Cybersecurity Control Efficacy Measurement with Stochastic Loss Modeling
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Solution Overview
Problem
Conventional cybersecurity risk management relies on ordinal measurements that do not consider systemic dependencies between controls, leading to inaccurate efficacy measurements and poor decision-making.
Innovation Solution
A method for quantitative measurement of detection and response control effect on risk using parameters like Event Velocity, Visibility, Recognition, Containment, Monitoring Frequency, and Recovery Time to calculate Realized Loss, enabling comparison and evaluation of control efficacy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If ordinal measurements (high/medium/low, 1-thru-5 scales) are used to evaluate cybersecurity controls, then the evaluation process is simple and easy to perform, but the measurement precision and reliability of efficacy measurements deteriorate
Solution Approach 1:
The patent transforms cybersecurity control evaluation from subjective ordinal parameters (high/medium/low, 1-thru-5 scales) to objective quantitative parameters (detection time in hours, containment time in hours, financial loss in dollars). This parameter transformation enables mathematical operations and stochastic modeling while maintaining ease of data collection through standard security metrics.
Solution Approach 2:
The patent replaces the manual, subjective assessment mechanism with an automated computational system that performs stochastic simulations and mathematical calculations. This substitution eliminates human bias and enables precise, repeatable measurements of control efficacy through algorithmic processing of security event data.
2Ease of operation
If conventional cybersecurity tools are used for ROI analysis, then the analysis process is straightforward, but the reliability of efficacy measurements and ROI analysis deteriorates
Solution Approach 1:
The patent implements feedback loops where stochastic simulation results feed into refined models of detection and containment processes. The system continuously iterates between observed security events, simulated outcomes, and updated control efficacy measurements, creating a self-correcting measurement system that improves reliability through repeated validation against actual security data.
Solution Approach 2:
The patent performs preliminary stochastic simulations using historical security event data before conducting formal ROI analysis. This preliminary action establishes baseline expectations and validates model accuracy, ensuring that subsequent ROI calculations are based on verified relationships between control parameters and security outcomes.
3Device complexity
If systemic dependencies between controls are not considered, then the evaluation process remains simple, but the accuracy of efficacy measurements deteriorates
Solution Approach 1:
The patent merges multiple cybersecurity controls into a unified stochastic model that captures their interdependencies. Rather than evaluating controls in isolation, the system combines detection controls, containment controls, and response controls into an integrated simulation framework that reflects how these controls interact during actual security incidents.
Solution Approach 2:
The patent creates a composite evaluation model that integrates multiple control types and their interactions. This composite approach combines probabilistic models of detection, containment, and response into a unified framework that captures the emergent behavior of control systems working together, providing more accurate efficacy measurements than individual control assessment.
Data Source
AI summary
A system and method for quantitative measurement of detection and response control effect on risk. Various parameters are used to determine the value of incident detection and response controls, including Maximum Loss ML, Event Velocity v, Loss Growth Rate G, Visibility V, Recognition R, Monitoring Frequency M, Monitoring Window d, Containment C, Detection and Containment Time T, Recovery Time S, and Realized Loss L.