Cyber Risk Monetization Using Segmented Underwriting Assessment
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Solution Overview
Problem
Existing cybersecurity systems fail to accurately monetize cyber risks, leading to unprepared organizations facing cyber-attacks, inadequate insurance underwriting, and challenges in mergers, acquisitions, and financial evaluations.
Innovation Solution
A cybersecurity system that collects risk data from multiple sources, classifies vulnerabilities, and uses machine learning to estimate financial impacts of cyber-attacks, providing actionable financial insights and risk assessments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If high-level risk assessment is used for cyber insurance underwriting, then underwriting process is simplified, but risk evaluation accuracy deteriorates
Solution Approach 1:
The system segments risk assessment into multiple hierarchical levels: high-level screening for quick underwriting decisions, and detailed quantitative analysis for complex cases. This allows the underwriting process to be simplified for low-risk cases while maintaining accuracy for high-risk cases through deeper analysis.
Solution Approach 2:
The system applies partial quantitative analysis based on risk level - not all entities require full quantitative assessment. High-level assessment is sufficient for low-risk entities, while excessive/detailed analysis is applied only when necessary, optimizing the balance between process simplicity and evaluation accuracy.
2Measurement precision
If comprehensive risk data collection is implemented, then risk assessment accuracy is improved, but system complexity increases
Solution Approach 1:
The system segments data collection into modular components: automated data sources (APIs, logs, sensors), semi-automated sources (surveys, interviews), and manual sources. Each module can be independently configured and activated based on assessment needs, reducing overall system complexity while maintaining comprehensive data collection capability.
Solution Approach 2:
The system employs a universal data collection framework that handles multiple data types (structured, semi-structured, unstructured) from diverse sources through standardized interfaces. This multi-functional approach consolidates complexity into a single framework rather than requiring separate systems for each data source.
3Measurement precision
If quantitative risk analysis is performed, then financial impact assessment is improved, but time and resources required increase
Solution Approach 1:
The system performs preliminary data gathering and automated preliminary analysis before full quantitative assessment. Historical data is pre-processed and stored in ready-to-use formats, and automated scripts prepare data sets in advance, reducing the time required for actual quantitative analysis while maintaining assessment accuracy.
Solution Approach 2:
The system uses historical risk data and patterns from similar entities as templates for current assessments. By copying and adapting proven assessment models and historical patterns, the system reduces the time and resources needed for de novo quantitative analysis while maintaining accuracy through evidence-based approaches.
Data Source
AI summary
A method and system for evaluating cyber risk of an entity comprising a risk evaluation module configured to collect risk data on risks of cyber-attacks connected to SaaS, infrastructure, and legal regulations classified by geolocation, industry type, and size of the victim organization, an entity evaluation module for collecting vulnerability data on assets of the entity classified by industry type, geolocation, size and cyber threat vector vulnerabilities and a monetization engine configured to make an assessment of expected financial loss from a specified cyber-attack to an entity classified by geolocation, industry type, and size, based on the risk data.