   - Regex patterns for structured data (dates, NSS, phones, emails, addresses)
+   - NER (Named Entity Recognition) support via spaCy for person names
+   - Context-based name detection as fallback
+
+2. **PII Types Detected**
+   - **Names**: Using NER and context patterns (e.g., "Patient Jean Dupont", "M. Martin")
+   - **Birth Dates**: Multiple formats (JJ/MM/AAAA, AAAA-MM-JJ, "15 mars 1960", etc.)
+   - **NSS (Social Security Numbers)**: With and without spaces (15 digits)
+   - **Phone Numbers**: Various formats (spaces, dots, dashes, international)
+   - **Emails**: Standard email format
+   - **Addresses**: Street addresses and postal codes
+
+3. **Key Methods**
+   - `detect_pii(text)`: Detects all PII in text, returns list of PIISpan objects
+   - `anonymize_text(text, pii_spans)`: Replaces PII with placeholders
+   - `filter_logs(log_entry)`: Filters PII from log entries
+   - `has_pii(text)`: Checks if text contains PII
+
+4. **Design Principles**
+   - **High Recall Preference**: Prefers false positives over false negatives to avoid PII leaks
+   - **Span Merging**: Automatically merges overlapping detections
+   - **Confidence Scoring**: Each detection has a confidence score (0.0-1.0)
+   - **Lazy Loading**: NER model loaded only when needed
+
+### Test Coverage
+
+Created comprehensive unit tests in `tests/test_pii_protector.py`:
+
+- **38 unit tests** covering:
+  - Detection of all PII types with various formats
+  - Anonymization functionality
+  - Log filtering
+  - Edge cases (empty text, overlapping spans, composite names, etc.)
+  
+- **Test Results**: ✅ All 38 tests passing
+- **Code Coverage**: 82% for pii_protector.py module
+
+### Requirements Satisfied
+
+✅ **Exigence 11.1**: Hybrid detection (regex + NER) implemented  
+✅ **Exigence 11.2**: PII excluded from logs via `filter_logs()`  
+✅ **Exigence 11.3**: PII excluded from error messages (via anonymization)  
+✅ **Exigence 5.10**: Audit logs maintained without PII exposure  
+
+### Key Features
+
+1. **Flexible Regex Patterns**
+   - Handles multiple date formats (slash, dash, ISO, text)
+   - Detects NSS with/without spaces
+   - Supports various phone number formats
+   - Postal codes and street addresses
+
+2. **Smart Name Detection**
+   - Context-based detection ("Patient", "M.", "Mme", etc.)
+   - Optional NER integration with spaCy
+   - Handles composite names (Jean-Pierre, Dupont-Martin)
+
+3. **Robust Anonymization**
+   - Replaces PII with clear placeholders ([NOM_ANONYMISÉ], [NSS], etc.)
+   - Preserves text structure
+   - Handles multiple PII types in same text
+
+4. **Conservative Approach**
+   - High recall to minimize PII leaks
+   - Accepts false positives as acceptable trade-off
+   - Comprehensive pattern coverage
+
+### Files Created
+
+1. `src/pipeline_mco_pmsi/processors/pii_protector.py` (400+ lines)
+   - PIIProtector class
+   - PIISpan dataclass
+   - Comprehensive regex patterns
+   - NER integration support
+
+2. `src/pipeline_mco_pmsi/processors/__init__.py`
+   - Module exports
+
+3. `tests/test_pii_protector.py` (450+ lines)
+   - 38 unit tests
+   - 4 test classes covering different aspects
+   - Edge case testing
+
+### Optional Tasks (Not Implemented)
+
+- **Task 3.2**: Property-based tests (marked as optional)
+- **Task 3.3**: Additional unit tests for edge cases (marked as optional)
+
+These can be implemented later if needed for more exhaustive testing.
+
+### Next Steps
+
+The PII Protector is now ready to be integrated into the pipeline:
+- Can be used by the Audit Logger to filter logs
+- Can be used to anonymize clinical text before export
+- Can be used to validate that no PII leaks into system outputs
+
+### Usage Example
+
+```python
+from pipeline_mco_pmsi.processors import PIIProtector
+
+# Initialize protector
+protector = PIIProtector(use_ner=False)  # or True to enable NER
+
+# Detect PII
+text = "Patient Jean Dupont, né le 15/03/1960, NSS 1 60 03 75 123 456 78"
+pii_spans = protector.detect_pii(text)
+
+# Anonymize text
+anonymized = protector.anonymize_text(text)
+# Result: "Patient [NOM_ANONYMISÉ], né le [DATE_NAISSANCE], NSS [NSS]"
+
+# Filter logs
+log = "ERROR: Patient Jean Dupont - traitement échoué"
+filtered = protector.filter_logs(log)
+# Result: "ERROR: Patient [NOM_ANONYMISÉ] - traitement échoué"
+
+# Check for PII
+has_pii = protector.has_pii(text)  # Returns True
+```
+
+## Conclusion
+
+Task 3.1 successfully completed with a robust, well-tested PII protection system that meets all specified requirements. The implementation follows the conservative approach specified in the requirements, prioritizing high recall to prevent PII leaks.