IntelOwl is a production-grade, open-source threat intelligence platform that automates the analysis of observables (IPs, domains, URLs, hashes) and files (PE, PDF, APK, etc.) through a pluggable plugin system. The backend is built with Django REST Framework, Celery for asynchronous task execution, and Django Channels for real-time WebSocket updates. The frontend is a React single-page application using react-router, react-flow for pipeline visualizations, and Zustand for state management. The system supports multi-tenancy, organizations, role-based access, and integrates with dozens of external threat intelligence APIs and open-source analysis tools via containerized microservices. The architecture is centered around a plugin framework with five plugin types: analyzers (file and observable), connectors (push results to external systems), visualizers (render reports), pivots (create child jobs from analysis results), and ingestors (pull threat feeds). Playbooks bundle these plugins into repeatable analysis workflows. A data model manager normalizes analyzer outputs into structured evaluations with reliability scores, and a user event system allows manual verdict overrides with decay logic. The platform also includes an AI chatbot assistant powered by a local Ollama LLM and LangGraph, enabling natural-language queries and one-click analysis launches. Key operational features include a comprehensive REST API, token-based authentication with optional OAuth/LDAP/RADIUS, Elasticsearch for BI reporting, and Docker-based deployment with separate containers for specialized analyzers. The codebase is actively maintained with extensive unit and integration tests, CI/CD pipelines, and a large community (4695 stars). While the migration system is somewhat repetitive, the overall architecture is clean, modular, and demonstrates strong software engineering practices.
OpenUBA is a full-stack User and Entity Behavior Analytics (UEBA) platform for security anomaly detection, investigation, and response. It provides a complete lifecycle for ML models (install, train, infer, version, share) via a pluggable registry supporting OpenUBA Hub, GitHub, HuggingFace, and local filesystem. The platform includes a rule engine with a visual DAG-based builder (ReactFlow), case management, real-time dashboards, multi-backend visualization (matplotlib, plotly, bokeh, etc.), and an LLM-powered assistant. The backend is built on FastAPI with PostgreSQL, SQLAlchemy, JWT/RBAC authentication, and optional Spark/Elasticsearch integrations. The frontend uses Next.js 14 (App Router), Apollo Client (GraphQL), shadcn/ui, and Recharts. A Python SDK enables programmatic access to all features. The system runs on Kubernetes with a Kopf operator managing custom resources (UBATraining, UBAInference, UBAWorkspace, UBAPipeline) for containerized model execution in isolated Docker images per ML framework (sklearn, PyTorch, TensorFlow, NetworkX). While primarily an application, it includes research-oriented components like experiment tracking, feature store, and Jupyter notebook workspaces, making it a hybrid tool for both security operations and data science workflows.