AI Threat Modeling for Code and Infrastructure Risk Scoring
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Solution Overview
Problem
Existing threat management systems struggle to effectively identify and score potential threats in computer applications, as new applications are designed and enabled, with unknown threats needing to be identified, tracked, and addressed.
Innovation Solution
A threat modeling system uses a machine learning model to analyze a code base and infrastructure diagram of an application, identifying potential threats, comparing them with risk mitigation data to determine a threat level, and updating threat models based on operator input and error detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional threat detection systems are used, then known threats can be identified, but new and unknown threats cannot be effectively detected
Solution Approach 1:
The system performs preliminary threat modeling during the software development lifecycle before deployment, identifying potential threats in advance. Threat models are created during design phases, allowing the system to detect and address security vulnerabilities before they can be exploited in production environments.
Solution Approach 2:
The system implements continuous feedback loops where threat detection results, vulnerability assessments, and security incidents are fed back into the threat model. This feedback mechanism allows the system to learn from new threats and continuously improve its detection capabilities, adapting to emerging security risks.
2Measurement precision
If comprehensive threat analysis is performed on all applications, then accurate threat levels can be determined, but the complexity and time required increases significantly
Solution Approach 1:
The system segments threat analysis into distinct components including threat identification, vulnerability assessment, risk evaluation, and mitigation strategy generation. Each component processes specific aspects of security analysis independently, allowing for modular implementation and reduced overall system complexity while maintaining comprehensive coverage.
Solution Approach 2:
The system dynamically adjusts analysis parameters such as scan depth, threat categories, and evaluation criteria based on application type, criticality, and available resources. This parameter adaptation allows the system to maintain high measurement precision for critical applications while reducing complexity for less sensitive systems.
3Reliability
If detailed threat modeling is conducted for each application, then comprehensive security coverage is achieved, but the time and resources required increase exponentially
Solution Approach 1:
The system applies partial threat modeling by focusing analysis on the most critical security aspects and high-risk areas of each application. Rather than exhaustively analyzing every component, the system identifies and prioritizes key threat vectors, allowing for efficient security assessment that achieves sufficient coverage without exponential resource consumption.
Solution Approach 2:
The system implements universal threat modeling frameworks and templates that can be applied across multiple applications and industries. These reusable models provide standardized security assessment capabilities that reduce the time and resources required for each individual analysis while maintaining comprehensive security coverage.
Data Source
AI summary
Methods and systems are described herein for a threat modeling system. The threat modeling system may use a code base and/or an infrastructure diagram to generate a threat level for an application. In particular, the threat modeling system may input the code base (and in some embodiments the infrastructure diagram) into a machine learning model that has been trained to identify potential threats in computer code and/or within infrastructure diagrams. In response, the threat modeling system may receive potential threats identified by the machine learning model. Furthermore, the threat modeling system may retrieve risk mitigation data associated with the application and compare that risk mitigation data with risk data (e.g., with a threat library). Based on the comparison, the threat modeling system may determine a threat level for the application.