Secure AI Data Pipeline Platform

A comprehensive cloud security analysis platform that ingests multi-cloud resource data and analyzes security risks using AI-powered threat detection.

🚀 Features

• Multi-Cloud Data Ingestion: Automated data collection from AWS, GCP, and Azure using CloudQuery
• AI-Powered Security Analysis: Advanced threat detection using OpenAI GPT-4 and secure prompt templates
• Data Sanitization Pipeline: PII removal and differential privacy for secure AI processing
• Interactive Dashboard: Modern React/Next.js interface
• Risk Scoring: 0-10 risk assessment with contextual analysis
• Compliance Monitoring: PCI DSS, SOC 2, ISO 27001, HIPAA, GDPR support

📦 Quick Start

Prerequisites

• Docker and Docker Compose
• Node.js 18+ (for local development)
• Python 3.11+ (for local development)
• Cloud provider credentials (AWS, GCP, Azure)

1. Clone and Setup

git clone <repository-url>
cd secure-ai-data-pipelines

# Copy environment configuration
cp env.example .env

# Edit .env with your configuration
nano .env

2. Configure Environment

Update .env with your settings:

# Database
DATABASE_URL=postgresql://cloudquery:secure_password@localhost:5432/cloudquery_security

# AI API Keys
OPENAI_API_KEY=sk-your-openai-api-key-here

# Cloud Provider Credentials
AWS_ACCESS_KEY_ID=your-aws-access-key
AWS_SECRET_ACCESS_KEY=your-aws-secret-key
GCP_PROJECT_ID=your-gcp-project-id
AZURE_SUBSCRIPTION_ID=your-azure-subscription-id

# Security
SECRET_KEY=your-super-secret-jwt-key-here
ENCRYPTION_KEY=your-32-byte-encryption-key-here

3. Start the Platform

# Start all services
docker-compose up -d

# Check service status
docker-compose ps

# View logs
docker-compose logs -f backend

4. Initialize Data Sync

# Run initial CloudQuery sync for each cloud provider
docker-compose run --rm cloudquery sync /configs/aws.yml
docker-compose run --rm cloudquery sync /configs/gcp.yml
docker-compose run --rm cloudquery sync /configs/azure.yml

5. Access the Dashboard

• Frontend: http://localhost:3000

🏗️ Architecture

The platform processes cloud security data through an automated pipeline:

CloudQuery Sync → PostgreSQL → Background Workers → AI Analysis → Dashboard

Key components include CloudQuery for multi-cloud data collection, PostgreSQL for storage, background workers for processing, AI analysis for security assessment, and a React dashboard for visualization.

🛠️ Troubleshooting

For detailed troubleshooting information, see TROUBLESHOOTING.md.

🤝 Contributing

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

Built with ❤️ by CloudQuery