AI/ML Security Threat Identification for Code Changes
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Solution Overview
Problem
Threat modeling in large organizations is not scalable for non-business-critical applications and applications without public internet access, leading to gaps in security posture and inefficient use of computing resources.
Innovation Solution
A threat identification system utilizing AI/ML to identify security threats and confidence levels for each application version, notifying a security team only when necessary, thereby reducing resource consumption and improving network security.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual threat modeling is performed for each application version, then security coverage is improved, but computing resources and time consumption increase significantly
Solution Approach 1:
The system enables applications to perform self-service threat identification by automatically analyzing their own code changes against pre-trained threat models. Each application independently evaluates its vulnerability status without requiring manual security team intervention for routine assessments, thereby reducing time consumption while maintaining security coverage.
Solution Approach 2:
The system performs preliminary threat modeling and training on business-critical applications before they are deployed. These pre-trained models serve as reference frameworks that enable faster, automated threat identification in subsequent applications, eliminating the need to perform complete threat analysis from scratch for each new application.
2Reliability
If manual threat modeling is performed for all applications, then security posture is improved, but scalability deteriorates in large organizations
Solution Approach 1:
The system segments the threat modeling process into different tiers: business-critical applications undergo comprehensive manual threat modeling, while non-critical applications use automated self-service assessment. This segmentation allows the organization to maintain high security posture for critical systems while achieving scalability across all applications through differentiated service levels.
Solution Approach 2:
The system introduces an intermediary automated threat identification system that acts as a mediator between manual security team efforts and application security needs. This intermediary layer processes routine threat assessments automatically, enabling the security team to focus on high-value activities and allowing the system to scale effectively across large organizations with thousands of applications.
3Loss of energy
If automated threat identification is implemented, then resource consumption is reduced, but detection accuracy may deteriorate
Solution Approach 1:
The system incorporates feedback mechanisms where automated threat identification results are reviewed and validated by security teams for critical applications. The outcomes of these reviews feed back into improving the automated detection algorithms, creating a continuous improvement cycle that maintains high detection accuracy while reducing resource consumption over time.
Solution Approach 2:
The system dynamically adjusts the level of automated versus manual threat modeling based on application risk parameters, change frequency, and historical vulnerability data. By changing the intensity and scope of threat analysis parameters adaptively, the system achieves accurate detection for high-risk applications while using minimal resources for low-risk applications.
Data Source
AI summary
A method for security threat identification using artificial intelligence/machine learning is provided. The method includes receiving code changes for a new version of an application code. New potential threats are determined for the new version of the application code based on the code changes and pre-trained code changes. A new potential threat score is determined based on the new potential threats and a current potential threat score of a current version of the application code. New pre-determined threats are determined for the new version of the application code based on a self-service threat model of the new version of the application code. A new pre-determined threat score is determined based on the new pre-determined threats and a current pre-determined threat score of the current version of the application code. A threat confidence level is determined based on the new potential threat score and the new pre-determined threat score.