Cyber Security Risk Model Quantifying Asset Exposure
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Solution Overview
Problem
Conventional analytical techniques for calculating cyber security risks in information handling systems, such as those in maritime environments, are largely qualitative and do not adequately address the quantifiable assessment of risks across diverse and complex asset environments with numerous access points, failing to provide an efficient and accurate risk mitigation strategy.
Innovation Solution
A cyber security risk model and index are developed to quantify cyber security risks by assessing virtual asset security attributes, including functions, connections, and identities, using a model that calculates a relative cyber security risk score to facilitate modifications and adjustments in system configurations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If conventional qualitative analytical techniques are used to assess cyber security risks, then understanding of risk characteristics is improved, but quantifiability and measurement precision of risks deteriorate
Solution Approach 1:
The patent transforms qualitative cyber security risk assessment into quantitative assessment by introducing measurable parameters including risk probability (P), risk impact (I), and risk exposure (E). The risk probability is calculated based on vulnerability data and threat intelligence, risk impact is quantified using asset criticality and potential damage metrics, and risk exposure is computed as the product of probability and impact. This parameter transformation enables precise measurement and comparison of cyber security risks across different systems and scenarios.
2Reliability
If comprehensive risk assessment across diverse asset environments is performed, then coverage and reliability of risk evaluation is improved, but system complexity and difficulty of assessment increase
Solution Approach 1:
The patent segments the cyber security risk assessment system into distinct functional modules: asset identification module that catalogs digital assets and their criticality, vulnerability assessment module that evaluates system weaknesses, threat intelligence module that incorporates external threat data, risk calculation module that computes probability and impact metrics, and prioritization module that ranks risks. This segmentation allows comprehensive coverage of diverse asset environments while managing complexity through modular design, where each module handles specific aspects of the assessment independently.
Solution Approach 2:
The patent standardizes complex risk assessment by transforming diverse security attributes into uniform quantitative parameters. All assets are evaluated using consistent metrics for risk probability (based on vulnerability severity and threat likelihood), risk impact (based on asset criticality and potential damage), and risk exposure (calculated as probability multiplied by impact). This parameter standardization enables reliable comparison across heterogeneous systems while simplifying the assessment process through a unified computational framework.
3Measurement precision
If detailed security attributes of virtual assets are assessed, then measurement precision of risk evaluation is improved, but ease of operation and implementation difficulty increase
Solution Approach 1:
The patent achieves precise risk evaluation by transforming detailed security attributes into standardized quantitative parameters. Security attributes such as vulnerability severity, asset criticality, and threat likelihood are converted into numerical values for risk probability and risk impact. The risk exposure is then calculated using the straightforward formula E = P × I, where P is risk probability and I is risk impact. This parameter transformation maintains high measurement precision while enabling automated computation and simplifying the assessment process.
Solution Approach 2:
The patent implements automated risk assessment that reduces manual intervention. The system automatically collects security attribute data from various sources, computes risk metrics using predefined algorithms, and generates prioritized risk lists without requiring extensive manual analysis. This self-service approach maintains high measurement precision through consistent application of calculation formulas while significantly improving ease of operation and reducing implementation complexity.
Data Source
AI summary
A cyber security risk model mitigates cyber security risks for an asset environment (including a virtual asset) by utilizing a functions, connections and identities to determine a cyber security risk index (CSRI). An asset environment may comprise one or more functions where each function has one or more connections associated with any one or more of the functions and one or more identities associated with the virtual asset. A CSRI may be determined for each function based on the cyber security risk model that takes into account the cyber security risks or attributes associated with each function, connection and identity associated with the virtual environment or virtual asset. The asset environment may be adjusted, reconfigured, or otherwise altered based on the CSRI for any given function or for an overall CSRI. An alert may also be triggered based, at least in part, on a determined CSRI.