PII Detection System

A modern web application for detecting Personally Identifiable Information (PII) in CSV files using AWS Bedrock's Titan model.

Architecture

This application uses a clean separation between backend and frontend:

• Backend: Node.js/Express server with AWS Bedrock integration
• Frontend: React/Next.js with shadcn/ui components
• AI Model: Amazon Titan Text Express v1 via AWS Bedrock for PII classification

Project Structure

PII_detection/
├── backend/
│   ├── app/
│   │   ├── __init__.py
│   │   ├── main.py              # FastAPI application
│   │   ├── models.py            # Pydantic data models
│   │   └── pii_detector.py      # Core PII detection logic
│   ├── data/                    # Test CSV files
│   ├── requirements.txt         # Python dependencies
│   ├── tests/                   # Backend tests
│   └── venv/                    # Virtual environment
├── frontend/
│   ├── src/
│   │   ├── app/                 # Next.js app directory
│   │   ├── components/          # React components
│   │   └── lib/                 # Utility functions
│   ├── package.json             # Node.js dependencies
│   └── tailwind.config.ts       # Tailwind CSS config
└── CLAUDE.md                    # Development instructions

Setup & Installation

Backend Setup

1. Navigate to backend directory:

   cd backend

2. Create and activate virtual environment:

   python -m venv venv
   source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install dependencies:

   pip install -r requirements.txt

4. Configure AWS credentials (choose one method):

   Option A: AWS CLI (Recommended)

   aws configure

   Option B: Environment Variables

   export AWS_ACCESS_KEY_ID=your_access_key
   export AWS_SECRET_ACCESS_KEY=your_secret_key
   export AWS_REGION=us-east-1

   Option C: AWS Profile

   export AWS_PROFILE=your-profile-name

5. Start the backend server:

   uvicorn app.main:app --reload --host 0.0.0.0 --port 8000

Frontend Setup

1. Navigate to frontend directory:

   cd frontend

2. Install dependencies:

   npm install

3. Start the development server:

   npm run dev

Usage

1. Start both servers:

   • Backend: http://localhost:8000
   • Frontend: http://localhost:3000

2. Access the web application at http://localhost:3000

3. Upload a CSV file using the drag-and-drop interface

4. View results showing PII classification for each column with:

   • PII detection status
   • Confidence scores
   • Sample values
   • AI reasoning

API Endpoints

Backend API (Port 8000)

• GET / - Health check
• GET /health - Detailed health status
• POST /analyze-csv - Upload and analyze CSV file
• POST /analyze-column - Analyze individual column

Example API Usage

# Upload CSV for analysis
curl -X POST "http://localhost:8001/analyze-csv" \
  -H "Content-Type: multipart/form-data" \
  -F "file=@your-file.csv"

AWS Requirements

• AWS Account with Bedrock access
• IAM permissions for Bedrock model invocation
• Bedrock model access to anthropic.claude-instant-v1
• Regional availability: Ensure Bedrock is available in your selected region

Security Considerations

This is a defensive security tool designed to help identify PII in datasets. Key security practices:

• Never commit AWS credentials to version control
• Use IAM roles with minimal required permissions
• Process sensitive data in secure environments
• Review detected PII classifications before taking action

Development

Backend Development

cd backend
source venv/bin/activate
uvicorn app.main:app --reload

Frontend Development

cd frontend
npm run dev

Adding New Features

1. Backend: Add new endpoints in app/main.py
2. Frontend: Create new components in src/components/
3. UI Components: Use shadcn/ui for consistent styling

Testing

Backend Tests

cd backend
python -m pytest tests/

Frontend Tests

cd frontend
npm test

Deployment

Backend Deployment

• Deploy as Docker container or serverless function
• Ensure AWS credentials are configured in production environment

Frontend Deployment

• Build: npm run build
• Deploy to Vercel, Netlify, or static hosting

Troubleshooting

Common Issues

1. AWS Authentication Error: Verify AWS credentials and region
2. CORS Issues: Ensure backend allows frontend origin
3. Port Conflicts: Change ports in configuration if needed
4. Model Access: Verify Bedrock model permissions in AWS console

Backend Logs

Check FastAPI logs for detailed error information:

uvicorn app.main:app --log-level debug

Frontend Logs

Check browser console for frontend errors and network issues.