Cybersecurity Scoring Engine Using Graph Analysis for Insurance Risk
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Solution Overview
Problem
Current cybersecurity assessment methods lack a comprehensive and holistic approach to evaluate network threats and security capabilities, failing to accurately score organizations' cybersecurity posture due to heterogeneous data sources and inadequate use of time-graphs and machine learning for insurance purposes.
Innovation Solution
A system comprising sensors, multi-dimensional time-series databases, and a cybersecurity scoring engine that crawls internet resources and public databases to gather and analyze data from diverse sources, generating a weighted cybersecurity rating by assessing vulnerabilities, patching frequency, and other factors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple data sources are gathered to improve cybersecurity assessment comprehensiveness, then the accuracy and completeness of security profiles improve, but the complexity of data collection and processing increases due to heterogeneous data formats and sources
Solution Approach 1:
The patent employs intermediate processing layers including web crawlers, data normalization services, and standardized data models that act as mediators between heterogeneous data sources and the analysis engine. These intermediaries transform diverse data formats into a unified structure, enabling comprehensive security assessment without directly managing the complexity of multiple sources.
Solution Approach 2:
The system transforms heterogeneous data by changing its parameters and format through normalization processes. Data from different sources is converted to standardized schemas with consistent data types, time formats, and structural representations, allowing the analysis engine to process diverse information uniformly while maintaining assessment accuracy.
2Reliability
If time-graphs and machine learning are implemented to analyze cybersecurity trends, then the ability to detect patterns and predict threats improves, but the computational resources and processing time required increase
Solution Approach 1:
The system performs preliminary data processing and feature extraction before applying machine learning algorithms. Time-graphs are pre-computed from historical data, and relevant features are extracted and stored in optimized formats. This preliminary action reduces the computational burden during actual threat analysis, enabling reliable pattern detection with lower real-time resource consumption.
3Loss of information
If comprehensive reconnaissance is performed to establish full security profiles, then the completeness of cybersecurity information improves, but the time and computational effort required for data collection increases
Solution Approach 1:
The system implements a tiered reconnaissance approach that performs partial comprehensive scans at different intervals. Critical security parameters are monitored continuously with high frequency, while less critical parameters are assessed periodically. This partial action strategy maintains information completeness for essential security metrics while reducing overall data collection time and resource expenditure.
Data Source
AI summary
A system for comprehensive cybersecurity analysis and rating based on heterogeneous data and reconnaissance is provided, comprising a multidimensional time-series data server configured to create a dataset with at least time-series data gathered from passive or active network reconnaissance of a client or target; and a cybersecurity scoring engine configured to retrieve the dataset from the multidimensional time-series data server, process the dataset using at least computational graph analysis, and generate an aggregated cybersecurity score based at least on results of processing the dataset.