Fortra's Alert Logic MDR vs Splunk User Behavior Analytics comparison

Alert Logic specializes in log management, security configuration, SIEM, SaaS monitoring, network protection, and MDR services. It is suitable for sectors such as utilities, financial services, and government.

Alert Logic offers extensive log management and proactive security by centralizing logs, providing detailed notifications, and detecting intrusions. With global threat visibility and SOCs in the US and Europe, it speeds up the threat detection process in seconds. Advanced technologies, like AI and ML, improve its MDR services and differentiate legitimate user behavior from threats.

• Excellent log messages for seamless log management
• Immediate log retrieval during production issues
• Clear, user-friendly notifications
• Proactive and detailed incident notifications
• Centralized dashboard for easy monitoring
• Good intrusion detection and heuristic threat monitoring
• Global threat visibility with fast threat detection
• Human expertise backing threat detection and response

• Streamlined log management and rapid issue resolution
• Centralized monitoring for efficient incident management
• Fast global threat detection improves security stance
• Human expertise for accurate threat response
• Advanced threat differentiation using AI and ML

Alert Logic proves beneficial for various industries, with e-commerce applications utilizing it for robust log management and security measures. Utility sector companies leverage its network protection and threat detection capabilities, while financial services organizations appreciate its MDR services. Government agencies rely on its comprehensive infrastructure monitoring and SOC expertise for enhanced security.

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.