Cyber Risk Quantification via Control Flow Graph Analysis
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Solution Overview
Problem
Current cybersecurity measures lack a comprehensive and measurable approach to managing cyber risk, often resulting in overspending or underspending due to the complexity of controls and lack of meaningful metrics, leading to ineffective risk management and compliance-based strategies that fail to account for actual risk profiles.
Innovation Solution
A method and device for constructing a control flow graph to quantify the efficacy of cybersecurity controls, identifying weaknesses, and modifying them to enhance cyber risk management by using statistical visualizations and simulations to prioritize and optimize security investments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If comprehensive cybersecurity controls are implemented, then cyber risk mitigation is improved, but device complexity and cost increase
Solution Approach 1:
The patent segments cybersecurity controls into distinct functional categories (preventive, detective, corrective) and organizes them within a structured framework. This segmentation allows organizations to selectively implement controls based on their specific risk profiles rather than implementing all possible controls, thereby reducing complexity while maintaining effective risk mitigation.
Solution Approach 2:
The patent introduces quantitative parameters and metrics to measure control efficacy and risk levels. By changing the approach from qualitative to quantitative assessment, organizations can objectively determine the appropriate level and type of controls needed, optimizing the balance between risk mitigation and complexity/cost.
2Measurement precision
If quantitative metrics are introduced to measure control efficacy, then risk management precision is improved, but measurement and analysis difficulty increases
Solution Approach 1:
The patent establishes feedback mechanisms that continuously measure control efficacy using quantitative metrics and provide this information back to risk management processes. This feedback loop enables automatic adjustment and optimization of controls based on measured performance, reducing the long-term difficulty of measurement while improving precision.
Solution Approach 2:
The patent introduces an intermediary framework that translates complex control interactions into simplified quantitative metrics. This intermediary layer handles the complexity of measuring control efficacy by providing standardized measurement approaches and visualization tools, making the measurement process more manageable.
3Measurement precision
If statistical visualizations are used to analyze control combinations, then risk analysis precision is improved, but computational requirements increase
Solution Approach 1:
The patent applies statistical visualizations and simulations selectively to the most critical control combinations and risk scenarios rather than analyzing all possible combinations. This partial action approach maintains high risk analysis precision for key areas while reducing overall computational resource requirements.
Data Source
AI summary
A control flow graph representing a plurality of controls is constructed, wherein each control comprises a measure taken to counter threats to an IT infrastructure. For each path through the control flow graph, a metric quantifying an efficacy of the controls along the path in countering the threats is calculated. A threat strength distribution for threats to the IT infrastructure is constructed. A visualization of an efficacy of a combination of the plurality of controls is generated, based on the metrics, the control flow graph, and the threat strength distribution. A weakness in the plurality of controls is identified, based on the visualization. The plurality of controls is modified based on the identifying.