Cyber Digital Twin Simulation for Security Control Prioritization
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Solution Overview
Problem
Existing cyber security systems struggle to efficiently prioritize and implement security controls in complex enterprise networks due to the scale and complexity of cyber threats, failing to consider how hackers exploit vulnerabilities in real-time attacks and lacking a comprehensive approach to identify and address missing security controls.
Innovation Solution
A Cyber Digital Twin (CDT) platform simulates enterprise networks to analyze attack graphs, determining and prioritizing security controls by evaluating their influence on cyber risk, using a digital twin to model hacker movements and automatically identify and implement the most effective security controls.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional security monitoring systems are used to detect cyber threats, then security personnel can be alerted to adverse events, but the scale and complexity of cyber threats hamper operator ability to prioritize and rationalize security controls requirements
Solution Approach 1:
The patent creates a digital twin (analytical attack graph) that copies and simulates the enterprise network's attack surface. This virtual model allows security personnel to analyze threat priorities without directly interacting with the complex real network, resolving the contradiction by providing a simplified yet accurate representation for decision-making.
Solution Approach 2:
The simulation engine acts as an intermediary between the complex cyber threat landscape and security personnel. It processes the complexity of multiple attack paths and security controls, translating them into prioritized recommendations that operators can act upon without being overwhelmed by the underlying complexity.
2Reliability
If comprehensive security controls are implemented across the entire enterprise network, then cyber security risk is reduced, but the time and resources required to implement and manage all security controls increase significantly
Solution Approach 1:
The patent applies partial action by identifying and implementing only the most critical security controls based on simulation results. Rather than implementing all possible security controls across the entire network, the system prioritizes controls that address the most significant attack paths, reducing implementation time while maintaining effective risk reduction.
Solution Approach 2:
The enterprise network's security posture is segmented into discrete attack paths and individual security controls. This segmentation allows the system to evaluate and prioritize controls independently, enabling phased implementation of high-impact controls first rather than requiring simultaneous deployment across the entire network.
3Measurement precision
If manual analysis of attack graphs is performed to identify security controls, then detailed understanding of attack pathways is achieved, but the process is too slow to keep pace with evolving cyber threats
Solution Approach 1:
The patent replaces the manual mechanical process of analyzing attack graphs with an automated simulation engine. This engine computationally evaluates attack paths, security controls, and their interactions, achieving both high precision in analysis and rapid processing speed that cannot be attained through manual methods.
Solution Approach 2:
The simulation engine performs self-service by automatically analyzing the analytical attack graph, evaluating security controls, and generating prioritization recommendations without human intervention. This automation maintains high analytical precision while dramatically increasing productivity and speed of threat assessment.
Data Source
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AI summary
Implementations include receiving an AAG that at least partially defines a digital twin of an enterprise network and includes rule nodes each representing an attack tactic that can be used to move along a path, determining security controls each mitigating at least one rule node, executing an iteration of a simulation of a sub-set of security controls in the enterprise network, the iteration including: for each security control in the set of security controls, determining, an influence score that represents a change in a security risk from implementing the security control and a rule distribution, defining the sub-set of security controls based on the first influence scores, and reducing the AAG based on the sub-set of security controls to provide a residual AAG, determining a decrease in a graph risk value and the first AAG, and selectively implementing the sub-set of security controls in the enterprise network.