Autonomous Cyber Risk Assessment Engine for Dynamic Policy Updates
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Solution Overview
Problem
The insurance industry faces challenges in automating the analysis and quantification of cyber-related risks, which evolve rapidly, leading to potential coverage gaps and increased risks for both insurers and insureds, as traditional underwriting methods are not equipped to keep pace with the dynamic nature of cyber threats.
Innovation Solution
A system and method for autonomous risk assessment and quantification using a network-connected server with a deep web extraction engine and cyber risk analysis engine, employing machine learning to predict risks from accidental and malicious events, and performing predictive simulations to update risk models and recommend insurance coverage requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional underwriting methods are used to assess cyber risks, then the insurance policies can be issued with established processes, but the policies lag substantially behind evolving cyber risks creating coverage gaps
Solution Approach 1:
The patent implements dynamic risk assessment by continuously monitoring cyber threats and automatically updating policy terms in real-time. The system transitions from static, annual policy reviews to dynamic, continuous adjustment of coverage based on current threat landscapes, ensuring policies remain current with evolving risks without manual intervention delays
Solution Approach 2:
The system establishes continuous feedback loops where threat intelligence data flows into the underwriting engine, which automatically adjusts policy terms and premiums. This closed-loop system ensures that emerging threats are rapidly incorporated into coverage decisions, eliminating the lag between threat evolution and policy response
2Measurement precision
If vast amounts of data are analyzed to determine suitable underwriting offers, then comprehensive risk assessment is achieved, but the process becomes time-consuming
Solution Approach 1:
The patent replaces manual data analysis and human underwriting judgment with an automated underwriting engine that processes vast datasets using machine learning algorithms. This substitution enables simultaneous processing of multiple data sources (threat intelligence, vulnerability scans, security logs) at machine speed while maintaining comprehensive analytical depth, achieving both precision and speed
Solution Approach 2:
The underwriting engine is designed as a universal system that handles multiple types of cyber risk data through a single platform. It consolidates diverse data sources including threat feeds, asset inventories, security configurations, and historical claims into one integrated assessment process, eliminating the need for separate manual evaluation procedures for each data type
3Ease of operation
If automated systems are implemented to process claims, then convenience for insured and turnaround speed improve, but greater automation is still needed in risk assessment and policy management
Solution Approach 1:
The system implements self-service capabilities where policyholders can access real-time policy status, threat alerts, and mitigation recommendations through automated interfaces. The underwriting engine autonomously makes coverage decisions based on predefined criteria and incoming threat data, eliminating the need for manual claim adjudication and policy adjustments while enhancing user convenience
Data Source
AI summary
A system for autonomous risk assessment and quantification for insurance policies for computer and information technology related risks, including but not limited to losses due to system availability, cloud computing failures, current and past data breaches, and data integrity issues. The system will use a variety of current risk information to assess the likelihood of operational interruption or loss due to both accidental issues and malicious activity. Based on these assessments, the system will be able to autonomously issue policies, adjust premium pricing, process claims, and seek re-insurance opportunities with a minimum of human input.