External Cybersecurity Risk Scoring via Network Prefix Analysis
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Solution Overview
Problem
Current methods for assessing cybersecurity breach risk in organizations are subjective and lack a robust, generic measure, relying on fine-grained network traffic data that is challenging to convert into a comprehensive risk score.
Innovation Solution
A computer-implemented predictive analytics system that assesses cybersecurity breach risk by scoring individual network prefixes and combining scores to generate an overall organization score, using externally visible data and features indicative of cybersecurity posture, such as DNS resolvers, SSL/TLS configurations, and reputation blacklists.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If fine-grained network traffic data is used for risk assessment, then measurement precision is improved, but device complexity and ease of operation deteriorate
Solution Approach 1:
The patent extracts only the most critical security features from fine-grained network traffic data (e.g., DNS resolver configuration, SSL/TLS certificate validity, port openness) rather than analyzing all available data points. This extraction approach maintains measurement precision for risk assessment while significantly reducing system complexity by focusing on a curated subset of externally-visible features.
Solution Approach 2:
The patent introduces an intermediary layer of pre-defined security features that act as mediators between raw network traffic data and final risk scores. These features serve as a simplified interface, translating complex network observations into standardized security indicators that are easier to process and interpret without losing essential risk information.
2Measurement precision
If fine-grained network traffic data is collected, then measurement precision is improved, but ease of operation worsens
Solution Approach 1:
The patent extracts only the most critical security features from fine-grained network traffic data (e.g., DNS resolver configuration, SSL/TLS certificate validity, port openness) rather than analyzing all available data points. This extraction approach maintains measurement precision for risk assessment while significantly reducing system complexity by focusing on a curated subset of externally-visible features.
Solution Approach 2:
The patent introduces an intermediary layer of pre-defined security features that act as mediators between raw network traffic data and final risk scores. These features serve as a simplified interface, translating complex network observations into standardized security indicators that are easier to process and interpret without losing essential risk information.
3Ease of operation
If subjective expert assessments are used, then ease of operation is improved, but measurement precision deteriorates
Solution Approach 1:
The patent replaces the mechanical system of subjective expert judgment with an automated computational system that objectively calculates risk scores based on observable network features. The system uses algorithmic processing of security features (DNS configuration, SSL certificates, port scans) to generate standardized risk assessments, eliminating human subjectivity while maintaining operational simplicity through automation.
Solution Approach 2:
The patent transforms subjective expert assessments into objective parameter-based measurements by defining specific, quantifiable security features (e.g., number of open ports, certificate validity status, DNS resolver responses). This parameterization allows risk assessment to be performed through automated comparison of measurable attributes rather than subjective evaluation, improving both objectivity and consistency.
Data Source
AI summary
A system and method for assessing the cybersecurity breach risk associated with a given organization is disclosed. The system and method assume no internal visibility into any organizational network. A taxonomy of possible data sources is defined and motivated. The system and method are both purely empirical and robust against common difficulties in scoring organizational networks, such as the raw number of network assets owned by the organization.