Corelight Open NDR vs OpenUBA

Corelight Open NDR turns network and cloud traffic into evidence for security operations. Its detection combines machine learning, behavioral analytics, curated signatures and threat intelligence, with real-time detection and anomaly explanations. Automated triage and integrated response workflows provide context for containing threats. The platform brings network security monitoring, intrusion detection, static file analysis and intelligent packet capture together. It handles network traffic, packets, flow logs, cloud network traffic, protocol logs and files. Deployment options include physical, software, virtual, cloud and flow-log sensors across on-premises, air-gapped, hybrid and multicloud environments. Cloud-native sensors support AWS, Azure and Google Cloud; software sensors support Linux-based systems. Physical and software sensors are built for environments at 100+ Gbps scale. Native integrations cover SIEM, XDR, SOAR and cloud platforms, including Splunk, CrowdStrike, Microsoft, Elastic and AWS. A Logs API and MCP server support programmatic access. Pricing is on request, with capacity-based licensing. Corelight serves large enterprises and government agencies in more than fifteen countries.

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.