Machine Learning Security Vulnerability Impact Assessment

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Solution Overview

Problem

Existing systems lack the ability to accurately and efficiently determine the impact of security vulnerabilities on computing aspects of software applications executed on specific platforms, leading to delayed mitigation efforts and potential data breaches.

Innovation Solution

A system that uses machine learning models to analyze security vulnerability data and determine computing aspect impact levels, providing a graphical user interface for users to easily identify and prioritize security threats.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If manual techniques are employed to determine security vulnerabilities, then security assessment can be performed, but errors increase and subjectivity reduces reliability

Engineering Contradiction:

Improvesecurity assessment reliabilityVSAvoidmanual assessment complexity

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The patent replaces manual mechanical assessment processes with automated machine learning-based systems. The machine learning model automatically analyzes security vulnerability data, computes impact levels, and generates assessments without human intervention, thereby eliminating subjectivity and reducing errors while maintaining reliability.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system enables self-service security assessment where the machine learning model autonomously processes vulnerability data and generates impact assessments without requiring manual analysis. The automated system serves itself by continuously learning from data and improving its assessment capabilities.

Inventive Principle:

Principle #25Self-service

2Measurement precision

If publicly available information is used to assess security, then assessment can be performed, but information may be outdated or inaccurate

Engineering Contradiction:

Improvesecurity information accuracyVSAvoidinformation publication delay

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The machine learning model performs preliminary analysis of security vulnerability data before it becomes publicly available or outdated. By continuously monitoring and analyzing security data sources, the system proactively identifies and assesses vulnerabilities in real-time, ensuring accurate and current security assessments.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system implements continuous feedback loops where the machine learning model constantly receives new security vulnerability data, updates its assessments, and refines its predictions. This real-time feedback mechanism ensures that security assessments remain accurate and current without relying on outdated public information.

Inventive Principle:

Principle #23Feedback

3Reliability

If more time is devoted to correcting security vulnerabilities, then security posture improves, but attackers exploit vulnerabilities in real-time before corrections

Engineering Contradiction:

Improvesecurity postureVSAvoidvulnerability exploitation speed

Core Design Contradiction:

ReliabilityVSSpeed

Solution Approach 1:

The machine learning model performs preliminary prioritization of security vulnerabilities by computing impact levels before actual exploitation occurs. By predicting which vulnerabilities will have the highest impact, the system enables proactive remediation efforts focused on the most critical threats before attackers can exploit them.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system converts the harm of real-time vulnerability exploitation into benefit by using machine learning to predict and prioritize vulnerabilities before exploitation occurs. The predicted impact levels transform the threat landscape into actionable intelligence, allowing security teams to address the most dangerous vulnerabilities first.

Inventive Principle:

Principle #22Blessing in disguise (Convert harm into benefit)

4Measurement precision

If network engineers rely on subjective opinions to determine security vulnerabilities, then assessment can be performed, but disagreement reduces consistency

Engineering Contradiction:

Improvevulnerability prioritization accuracyVSAvoidsubjective assessment complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The patent replaces subjective human opinion-based assessment with objective machine learning-based computation. The machine learning model consistently applies learned patterns and algorithms to prioritize vulnerabilities, eliminating disagreements and ensuring uniform assessment criteria across all security evaluations.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Data Source

PatentUS12282565B2Generative cybersecurity exploit synthesis and mitigation

Publication Date: 2025.04.22 CITIBANK N A

AI summary

Described herein are systems and methods for identifying security vulnerabilities. The systems and methods herein can utilize security vulnerability information to identify potential security threats and can utilize this information to generate an attack using a machine learning model, such as a large language model. Generated attacks can be carried out to assess impact of a security vulnerability. An output can be provided that represents the assessed impact. In some implementations, the systems and methods herein generate patches or other mitigations for security vulnerabilities, which can be tested and deployed to address security vulnerabilities.