Time-Series Vulnerability Profiling for Technology Risk Scoring
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Solution Overview
Problem
Existing systems lack the ability to effectively evaluate and quantify the risk of technological vulnerabilities and maintenance practices within organizations, which are crucial for predicting potential disruptions and managing business risks.
Innovation Solution
A vulnerability measurement system utilizing probes to collect observable data, stored in a time-ordered database, and analyzed by a neural network model to infer patterns and generate a technology risk score, which is used to predict organizational disruption and inform insurance pricing or guarantees.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If automated systems are used to capture and measure business processes, then measurement capability is improved, but the complexity of analyzing and quantifying the data increases beyond human capacity
Solution Approach 1:
The patent replaces manual human analysis of business process data with machine learning algorithms and automated analytical systems. The system captures business process data through automated probes and uses computational models to analyze patterns, substituting the mechanical human cognitive process with an automated computational system that can handle the complexity of longitudinal data analysis.
2Reliability
If longitudinal measures of organizational performance are created, then predictive capability is improved, but the difficulty of evaluating and managing multiple risk correlates increases
Solution Approach 1:
The patent transforms multiple complex risk correlates into a single composite risk score through parameter aggregation. The system evaluates numerous business process metrics and technical vulnerability indicators, then combines them into a unified longitudinal performance measure that simplifies the evaluation process while maintaining predictive power for organizational disruption.
3Measurement precision
If machine learning is used to quantify organizational performance, then analytical capability is improved, but the lack of existing systems to evaluate technology risk increases implementation difficulty
Solution Approach 1:
The patent establishes a comprehensive framework and system architecture in advance for evaluating technology risk. By pre-defining the probe mechanisms, data collection protocols, and analytical models, the system creates a ready-to-implement infrastructure that reduces the difficulty of deployment. The framework includes pre-configured methods for capturing business process data and assessing technical vulnerabilities.
Data Source
AI summary
Various systems and methods are described for correlating technology choices with the risk of system vulnerabilities. A system captures and quantifies both observations of technology choices as well as the outputs certain outputs of internal choices and processes across a number of different organizations. A Bayesian estimate of vulnerability is imputed from the choices and observed use of vulnerable technology, further segmented by business type, revenue, and size. Differences between the observation of a particular organization and Bayesian expected value are measured and converted to vulnerability score, the vulnerability score embodying a point-in-time and longitudinal measure of organizational performance, including the likelihood of future compromise due to software vulnerabilities. The vulnerability score can then be further used to price risk, for example in a cyber insurance context.