Automated Cyber Risk Assessment via Multi-Model Feature Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for underwriting cyber security insurance are inefficient and error-prone due to the inability of human underwriters to accurately assess the complex cyber security risks of an organization, as they lack access to real-time data and are overwhelmed by the volume and variety of data contributing to the risk profile.
Innovation Solution
A cyber security risk assessment system that uses a combination of software, firmware, and hardware to generate an input feature space from multiple data sources, employing AI computer models to compute the likelihood of data breach incidents, recognize events, determine severity, and generate risk factor scores, thereby automating the underwriting process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual underwriting is used, then human underwriters can examine data points, but they cannot access real-time data and are overwhelmed by the volume and variety of data
Solution Approach 1:
The patent replaces the manual mechanical process of human underwriters examining data points with an automated computer system that collects data from multiple sources, processes it through algorithms, and generates risk assessments. This substitution eliminates the limitations of human capacity while maintaining comprehensive data analysis capability.
Solution Approach 2:
The system performs self-service by automatically collecting data from various computer sources, processing it through integrated algorithms, and generating risk factor scores without requiring human intervention. The system serves itself by continuously monitoring and assessing risks in real-time.
2Reliability
If human underwriters review computer logs, then they can assess security risks, but they cannot detect anomalies and become overwhelmed quickly
Solution Approach 1:
The patent replaces human visual inspection of computer logs with automated anomaly detection algorithms that continuously monitor data streams. The system processes large volumes of log data automatically, identifying anomalies without human intervention and eliminating the time loss associated with manual review.
Solution Approach 2:
The system maintains continuous operation by continuously collecting and analyzing data from computer sources without interruption. The automated process ensures uninterrupted risk assessment, eliminating the periodic nature of manual review and ensuring real-time detection of security risks.
3Adaptability or versatility
If existing cyber security companies track external data, then they can detect vulnerabilities, but they fail to assess internal factors and provide single scores that don't match insurance types
Solution Approach 1:
The patent segments the comprehensive risk assessment into multiple distinct risk factors, each corresponding to different aspects of cyber security (e.g., data security, network security, endpoint security). This segmentation allows the system to provide detailed, precise measurements for each risk category while maintaining overall comprehensiveness.
Solution Approach 2:
The system achieves universality by collecting data from multiple internal and external computer sources and processing it through a unified framework that generates risk factor scores applicable to various insurance types. The multi-functional approach enables the system to serve different insurance needs while maintaining consistent, accurate risk measurement.
Data Source
AI summary
A cyber security risk assessment system is described. In an example implementation, the system may generate an input feature space including data associated with a computing system by collecting the data from a plurality of computer sources. The system may compute a likelihood of data-security breach incidents based on the input feature space using a first computer model, recognize events based on the input feature space using a second computer model, and determine a severity of the data-security breach incident or the event using a third computer model. In some instances, the system may generate risk factor scores based on the determined severity, data-security breach incident, and the event, where the risk factor scores indicate a computer security risk of a certain computer security aspect of the computing system. The system may then perform an action based on the risk factor scores.