User Risk Scoring from Security Posture and Activity Patterns
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Solution Overview
Problem
Existing systems lack effective methods to determine user risk based on posture and activity, particularly in complex network environments, which can lead to security vulnerabilities and compliance issues.
Innovation Solution
A data platform is utilized to collect and analyze user data through agents deployed on compute assets, generating polygraphs that model user behavior and detect deviations from typical patterns, enabling real-time anomaly detection and risk assessment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If user risk determination is performed based on posture and activity data, then security monitoring capability is improved, but system complexity increases
Solution Approach 1:
The system segments the risk assessment process into multiple independent components: data collection agents running on compute assets, a centralized data platform for processing, and risk determination modules that analyze specific posture and activity parameters. This segmentation allows each component to be optimized independently and reduces overall system complexity by distributing functionality.
Solution Approach 2:
The data platform acts as an intermediary between the compute assets (where agents collect data) and the risk determination logic. This intermediary layer consolidates and pre-processes posture and activity data, reducing the complexity of real-time risk assessment by filtering and preparing data before it reaches the analysis modules.
2Speed
If real-time anomaly detection is implemented, then threat response time is improved, but computational resources consumed increases
Solution Approach 1:
The system performs preliminary actions by collecting and storing posture and activity data in advance, and by pre-processing this data in the data platform. Risk determination modules then operate on this pre-prepared data, reducing the computational burden during real-time threat response while maintaining fast detection speeds.
Solution Approach 2:
The system applies partial action by selectively analyzing only the most relevant posture and activity parameters for risk assessment, rather than processing all possible data. This targeted approach enables real-time anomaly detection while reducing computational resource consumption by focusing calculations on critical security indicators.
3Measurement precision
If comprehensive user behavior modeling is performed, then risk assessment accuracy is improved, but data processing time increases
Solution Approach 1:
The behavior modeling process is segmented into discrete analytical modules that process different aspects of user activity independently. Each module analyzes specific behaviors or posture parameters, and results are aggregated to form the comprehensive risk assessment. This segmentation enables accurate modeling while reducing overall processing time through parallel computation.
Solution Approach 2:
The system maintains continuous collection and processing of user behavior data, allowing risk assessment models to be updated in real-time without interrupting operations. This continuous action enables accurate risk evaluation while minimizing data processing time by working with continuously available data streams rather than batch processing historical data.
Data Source
AI summary
Determining user risk based on user posture and activity, including: determining a risk score for a user based on a security posture associated with the user and historical activity associated with the user; and controlling access by the user to one or more resources based on the risk score.