OpenUBA vs Security Vision UEBA

OpenUBA is a free, open-source framework for user and entity behavior analytics in security analytics. Users can install, train, and run models, track versions and artifacts, and use feedback and shadow modes. Listed model frameworks include scikit-learn, PyTorch, TensorFlow, Keras, NetworkX, and Spark MLlib. A visual flow-based rule builder combines model outputs with logical conditions; alerts can link to anomalies and cases. Data loaders cover local CSV, Elasticsearch, Spark, and aggregated multi-source loading. The project describes a Kubernetes-native architecture, with containerized components for development Kind clusters or production Kubernetes clusters. It provides REST endpoints, a GraphQL API, and a Python SDK for model registration, job submission, and visualization. Access controls listed in the README include JWT authentication, four roles, per-page read and write permissions, and audit logging. Model training and inference use isolated Docker containers or ephemeral Kubernetes Jobs with resource limits. The built-in assistant supports local Ollama and cloud APIs from OpenAI, Claude, and Gemini. The project identifies itself as beta, and its listed release is v0.0.2 as a pre-release.

Security Vision UEBA builds behavioral models for users, accounts, devices, hosts and processes, then detects deviations in activity and traffic. Its analysis combines correlation rules, statistical methods and machine learning to identify suspicious behavior and incidents. It can analyze raw data from data lakes, SIEM, next-generation firewalls, proxy servers, and Windows or Linux devices. Configured connectors support SIEM systems, Cisco, CheckPoint, Palo Alto and Juniper network devices, Squid and Blue Coat proxies, Kafka, Elasticsearch, and Windows and Linux logs. Users can add or adapt Sigma and correlation rules with a correlation rule editor. UEBA can enrich incident data and send events or incidents to SOAR or SIEM. Response actions can include adding entries to NGFW blocklists or SIEM active lists. Its API provides alarms, suspicious events and object alerts. Deployment is on premises; the maker says its machine-learning models run on customer infrastructure without sending data to the cloud. It is paid with individual pricing, and license type and support level affect cost.