gdpr-safe-rag 0.1.0

Production-grade Python toolkit for building GDPR-compliant RAG systems with automatic PII detection, audit trails, and compliance validation

GDPR Safe RAG

Production-grade Python toolkit for GDPR-compliant RAG (Retrieval-Augmented Generation) systems with automatic PII detection, audit trails, and compliance validation.

Features

• PII Detection: Automatic detection of personal identifiable information with support for UK, EU, and common patterns
• Redaction Strategies: Multiple redaction approaches (token, hash, category) for different use cases
• Audit Logging: Comprehensive audit trails with PostgreSQL or SQLite backends
• Compliance Checking: Built-in GDPR compliance validation with detailed reports
• Async-First: Full async/await support for modern Python applications
• Type Safe: Complete type hints with mypy strict mode compatibility

Quick Start

Installation

pip install gdpr-safe-rag

Or install from source:

git clone https://github.com/Charlescifix/gdpr-safe-rag.git
cd gdpr-safe-rag
pip install -e .

Basic PII Detection

from gdpr_safe_rag import PIIDetector

# Initialize detector with UK patterns
detector = PIIDetector(region="UK", detection_level="strict")

# Sample text with PII
text = """
Contact John Smith at [e-mail] or call 07700 900123.
His NHS number is 123 456 7890.
"""

# Detect PII
items = detector.detect(text)
for item in items:
    print(f"{item.type}: {item.value} (confidence: {item.confidence:.2f})")

# Redact PII
result = detector.redact(text)
print(result.redacted_text)
# Output: Contact John Smith at [EMAIL_1] or call [PHONE_1].
#         His NHS number is [NHS_NUMBER_1].

# Get mapping for potential restoration
print(result.mapping)
# Output: {'[EMAIL_1]': '[e-mail]', ...}

Audit Logging

import asyncio
from gdpr_safe_rag import AuditLogger

async def main():
    async with AuditLogger(storage_path="audit.db") as logger:
        # Log document ingestion
        await logger.log_ingestion(
            document_id="doc-001",
            user_id="admin",
            pii_detected=True,
            pii_count=5,
        )

        # Log user query
        await logger.log_query(
            user_id="user-123",
            query_text="What is the company policy?",
            retrieved_docs=["doc-001", "doc-002"],
        )

        # Log data deletion (right to erasure)
        await logger.log_deletion(
            user_id="user-456",
            resource="user-456-data",
            reason="user_request",
        )

        # Export compliance report
        from datetime import datetime, timedelta
        report = await logger.export_compliance_report(
            start_date=datetime.now() - timedelta(days=30),
            end_date=datetime.now(),
        )
        print(report)

asyncio.run(main())

Compliance Checking

import asyncio
from gdpr_safe_rag import ComplianceChecker, AuditLogger

async def main():
    # Sample documents with metadata
    documents = [
        {
            "id": "doc-001",
            "created_at": datetime.now() - timedelta(days=30),
            "pii_detected": True,
            "pii_count": 5,
        },
    ]

    async with AuditLogger(storage_path="audit.db") as logger:
        checker = ComplianceChecker(retention_days=2555)
        report = await checker.run_all_checks(
            documents=documents,
            audit_logger=logger,
        )

        print(report.to_text())
        # Shows detailed compliance status with remediation suggestions

asyncio.run(main())

Supported PII Types

UK Patterns

• Email addresses
• Phone numbers (mobile and landline)
• UK postcodes
• NHS numbers (with checksum validation)
• National Insurance numbers
• Credit card numbers (with Luhn validation)
• IBAN (with modulo 97 validation)

EU Patterns

• Email addresses
• Phone numbers
• Credit card numbers
• IBAN

Configuration

Configuration can be set via environment variables or .env file:

# Database
DATABASE_URL=postgresql+asyncpg://user:pass@localhost:5432/gdpr_rag

# PII Detection
PII_DETECTION_LEVEL=strict  # strict, moderate, lenient

# Audit Settings
AUDIT_RETENTION_DAYS=2555  # ~7 years

Development

Setup

# Clone repository
git clone https://github.com/Charlescifix/gdpr-safe-rag.git
cd gdpr-safe-rag

# Install development dependencies
pip install -e ".[dev]"

# Start PostgreSQL for testing (optional)
docker-compose up -d postgres

Running Tests

# Run all tests
pytest tests/ -v

# Run with coverage
pytest tests/ -v --cov=gdpr_safe_rag --cov-report=html

# Run type checking
mypy gdpr_safe_rag --strict

# Run linting
ruff check gdpr_safe_rag
black --check gdpr_safe_rag

Running Examples

# PII Detection (no database needed)
python examples/basic_pii_detection.py

# Audit Logging
python examples/audit_logging_postgres.py

# Compliance Check
python examples/compliance_check.py

Architecture

gdpr_safe_rag/
├── pii_detector/        # PII detection and redaction
│   ├── detector.py      # Main PIIDetector class
│   ├── patterns/        # Pattern definitions by region
│   ├── redactor.py      # Redaction strategies
│   └── validators.py    # Checksum validators
├── audit_logger/        # Audit trail functionality
│   ├── logger.py        # Main AuditLogger class
│   ├── backends/        # Storage backends (PostgreSQL, SQLite, Memory)
│   └── exporters.py     # Report export functionality
└── compliance_checker/  # GDPR compliance validation
    ├── checker.py       # Main ComplianceChecker class
    └── checks/          # Individual compliance checks

License

MIT License - see LICENSE for details.

Contributing

Contributions are welcome! Please read our contributing guidelines and submit pull requests.

Support

• GitHub Issues: Report bugs or request features
• Documentation: Full documentation