Professional-grade cybersecurity intelligence platform that leverages AI to automatically collect, analyze, and classify security vulnerabilities and threat intelligence from multiple international sources in real-time.
Sentinel Intelligence is a full-stack web application that demonstrates advanced software engineering practices including:
- AI/ML Integration with OpenAI GPT-4 and LangChain for intelligent content classification
- Real-time Data Processing with automated web scraping and API integration
- Production-Ready Architecture with comprehensive error handling and monitoring
- Scalable Backend built with FastAPI and PostgreSQL
- Modern Frontend using Next.js 15, React 19, and TypeScript
- DevOps & Deployment with automated CI/CD and cloud infrastructure
- Framework: FastAPI with async/await support and automatic API documentation
- AI/ML: OpenAI GPT-4 integration via LangChain for intelligent content classification
- Database: PostgreSQL with Supabase integration and SQLAlchemy ORM
- Web Scraping: BeautifulSoup4, FeedParser, and custom scrapers for multiple data sources
- Task Scheduling: Custom cron scheduler with dual-mode operation (testing/production)
- Rate Limiting: Intelligent rate limiting with exponential backoff
- Email Notifications: SMTP integration with HTML templating and conditional notifications
- Framework: Next.js 15 with App Router and Turbopack
- UI Library: React 19 with modern hooks and functional components
- Styling: Tailwind CSS v4 with responsive design
- Type Safety: Full TypeScript implementation with strict type checking
- Icons: Lucide React for consistent iconography
- Deployment: Vercel integration with automatic deployments
- Cloud Platform: Render.com with optimized free-tier configuration
- Database: Supabase PostgreSQL with real-time subscriptions
- Monitoring: Comprehensive logging, health checks, and status endpoints
- CI/CD: Automated deployment with build scripts and environment management
- Security: Environment variable management and API key security
- Automated Classification: GPT-4-powered categorization of security vulnerabilities
- Threat Assessment: Intelligent severity scoring and risk analysis
- Content Summarization: AI-generated summaries of security articles and CVE data
- Multi-Source Integration: Unified intelligence from RSS feeds, CVE databases, and security blogs
- Automated Scraping: Scheduled collection from multiple security sources including English, Chinese, and Russian sources
- Data Normalization: Consistent formatting across multiple data sources
- Duplicate Detection: Intelligent deduplication and content matching
- Historical Analysis: Trend analysis and pattern recognition
- Dual-Mode Scheduling: Testing (30-min) and production (3-day) cycles
- Comprehensive Monitoring: Health checks, status endpoints, and detailed logging
- Error Handling: Robust error recovery with retry logic and notifications
- Performance Optimization: Rate limiting, caching, and efficient database queries
- Scalability: Horizontal scaling support with load balancing considerations
- Async/Await Architecture: Full asynchronous implementation with FastAPI
- Custom Rate Limiting: Intelligent rate limiting with exponential backoff
- WebSocket Integration: Real-time communication for live updates
- Agent-Based Processing: LangChain-powered intelligent agent system
- Custom Hooks: TypeScript-powered state management and data fetching
- Component Composition: Proper typing and reusable component architecture
- Performance Optimization: Code splitting and efficient rendering
- Responsive Design: Mobile-first approach with Tailwind CSS
- Optimized Schema: Proper indexing and relationship design
- Connection Pooling: Efficient database connection management
- Data Migration: Automated schema updates and data integrity
- Query Optimization: Efficient data retrieval and processing
- Response Time: Optimized API endpoints with async processing
- Concurrent Processing: Handles multiple simultaneous requests efficiently
- Data Processing: Processes multiple security sources simultaneously
- Database: Optimized queries with proper indexing and connection pooling
- Caching: Intelligent caching strategies for frequently accessed data
- Monitoring: Real-time performance metrics and alerting
- Render.com: Optimized for free-tier with automatic scaling
- Environment Management: Comprehensive configuration management
- Health Monitoring: Automated health checks and status reporting
- Log Management: Structured logging with separate test/production logs
- Error Tracking: Comprehensive error reporting and notification system
- Version Control: Git with feature branching and pull requests
- Code Quality: ESLint, TypeScript strict mode, and Python type hints
- Testing: Automated testing suite with environment validation
- Documentation: Comprehensive API docs and implementation guides
- CI/CD: Automated build and deployment pipelines
- Python 3.8+, FastAPI, async/await programming
- PostgreSQL, SQLAlchemy, database design and optimization
- Web scraping, API integration, and data processing
- AI/ML integration with OpenAI and LangChain
- Task scheduling, cron jobs, and background processing
- Next.js 15, React 19, TypeScript 5.0
- Tailwind CSS, responsive design, and modern UI/UX
- State management, custom hooks, and component architecture
- Performance optimization and code splitting
- Cloud deployment (Render.com, Vercel)
- Environment management and configuration
- Monitoring, logging, and error handling
- CI/CD pipelines and automated deployment
- Performance optimization and scaling
- Microservices architecture with clear separation of concerns
- RESTful API design with comprehensive documentation
- Database design and optimization
- Error handling and fault tolerance
- Security best practices and API key management
sentinel-intelligence/
├── backend/ # FastAPI backend
│ ├── main.py # Main application and API endpoints
│ ├── agent.py # AI agent and LangChain integration
│ ├── db.py # Database models and operations
│ ├── cron_scheduler.py # Custom cron job scheduler
│ ├── rate_limiter.py # Intelligent rate limiting
│ ├── classify.py # AI classification logic
│ ├── models.py # Data models and schemas
│ ├── config.py # Configuration management
│ ├── db_cleanup.py # Database maintenance
│ ├── schema.sql # Database schema
│ ├── scrapers/ # Web scraping modules
│ │ ├── chinese_scrape.py
│ │ ├── english_scrape_with_vulners.py
│ │ └── russian_scrape.py
│ ├── tools/ # LangChain tools
│ └── utils/ # Utility functions
├── frontend/ # Next.js frontend
│ ├── src/app/ # React components and pages
│ ├── package.json # Dependencies and scripts
│ └── vercel.json # Vercel deployment config
├── render.yaml # Infrastructure as code
├── requirements.txt # Python dependencies
├── build.sh # Build script
├── start_local.sh # Local development script
└── README.md # This file
- Python 3.8+
- Node.js 18+
- PostgreSQL database
- OpenAI API key
# Backend setup
cd backend
python -m venv venv
source venv/bin/activate # On Windows: venv\Scripts\activate
pip install -r requirements.txt
python main.py
# Frontend setup
cd frontend
npm install
npm run dev- Implementation Summary - Technical implementation details
- Production Deployment Plan - Deployment strategies
- API Documentation - FastAPI auto-generated docs
- Database Schema - Database structure
This project demonstrates professional software engineering practices including:
- Clean code architecture and design patterns
- Comprehensive error handling and logging
- Production-ready deployment and monitoring
- Modern development tools and workflows
- Performance optimization and scalability considerations
This project is for demonstration purposes and showcases advanced software engineering skills in cybersecurity, AI/ML, and full-stack development.
Built with ❤️ using modern technologies and best practices
Perfect for showcasing advanced software engineering skills, AI/ML integration, and production-ready application development to potential employers.