Exabeam Advanced Analytics
The world’s most-deployed UEBA security solution – Modern threat detection using behavioral modeling and machine learning.
Complex Threat Identification with Behavioral Analysis
Cyberattacks are becoming more complex and harder to find. Often correlation rules can’t find the attacks because they lack context or miss incidents they’ve never seen — generating false negatives. Correlation rules also require significant maintenance. Advanced Analytics, Exabeam’s UEBA security solution, automatically detects the behaviors indicative of a threat. It fully integrates with Exabeam Threat Intelligence Services (TIS) to provide real-time actionable intelligence into potential threats in your environment by uncovering indicators of compromise (IOC) and malicious hosts.
Prebuilt Timelines Automatically Reconstruct Security Incidents
Analysts shouldn’t spend days or weeks gathering evidence and constructing timelines of incidents by querying and pivoting through their SIEM. With Advanced Analytics, a prebuilt-incident timeline flags anomalies and displays details of the incident for the full scope of the event and its context. Now analysts can stop spending time combing through raw logs to investigate incidents. What took weeks to investigate in a legacy SIEM can now be done in seconds with our UEBA security solution.
Customizable Case Management Designed for Security Teams
Managing SOC operations is expensive – it involves organizing resources and prioritizing incidents, in addition to investigating and mitigating those that impact your business. Another pain point is lack of skilled analysts to triage and prioritize incidents. The time required to quickly resolve incidents affects your bottom line. With Exabeam’s UEBA security solution, you can automate these tasks, to decrease mean time to resolution (MTTR), allowing your already stretched security staff to do more in less time. Exabeam Case Manager is fully integrated into Advanced Analytics enabling you to optimize analyst workflow and ensuring that no threats slip through the cracks.
Dynamic Peer Grouping
User behavior patterns often differ based on a myriad of attributes, including: the team they are on, what projects they are involved in, where they are located, and more. Thus, behavioral baselines shouldn’t be static. Dynamic peer grouping uses machine learning to assign users to groups based on their behavior, then to compare their activity against that of those groups to identify anomalous, risky behavior.
Lateral movement is a method attackers use to move through a network by using IP addresses, credentials, and machines in search of key assets. Tracking is difficult because the trace information only tells part of the story. Data must be analyzed from everywhere, linking the attack to the source. The Advanced Analytics patented technology tracks suspected activities even if there are changes to devices, IP addresses, or credentials.
Lateral Movement Detection
Lateral movement is a method attackers use to move through a network by using IP addresses, credentials, and machines in search of key assets. Tracking is difficult because the trace information only tells part of the story. Data must be analyzed from everywhere, linking the attack to the source. The Advanced Analytics patented technology tracks suspected activities even if there are changes to devices, IP addresses, or credentials.
