Cybersecurity Risk Scoring via Quantitative Data Value and Vulnerability
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Solution Overview
Problem
Existing cybersecurity risk frameworks rely on subjective, qualitative scales, failing to provide a quantitative risk measurement directly tied to the value and volume of sensitive data, which can lead to inconsistent and unreliable risk assessments.
Innovation Solution
A cybersecurity risk scoring system that calculates a quantitative score based on the type, volume, value, and potential vulnerability of sensitive data, using Value and Vulnerability coefficients to assess the risk of data exfiltration and provide an objective measure of cybersecurity risk.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If subjective qualitative scales are used for cybersecurity risk assessment, then the assessment process is simple and easy to implement, but the measurement precision and reliability of risk assessment deteriorates
Solution Approach 1:
The patent transforms the cybersecurity risk assessment from subjective qualitative parameters to objective quantitative parameters. It introduces specific measurable parameters including data value coefficients, data volume coefficients, vulnerability coefficients, and exposure coefficients. These parameters convert abstract risk concepts into quantifiable metrics that can be objectively measured and calculated, thereby improving measurement precision while maintaining operational feasibility through automated calculation.
2Ease of operation
If subjective qualitative scales are used for cybersecurity risk assessment, then the assessment process is simple, but the reliability and consistency of risk assessment deteriorates
Solution Approach 1:
The patent implements a feedback mechanism where the risk score calculation system continuously receives input data about data assets, vulnerabilities, and threats, processes this information through standardized coefficients, and generates quantitative risk scores. This feedback loop ensures consistent application of assessment criteria across different assessments and enables tracking of risk changes over time, thereby improving reliability while maintaining process simplicity through automation.
Solution Approach 2:
By establishing standardized quantitative parameters with defined calculation methods, the patent eliminates subjectivity and inconsistency inherent in qualitative assessments. The use of fixed coefficients for data value, volume, vulnerability, and exposure ensures that the same inputs always produce the same outputs, guaranteeing reliability and consistency across different risk assessments.
3Measurement precision
If quantitative risk measurement tied to data value and volume is implemented, then the accuracy and objectivity of risk assessment improves, but the device complexity and calculation requirements increase
Solution Approach 1:
The patent segments the complex risk assessment process into distinct modular components: data identification module, coefficient calculation module, risk score computation module, and remediation tracking module. Each module handles a specific aspect of the assessment, processing data independently and passing results to the next stage. This segmentation reduces system complexity by making each component manageable and independently implementable, while still achieving accurate quantitative risk measurement through their integrated operation.
Data Source
AI summary
Techniques for evaluating and improving data security are provided. In one embodiment, a method includes receiving results from a sensitive data scan of information technology (IT) infrastructure of an organization, in which the result includes indications of a volume of sensitive data found during the scan, types of the sensitive data found during the scan, and locations at which the sensitive data was found during the scan. The method also includes determining a cybersecurity risk score for the IT infrastructure. This can include calculating the cybersecurity risk score based on the volume of sensitive data found during the scan, value of the sensitive data found during the scan, and vulnerability of the locations at which the sensitive data was found during the scan. Additional systems, devices, and methods are also disclosed.