Cyber Security Portfolio Optimization Using Loss Distribution Approach
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Solution Overview
Problem
Current methods for quantifying and mitigating cyber security risks face challenges due to the lack of data for estimating parameters of frequency and severity distributions, often relying on expert judgment, and fail to accurately account for redundancies and synergies among cyber security projects, leading to suboptimal resource allocation.
Innovation Solution
The proposed solution involves using a Loss Distribution Approach (LDA) combined with scorecards and optimization techniques to calculate the expected loss reduction from cyber security projects, incorporating Poisson and log-normal distributions to quantify risks, and utilizing redundancy and synergy matrices to adjust for project interactions, while also considering budget constraints through integer optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If expert judgment is used to estimate frequency and severity distributions, then data requirements are reduced, but measurement precision of loss quantification deteriorates
Solution Approach 1:
The patent introduces scorecards as intermediary tools that bridge expert judgment and quantitative analysis. Scorecards provide a structured framework for experts to input their assessments in a standardized format, which then can be processed through mathematical models to generate quantitative loss estimates. This intermediary structure allows expert knowledge to be systematically converted into measurable parameters without requiring raw data.
Solution Approach 2:
The patent transforms qualitative expert judgments into quantitative parameters by changing the representation of risk assessments from subjective descriptions to numerical values. Through scorecard systems and loss distribution approaches, the patent converts expert opinions into frequency and severity parameters that can be mathematically processed, enabling precise quantification while maintaining expert insight.
2Device complexity
If traditional loss quantification methods are used, then calculation complexity is reduced, but accuracy in accounting for project redundancies and synergies deteriorates
Solution Approach 1:
The patent segments the loss quantification process into distinct modular components: scorecard assessment, frequency distribution modeling, severity distribution modeling, and redundancy/synergy adjustment. This segmentation allows each component to be independently developed and optimized, making the overall complex system more manageable while improving accuracy in capturing project interactions.
Solution Approach 2:
The patent adds new dimensions to traditional loss quantification by introducing redundancy and synergy matrices that capture project interactions. Instead of simply summing individual project losses, the patent incorporates additional dimensions that account for overlapping protections (redundancies) and complementary effects (synergies), thereby improving accuracy without fundamentally changing the calculation approach.
3Measurement precision
If comprehensive project portfolios are evaluated, then investment decision quality improves, but optimization difficulty under budget constraints worsens
Solution Approach 1:
The patent applies partial action by focusing optimization on the most critical projects first. Through scorecard-based prioritization, the system identifies and evaluates a subset of high-impact projects that provide the greatest loss reduction benefit, rather than attempting to optimize all projects simultaneously. This partial approach maintains decision quality while reducing computational complexity.
Solution Approach 2:
The patent introduces dynamic optimization that adapts to budget constraints in real-time. The system can adjust project selections based on available resources, allowing the optimization to be flexible and responsive. This dynamic approach enables quality investment decisions across different budget scenarios without requiring a completely different optimization system for each constraint level.
Data Source
AI summary
Systems and methods for optimizing cyber security projects. A method may include identifying cyber security related projects used to protect entity assets; and solving an optimization programming problem algorithm, using an optimization engine comprising an evolutionary algorithm, to identify project synergies to select a subset of cyber security related projects that optimize expected loss reduction from cyber security related threat.