Machine-Learning CI/CD Pipeline Security Monitoring
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing manual security processes for Continuous Integration and Continuous Deployment (CICD) pipelines rely on human experience and cannot be scaled to meet the needs of automated build and release infrastructure, leading to increased vulnerabilities and unknown dependencies.
Innovation Solution
A computer-implemented method and apparatus for CICD pipeline security check using a model trained on historical data to predict the security status of the pipeline, monitoring events, and determining compliance with security rules in real-time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual security processes are used for CICD pipelines, then security checks can be performed with human expertise, but the processes cannot be scaled to meet the needs of automated build and release infrastructure
Solution Approach 1:
The patent replaces manual human security review processes with an automated machine learning model that analyzes CICD pipeline events. The system uses automated event monitoring and prediction algorithms to substitute human expertise, enabling scalable security checks across multiple pipelines without relying on human operational knowledge and trust.
2Productivity
If more codes are written and more releases are made to meet agile development needs, then development productivity increases, but more unknown dependencies and vulnerabilities are introduced
Solution Approach 1:
The system performs preliminary security analysis by monitoring CICD pipeline events in real-time and using a prediction model to identify potential security issues before they reach production. The model proactively detects vulnerabilities and unknown dependencies during the build and deployment process, allowing security problems to be addressed before they cause harm.
Solution Approach 2:
The patent implements a feedback mechanism where the prediction model continuously analyzes pipeline events and provides security status information back to the CICD process. This feedback loop enables real-time security monitoring and allows the system to respond to newly introduced vulnerabilities as they occur during rapid development cycles.
3Reliability
If real-time security monitoring is implemented in CICD pipelines, then security status can be predicted and insecure pipelines can be terminated, but additional computational resources and time are required
Solution Approach 1:
The system monitors only the most critical pipeline events that are most indicative of security issues, rather than analyzing every single event in detail. The prediction model focuses on key security-relevant features from the event stream, performing partial analysis that provides sufficient security assessment without requiring exhaustive computational resources.
Data Source
AI summary
A computer-implemented method for Continues Integration and Continues Deployment (CICD) pipeline security check is provided according to embodiments of the present disclosure. In the method, a plurality of events is executed sequentially to create a CICD pipeline. The plurality of events is monitored. Moreover, a security status of the CICD pipeline is determined based on the monitored events and a model for predicting the security status of the CICD pipeline.