Cybersecurity Risk Measurement System Using Quantified Threat Data
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Solution Overview
Problem
Existing risk measurement and modeling methods for cybersecurity are subjective, inconsistent, and difficult to use effectively, often relying on flawed data inputs and lacking industry-specific threat data, business impact assessments, and control effectiveness evaluations.
Innovation Solution
A computer-implemented method and system that uses industry-specific threat likelihood data, business impact information, and control effectiveness assessments to improve the efficiency and accuracy of resource allocation for cybersecurity, providing a more objective and understandable risk measurement and modeling approach.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If subjective risk assessment methods (questionnaires, expert intuition) are used, then risk measurement can be performed with simple models, but the reliability and consistency of risk assessment deteriorates due to lack of calibration and objective standards
Solution Approach 1:
The patent transforms subjective risk parameters into objective quantitative parameters by establishing standardized measurement scales for threat likelihood, vulnerability, and business impact. This allows risk assessment to transition from uncalibrated expert opinion to calibrated quantitative measurement, resolving the contradiction between model simplicity and assessment reliability.
Solution Approach 2:
The patent replaces the 'mechanical' system of subjective human judgment with an automated computational system that applies standardized formulas and algorithms. This substitution eliminates human bias and calibration issues while maintaining model accessibility, thereby improving reliability without significantly increasing complexity.
2Ease of operation
If the standard risk formula (Risk=Threat×Vulnerability×Cost) is used, then a structured approach to risk calculation is provided, but the measurement precision deteriorates due to difficulty in defining and quantifying the variables
Solution Approach 1:
The patent segments the broad risk assessment task into distinct measurable components: threat likelihood, vulnerability, and business impact. Each component is further divided into specific measurable attributes (e.g., threat frequency, system weakness, financial consequence), allowing precise quantification while maintaining the simplicity of the overall Risk=Threat×Vulnerability×Cost framework.
Solution Approach 2:
The patent introduces standardized measurement scales and assessment protocols as intermediaries between the abstract risk concept and concrete numerical values. These intermediaries provide consistent methods for translating qualitative judgments into quantitative data, improving measurement precision without complicating the underlying risk calculation approach.
3Measurement precision
If industry-specific threat data, business impact assessments, and control effectiveness evaluations are incorporated, then the accuracy and usefulness of risk measurement improves, but the device complexity and data requirements increase
Solution Approach 1:
The patent creates a universal risk assessment framework that can accommodate multiple data sources and measurement approaches through a single standardized interface. The system can process industry-specific threat data, business impact assessments, and control effectiveness evaluations using the same core methodology, thereby improving measurement precision without proportionally increasing system complexity.
Data Source
AI summary
A method and system for risk measurement and modeling, which may be used to identify and mitigate information security risks for an information system, and which may improve the efficiency of risk measurement and modeling. Such a system may perform risk modeling based on threat likelihood information, the potential business impacts of particular threats, and data on the effectiveness of particular controls implemented by the operators of the information system, which may be used to calculate residual risk scores for particular risk scenarios that the information system may face.