Qualys VMDR vs Splunk User Behavior Analytics comparison

Vulnerability Management, Detection, and Response (VMDR) is a cornerstone product of the Qualys TruRisk Platform and a global leader in the enterprise-grade vulnerability management (VM) vendor space. With VMDR, enterprises are empowered with visibility and insight into cyber risk exposure - making it easy to prioritize vulnerabilities, assets, or groups of assets based on business risk. Security teams can take action to mitigate risk, helping the business measure their actual risk exposure over time. 

Qualys VMDR offers an all-inclusive risk-based vulnerability management solution to prioritize vulnerabilities and assets based on risk and business criticality. VMDR seamlessly integrates with configuration management databases (CMDB), Qualys Patch Management, Custom Assessment and Remediation (CAR), Qualys TotalCloud and other Qualys and non-Qualys solutions to facilitate vulnerability detection and remediation across the entire enterprise.

With VMDR, users are empowered with actionable risk insights that translate vulnerabilities and exploits into optimized remediation actions based on business impact. Qualys customers can now aggregate and orchestrate data from the Qualys Threat Library, 25+ threat intelligence feeds, and third-party security and IT solutions, empowering organizations to measure, communicate, and eliminate risk across on-premises, hybrid, and cloud environments.

Splunk User Behavior Analytics is a behavior-based threat detection is based on machine learning methodologies that require no signatures or human analysis, enabling multi-entity behavior profiling and peer group analytics for users, devices, service accounts and applications. It detects insider threats and external attacks using out-of-the-box purpose-built that helps organizations find known, unknown and hidden threats, but extensible unsupervised machine learning (ML) algorithms, provides context around the threat via ML driven anomaly correlation and visual mapping of stitched anomalies over various phases of the attack lifecycle (Kill-Chain View). It uses a data science driven approach that produces actionable results with risk ratings and supporting evidence that increases SOC efficiency and supports bi-directional integration with Splunk Enterprise for data ingestion and correlation and with Splunk Enterprise Security for incident scoping, workflow management and automated response. The result is automated, accurate threat and anomaly detection.