eyeCloudXOAR UEBA vs OpenUBA

eyeCloudXOAR UEBA is SecuLayer software that uses AI to analyze behavior by users, devices, and systems to identify insider threats and advanced attacks. It learns normal user patterns and looks for anomalies in real time, including unusual access times and large-scale data exfiltration. Behavioral risk scores help analysts prioritize higher-risk users and groups. The product collects and normalizes information from more than 40 sources, including HR, business systems, and appliances, then correlates individual events into broader threat scenarios to reduce false positives. Approval-linked workflows handle requests, approvals, result updates, and reminders for open items. Alerts can appear persistently on user PCs through a NAC Agent or reach collaboration group chats, with Webex named as an example. A report builder schedules anomaly and compliance reports; DRM integration can encrypt downloaded reports. The C-level dashboard displays Normal, Caution, and Critical threat levels alongside clearance, ticket, and scenario-event information. A trial is available by request, followed by an expert-arranged demo. SecuLayer was founded in February 2012 and is based in Seoul, South Korea.

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.