A UBA solution powered by machine learning enables security teams to model normal behavior and track subtle changes in user activity to identify malicious insiders.
To defend their confidential data from increasingly sophisticated cybercriminals, security teams must leverage machine learning to perform analytical tasks that are too tedious for humans to complete.
Security professionals need a comprehensive way to analyze user behavior, automatically respond to reports of suspicious activity and manage user access accordingly to thwart insider threats.
Security analysts need access to deep network insights in the form of user behavior analytics to unlock the full potential of technologies such as artificial intelligence and machine learning.
Unified identity and access management solutions from IBM enable security teams to silently protect their networks without disrupting the user experience.
IBM QRadar offers all the bells and whistles — and cowbell — security teams need to enhance their SIEM capabilities without paying an arm and a leg.
SIEM technology has evolved drastically over the past decade and will continue to mature as the threat landscape shifts and customers' needs change.
SIEM has evolved from its comparatively humble beginnings to integrate user behavior analysis and log management capabilities that are critical to any SOC.
From Suspicious Activity to Suspended Account in Less Than a Minute: Stopping Insider Threats With Automation
To protect corporate data from insider threats, security teams should leverage cognitive-enabled UBA and IGI solutions to measure users' risk scores.
To effectively manage and remediate insider threats, the CISO must establish a comprehensive approach to governance, data analysis and incident response.