AI Cyber Risk Prediction System for Vulnerability Prioritization
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Solution Overview
Problem
Current risk assessment systems, such as CVSS, are not effective in predicting which vulnerabilities will be exploited, leading to inefficient prioritization of patches and resource allocation in cybersecurity.
Innovation Solution
A computer-implemented system that uses artificial intelligence to calculate the probability of a cyber attack and its associated cost, taking into account industry verticals and specific vulnerabilities, to determine cyber aggregation risk and identify organizations to be incentivized to reduce this risk.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If standard risk assessment systems (CVSS) are used to prioritize vulnerabilities, then organizations err on the side of caution by patching severe vulnerabilities, but most flagged vulnerabilities will not be attacked resulting in wasted resources
Solution Approach 1:
The patent changes the parameters used for risk assessment from static CVSS scores to dynamic parameters including AI-predicted exploitation probability, industry vertical risk factors, vulnerability age, and patch availability. This transforms the risk calculation from a generic severity-based metric to a targeted prediction of actual exploitation likelihood, resolving the contradiction between reliable prediction and resource efficiency
Solution Approach 2:
The system incorporates feedback loops where AI models continuously learn from exploitation data, patching patterns, and threat intelligence to improve prediction accuracy over time. This feedback mechanism allows the system to adapt to emerging threats and refine which vulnerabilities require immediate attention versus those that can be deferred, optimizing resource allocation while maintaining reliable predictions
2Reliability
If organizations patch all severe vulnerabilities flagged by CVSS, then they maintain high security posture, but they cannot prioritize effectively since the majority of flagged vulnerabilities will not be attacked
Solution Approach 1:
The patent applies partial action by focusing patching efforts only on vulnerabilities with high predicted exploitation probability rather than all severe vulnerabilities. The AI system identifies a subset of critical vulnerabilities that require immediate attention, allowing organizations to maintain adequate security posture while avoiding excessive patching of low-risk vulnerabilities that would waste productivity
Solution Approach 2:
The system segments vulnerabilities into distinct risk categories based on AI predictions, industry vertical, and exploitation patterns. This segmentation creates prioritized groups (e.g., critical, high, medium, low) that enable targeted patching strategies, improving productivity by focusing resources on the most dangerous vulnerabilities while maintaining security through systematic handling of less critical issues
3Loss of information
If comprehensive vulnerability databases (NVD) are used to track all disclosed vulnerabilities, then complete visibility is achieved, but less than 3% are found to be exploited in the wild making the comprehensive tracking inefficient
Solution Approach 1:
The patent extracts the essential predictive elements from the comprehensive NVD database using AI models that identify patterns in exploitation behavior, threat actor preferences, and vulnerability characteristics. Instead of tracking all vulnerabilities equally, the system extracts and focuses on the subset of vulnerabilities with high exploitation probability, maintaining information completeness about all vulnerabilities while reducing tracking resources by prioritizing based on predicted risk
Data Source
AI summary
Embodiments of a computer-implemented system and methods for predicting and/or determining a probability of a cyber-related attack and associated costs are disclosed.