AI Threat Statement Generation for Complete Threat Modeling
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Solution Overview
Problem
Existing threat management systems lack a mechanism for effectively scoring and improving threat models, particularly in identifying and addressing unknown potential threats in computer applications.
Innovation Solution
A threat modeling system that utilizes a machine learning model to analyze code bases and infrastructure diagrams, compares risk mitigation data with threat libraries, and generates a threat level by identifying unmitigated risks, allowing for the updating and error detection of threat models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional threat management systems are used, then known threats can be detected, but unknown potential threats cannot be identified
Solution Approach 1:
The system enables threat models to self-improve by automatically generating threat statements from code bases and infrastructure diagrams using machine learning models. The system continuously learns from new applications and threats without requiring manual updates to the threat library, allowing it to identify unknown threats autonomously
Solution Approach 2:
The patent replaces manual threat modeling and scoring processes with automated machine learning models. These models analyze code bases and infrastructure diagrams to generate and score threat statements, substituting human expert analysis with automated AI-based systems that can process applications at scale
2Ease of operation
If manual threat modeling is performed, then threat models can be created, but scoring and improvement mechanisms are lacking
Solution Approach 1:
The system implements a feedback loop where threat statements are generated from applications, scored against a threat library, and used to continuously improve the threat model. The machine learning model learns from the scoring results and updates the threat library, creating a self-improving system that enhances reliability over time
Solution Approach 2:
The patent introduces quantitative scoring parameters (threat scores, risk levels, severity ratings) to transform qualitative threat models into measurable, comparable, and improvable data structures. This allows for systematic evaluation and continuous refinement of threat models based on numerical metrics
3Measurement precision
If comprehensive threat analysis is performed on all applications, then all potential threats can be identified, but the complexity and time required increases exponentially
Solution Approach 1:
The system segments the threat analysis process into distinct components: code base analysis, infrastructure diagram analysis, threat statement generation, threat scoring, and mitigation recommendation. Each component is handled by specialized machine learning models, reducing overall system complexity while maintaining comprehensive analysis capability
Solution Approach 2:
The patent creates a universal threat modeling system that can analyze multiple application types, programming languages, and infrastructure configurations using the same machine learning models. The system processes diverse inputs (code bases, diagrams, configurations) through a unified framework, reducing complexity compared to specialized tools for each application type
4Reliability
If traditional antivirus applications are used, then known viruses can be eliminated, but new threats remain undetected
Solution Approach 1:
The system performs preliminary threat identification by analyzing code bases and infrastructure diagrams before deployment using machine learning models. It proactively generates threat statements and identifies potential vulnerabilities in advance, rather than waiting for threats to manifest as known virus signatures
Solution Approach 2:
The patent introduces machine learning models as intermediaries between the application code and the threat detection process. These models translate raw code and infrastructure data into meaningful threat assessments, bridging the gap between traditional signature-based detection and modern behavioral analysis
Data Source
AI summary
Methods and systems are described herein for a threat modeling system. The threat modeling system may use machine learning and operator help to determine whether the threat model has errors and/or whether the threat model is complete. In particular, the threat modeling system may input each threat or threat statement within the threat model into a machine learning model that has been trained to detect errors within the threat model. When errors are detected, the threat modeling system may present those errors to an operator to be fixed. In addition, the threat modeling system may enable an operator to add new threats to the model and then check those threats for error and completeness.