Predictive Application Security Scoring via ML
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Solution Overview
Problem
Current computing technologies face challenges in effectively identifying and assessing the severity of vulnerabilities in applications during development and deployment, as existing methods often rely on disparate security tools that provide inconsistent and difficult-to-consolidate data, hindering early action on security issues.
Innovation Solution
A system and method that collect metrics from DevSecOps tools, combine them with information from external sources like vulnerability databases and social media, and apply a machine learning model to generate a predictive security score, using natural language processing to determine severity and weight metrics for a comprehensive security posture assessment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If multiple disparate security tools are used to identify vulnerabilities, then the quantity of vulnerability detection increases, but the consistency and consolidability of data decreases
Solution Approach 1:
The patent combines data from multiple disparate security tools (static analysis, dynamic analysis, SAST, DAST, SCA tools) into a unified security scoring system. The machine learning model aggregates metrics from these different sources and applies consistent weighting and normalization to produce reliable, consolidated security scores that maintain data consistency across diverse tool outputs.
Solution Approach 2:
The system transforms raw vulnerability data from multiple tools into standardized parameters through metric normalization and weighting. The machine learning model converts heterogeneous security metrics into a unified scoring framework, changing the parameter representation to enable consistent comparison and aggregation across different security tool outputs.
2Ease of operation
If traditional security assessment methods are used, then the simplicity of implementation is maintained, but the accuracy of vulnerability severity assessment decreases
Solution Approach 1:
The patent introduces a machine learning model as an intermediary between raw security metrics and final vulnerability assessment. This intermediary layer processes multiple security metrics, applies learned weights and relationships, and produces accurate severity predictions. The system maintains ease of operation by automating this complex analysis while improving measurement precision through data-driven assessments.
Solution Approach 2:
The system replaces traditional manual or rule-based security assessment mechanisms with a machine learning-based predictive model. This substitution enables automated, data-driven severity assessment that is both accurate and easy to implement, as the model handles the complex analysis automatically without requiring manual configuration of assessment rules.
3Loss of information
If comprehensive data from multiple sources is collected, then the completeness of security assessment improves, but the complexity of data processing increases
Solution Approach 1:
The patent extracts and focuses on the most relevant security metrics from comprehensive data sources using a machine learning model. The system identifies and extracts key features from multiple data sources (vulnerability metrics, code quality metrics, security scan results) that contribute most to accurate security assessment, processing only the essential data while maintaining completeness.
Solution Approach 2:
The system transforms comprehensive raw data from multiple sources into a reduced set of weighted metrics that the machine learning model can process efficiently. By changing the parameter representation and applying dimensionality reduction through metric selection and weighting, the system maintains assessment completeness while reducing processing complexity.
Data Source
AI summary
A method includes receiving, by a computing device, metrics identifying vulnerabilities in an application; collecting, by the computing device, information related to the vulnerabilities; assigning, by the computing device, weights to the metrics using collected information; applying, by the computing device, a machine learning model on the weighted metrics; and generating, by the computing device, a predictive score for the vulnerabilities using the machine learning model.