Anomali vs Splunk User Behavior Analytics comparison

Anomali delivers advanced threat intelligence solutions designed to enhance security operations by providing comprehensive visibility into threats and enabling real-time threat detection and management.

Anomali stands out in threat intelligence, offering an innovative platform that integrates data to identify and analyze threats effectively. It enables teams to streamline threat detection processes and respond to incidents with increased agility. With a focus on accuracy and efficiency, Anomali supports cybersecurity professionals in making informed decisions to safeguard their networks consistently.

• ThreatStream: A platform that aggregates threat intelligence feeds to enhance threat assessment.
• Match: An automated detection feature that matches internal log data against threat intelligence insights.
• Link: Provides graphical visualization for threat analysis, helping to understand threat vectors better.
• STAXX: A lightweight tool for collecting and analyzing open-source threat intelligence.

• Improved Threat Detection: Streamlined processes lead to faster identification and response to threats.
• Resource Efficiency: Automation reduces manual tasks, allowing security teams to focus on strategic activities.
• Better Decision Making: Provides actionable insights that enable more informed security strategies.

In industries like finance and healthcare, Anomali is implemented to address specific challenges like compliance and data protection. By using this platform, organizations gain the ability to adapt to evolving threats, ensuring robust and adaptable security postures tailored to industry demands.

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.