OpenSearch vs OpenUBA

OpenSearch is a free, community-driven, open-source suite for ingesting, searching, visualizing, and analyzing data. It includes a data store and search engine, OpenSearch Dashboards, and the Data Prepper server-side collector. Search functions include full-text and field queries, multi-index search, field boosting, score-based ranking, sorting, and aggregations. The project identifies search, observability, and security analytics as application areas, including log analysis, threat intelligence, and event correlation. Listed machine-learning and AI capabilities include vector search and anomaly detection. OpenSearch can be deployed on premises or in hybrid and multicloud environments. Its Apache License 2.0 permits use, modification, extension, embedding, monetization, and resale. It is compatible with Beats, Fluent Bit, and Fluentd for ingestion and processing. A read-only ODBC driver for Windows and macOS connects tools such as Tableau, Microsoft Excel, and Power BI to the SQL plugin. Dashboards provides visualizations and an interface for plugins including security, alerting, Index State Management, and SQL. The downloads page warns that minimal distributions lack security features and are intended for carefully controlled environments or embedding in another service.

OpenUBA is a free, open-source User and Entity Behavior Analytics framework for security analytics. It analyzes behavior with machine-learning models to identify anomalies, assign risk scores, and produce classifications. The dashboard covers entity monitoring, risk trends, job orchestration, case management, and real-time behavioral baselines for tracking 10,000+ users. Data ingestion supports Spark, Elasticsearch, and CSV, and the project describes the platform as SIEM agnostic. Rule Studio provides a visual flow-graph builder for detection logic, while matching rules can trigger real-time alerts. Cases can be triaged, escalated, and resolved in built-in workflows. Models use a model.yaml configuration and MODEL.py interface with train() and infer() methods; examples use scikit-learn, TensorFlow, PyTorch, Keras, and NetworkX. A Kubernetes-native operator runs jobs in ephemeral containers, with GraphQL logs and monitoring. Deployment is self-hosted on Kind or production Kubernetes clusters.