Node Entitlement Risk Scoring via Peer Group Standard Deviation
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Solution Overview
Problem
Existing cybersecurity systems fail to efficiently and accurately quantify and mitigate risks associated with node entitlements in organizational networks, particularly due to cumbersome manual reviews and the lack of comprehensive risk scoring that addresses entitlement management.
Innovation Solution
A method and system for providing risk scores to nodes in a networked system by mapping precalculated inherent risk scores to nodes based on their entitlements, grouping nodes into peer groups, calculating standard deviations, and transforming metrics using a cumulative distribution function to generate threat scores, enabling identification and remediation of threat nodes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If periodic manual reviews of nodes are conducted to identify entitlement-related risks, then security assessment coverage is achieved, but the process becomes cumbersome and inefficient
Solution Approach 1:
The system enables automated self-assessment of node entitlement risks by computing risk scores based on entitlement data and security event data, eliminating the need for manual reviewer intervention while maintaining comprehensive security assessment coverage
Solution Approach 2:
Manual review processes are replaced with an automated computational system that calculates risk scores using entitlement datasets and security event data, substituting human mechanical review with algorithmic processing to improve efficiency while maintaining reliability
2Measurement precision
If extensive lists of entitlements are reviewed manually to compare node profiles, then complete entitlement assessment is achieved, but the process becomes time-consuming and challenging
Solution Approach 1:
The system automatically computes risk scores by processing entitlement datasets and security event data without manual intervention, achieving complete entitlement assessment while eliminating time-consuming manual review processes
Solution Approach 2:
Manual comparison of extensive entitlement lists is replaced with automated computational processing that efficiently analyzes entitlement data and security events to generate comprehensive risk assessments
3Reliability
If existing risk scoring tools rely on security event data and comparisons with member nodes, then risk assessment is provided, but the full extent of node risk related to entitlement management is not captured
Solution Approach 1:
The system merges entitlement data from entitlement datasets with security event data to compute comprehensive risk scores, combining multiple data sources to capture the full extent of node risk including entitlement management aspects that existing tools miss
Solution Approach 2:
The risk scoring system is enhanced to serve multiple functions: it processes both traditional security event data and entitlement data, providing comprehensive risk assessment that covers both security events and entitlement management risks
Data Source
AI summary
High risk nodes in a network are identified and remediated. Inherent risk scores are summed for entitlements of each node, resulting in an entitlement risk score sum for each node. For each peer group, a mean inherent risk score total and a standard deviation are calculated based on these sums. A metric representing the number of peer group standard deviations for each node from the peer group mean inherent score total is generated. This metric is then transformed onto a scale using a cumulative distribution function to generate a threat score for each node corresponding to all the entitlements. The nodes can be rank-ordered based on their threat scores to prioritize risk assessment analysis and remediation.