UEBA Risk Scoring With Watchlists for Security Alert Prioritization
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Solution Overview
Problem
Existing cybersecurity measures are inadequate in providing comprehensive protection against sophisticated threats, leading to inefficiencies and vulnerabilities due to the overwhelming number of alerts generated by disparate cybersecurity operations tools, making it challenging to prioritize and manage security threats effectively.
Innovation Solution
Implementing User and Entity Behavioral Analytics (UEBA) in a security analytics platform to compute risk scores for entities based on security data, using watchlists to modify risk scores, and visualizing these scores through graphical user interfaces, thereby enhancing threat detection and response efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple cybersecurity operations tools are deployed to provide comprehensive protection, then security coverage is improved, but the volume of alerts and system complexity increases
Solution Approach 1:
The patent consolidates multiple cybersecurity operations tools into a unified security analytics platform that aggregates data from various sources (SIEM, SOAR, IDS, IPS, antivirus, endpoint protection, vulnerability management) into a single system for centralized analysis and management, reducing operational complexity while maintaining comprehensive security coverage
Solution Approach 2:
The security analytics platform is designed as a multi-functional system that performs multiple security operations including threat detection, risk scoring, entity behavior analysis, alert management, and incident response coordination within a single integrated platform, eliminating the need for separate specialized tools
2Reliability
If multiple cybersecurity operations tools are deployed to detect sophisticated threats, then threat detection capability is improved, but the quantity of alerts generated increases
Solution Approach 1:
The patent extracts and isolates critical security signals from the vast amount of generated alerts by applying UEBA analytics and risk scoring mechanisms, filtering out false positives and low-priority events to deliver only the most significant threats requiring attention
Solution Approach 2:
The system changes the parameter of alert prioritization by computing risk scores based on multiple factors including entity behavior patterns, threat intelligence data, and contextual analysis, transforming the flat alert list into a prioritized ranking that reflects actual threat severity
3Reliability
If the volume of security threats is increased to cover all possible attacks, then security comprehensiveness is improved, but the difficulty of analyzing and acting on threats increases
Solution Approach 1:
The patent replaces manual mechanical analysis of security threats with automated UEBA analytics engines that use machine learning algorithms, behavioral pattern recognition, and risk scoring computations to automatically analyze, prioritize, and respond to security threats, dramatically reducing the human effort required
4Reliability
If traditional cybersecurity measures are used to protect against sophisticated threats, then basic security is maintained, but comprehensive protection is insufficient
Solution Approach 1:
The security analytics platform implements dynamic security measures that continuously adapt to evolving threats by learning from historical data, updating entity behavior baselines in real-time, and adjusting risk scoring algorithms to respond to new attack patterns and tactics
Solution Approach 2:
The system incorporates feedback mechanisms where security events, alert outcomes, and threat intelligence data are continuously fed back into the UEBA analytics models to refine and improve detection accuracy, entity behavior understanding, and risk assessment capabilities over time
Data Source
AI summary
A system and method for implementing user and entity behavioral analytics (UEBA) in a cybersecurity analytics platform. An example method includes receiving, by one or more processing devices of a security analytics platform, security data associated with a specified entity; generating, based on at least a subset of the security data, one or more security signals associated with the specified entity and occurring within a specified time window; computing, for each security signal of the one or more security signals, a respective risk score associated with the specified time window; computing, by aggregating risk scores associated with the one or more security signals, a risk score associated with the specified entity for the specified time window; and modifying, based on an attribute of a security watchlist associated with the specified entity, the risk score of the specified entity.