Cybersecurity Risk Model Using Bayesian Signal Integration
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Solution Overview
Problem
Integrating disparate types of data for comprehensive cybersecurity risk analysis is challenging due to asymmetry in information availability across organizations and the labor-intensive nature of tracking evolving security measures over time.
Innovation Solution
A system that combines technographic signals, query-derived signals, and pseudo signals into a Bayesian model to generate a cybersecurity risk model, using a cybersecurity risk modeling server that obtains and prioritizes signals based on their impact and availability, and generates pseudo signals when actual signals are unavailable.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If disparate types of data are collected for comprehensive security risk analysis, then the completeness of risk assessment is improved, but the difficulty of integrating data increases
Solution Approach 1:
The patent employs an intermediary processing layer that standardizes and normalizes disparate data types from multiple sources before integration. This intermediary system transforms heterogeneous security data into a unified format, enabling comprehensive risk assessment without directly confronting the integration complexity through custom point-to-point connections between all data sources.
Solution Approach 2:
The system applies parameter changes by transforming various data types into standardized parameters and metrics. Different data sources are converted into common risk indicators with standardized scales and weightings, allowing comprehensive analysis while simplifying integration through parameter uniformity rather than structural complexity.
2Adaptability or versatility
If information is standardized across different organizations, then the ability to contextualize security risk is improved, but the loss of organization-specific information increases
Solution Approach 1:
The patent implements local quality by applying organization-specific weighting and prioritization rules to standardized risk parameters. While the core parameters are standardized for cross-organization comparability, each organization can locally adjust the importance and sensitivity of specific risk factors based on their unique context, industry, and risk appetite.
Solution Approach 2:
The system employs dynamics by allowing organization-specific parameters and weightings to be dynamically adjusted over time. Organizations can modify their risk contextualization parameters as their business environment, threat landscape, or strategic priorities change, maintaining both standardization and organization-specific adaptability.
3Measurement precision
If security risk tracking is performed continuously over time, then the accuracy of risk assessment is improved, but the labor intensity increases
Solution Approach 1:
The patent implements self-service by enabling automated continuous monitoring and tracking of security risk parameters. The system automatically collects, updates, and re-assesses risk data without requiring manual intervention for each measurement cycle, maintaining high accuracy through continuous tracking while eliminating the proportional increase in labor intensity.
Solution Approach 2:
The system employs feedback mechanisms where risk assessment results automatically trigger updated measurements and adjustments. The continuous tracking creates closed-loop feedback where risk evaluations feed back into parameter adjustments and data collection priorities, improving accuracy through iterative refinement without requiring proportional human labor for each cycle.
Data Source
AI summary
Determining additional signals for determining cybersecurity risk is disclosed, including: obtaining signals associated with a cybersecurity risk, wherein the obtained signals include technographic signals and query derived signals obtained from queries; combining the technographic signals and the query derived signals into a Bayesian model indicating the cybersecurity risk; and determining additional technographic signals or additional query derived signals associated with the cybersecurity risk to obtain such that the additional technographic signals or the additional query derived signals are to be computed to impact the cybersecurity risk.