Live Threat Modeling Across Development Phases for Current Risk Detection
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Solution Overview
Problem
Existing threat modeling frameworks struggle to keep pace with evolving security risks in software applications, making it difficult to prioritize and maintain a current state of threat assessment.
Innovation Solution
A live threat modeling framework that automates the process of threat modeling, continuously monitoring and updating threat models in near real-time across the development lifecycle of applications, using AI-driven tools and APIs to identify and mitigate threats.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional threat modeling is performed manually and periodically, then the depth of security analysis can be thorough, but the threat model becomes outdated quickly as security risks evolve
Solution Approach 1:
The patent replaces manual threat modeling processes with automated machine learning systems that continuously analyze application code and security threats. The ML model automatically updates threat models by monitoring code repositories, build systems, and security databases, eliminating the need for periodic manual updates while maintaining high accuracy through continuous learning from new threat data.
Solution Approach 2:
The system implements continuous threat modeling by integrating security analysis into the entire software development lifecycle. The ML model continuously monitors code changes, dependency updates, and emerging threats, providing real-time threat assessments rather than periodic snapshots. This continuous operation ensures the threat model remains current without requiring dedicated update cycles.
2Productivity
If threat modeling is integrated continuously into development phases, then security risks are identified in real-time, but the complexity of the security system increases
Solution Approach 1:
The patent creates a unified security platform that performs multiple functions through a single ML model: vulnerability detection, threat prioritization, code analysis, and remediation guidance. This multi-functional approach integrates security into existing development workflows without requiring separate tools for each security task, reducing overall system complexity while enabling continuous security assessment across all development phases.
Solution Approach 2:
The system introduces an intermediary ML-based security layer that sits between developers and the codebase, automatically analyzing security risks without requiring developers to directly implement complex security checks. This intermediary handles the complexity of continuous monitoring and threat analysis, presenting simplified security recommendations to developers and integrating seamlessly with existing build and deployment systems.
3Loss of information
If manual threat modeling is performed, then detailed security analysis can be conducted, but the process is time-consuming and difficult to maintain current
Solution Approach 1:
The patent replaces manual security analysis with automated machine learning systems that continuously scan code repositories, analyze dependencies, and assess threats. The ML model processes vast amounts of security data including vulnerability databases, threat intelligence feeds, and code patterns, providing comprehensive security coverage that would be impossible to achieve manually while delivering results in real-time rather than through lengthy manual processes.
Data Source
AI summary
An example computer system for live threat modeling for an enterprise can include: one or more processors; and non-transitory computer-readable storage media encoding instructions which, when executed by the one or more processors, causes the computer system to: prepare abstracts for applications associated with the enterprise to form a threat model; monitor development phases of the applications; and apply the threat model to the applications during each of the development phases to identify risk.