Spacetime Graph for Continuous Code Risk Assessment

The patent replaces manual mechanical review processes with automated AI-based analysis systems. The Code Intelligence Analyzers and machine learning models automatically perform security and compliance assessments on code commits, replacing the manual mechanical work of security architects while maintaining or improving assessment quality.

The system enables self-service security assessment where the code analysis system automatically identifies material changes, performs risk assessments, and generates reports without requiring manual intervention from security architects for every commit. The system serves itself by continuously learning from code patterns and automatically adapting its analysis.

The patent applies partial action by focusing security analysis only on material changes rather than every code commit. The Code Intelligence Analyzers use machine learning to identify which changes are significant enough to warrant security review, performing partial analysis on selected commits rather than exhaustive analysis on all code changes, thereby reducing time consumption while maintaining security coverage.

The system performs preliminary security assessment automatically as part of the CI/CD pipeline before formal security reviews are needed. The automated analysis prepares risk assessments and identifies potential issues in advance, so when security architects do review code, the preliminary work has already been completed, reducing overall time consumption.

The patent implements continuous security analysis that operates throughout the entire software development lifecycle, from initial commits through deployment. The Code Intelligence Analyzers continuously monitor code changes in real-time, providing ongoing security assurance rather than periodic checks, ensuring security is maintained even as release frequency increases.

The system provides continuous feedback to developers and security architects about security risks in code commits. The automated analysis generates immediate feedback on potential security issues, allowing rapid correction before deployment. This feedback loop enables frequent releases while maintaining security standards through immediate identification and remediation of issues.

The Code Intelligence Analyzers perform self-service by automatically detecting material changes in code commits without requiring developer notification. The system autonomously analyzes code repositories, identifies significant changes using machine learning, and triggers security reviews automatically, eliminating the need for developers to manually notify DevSecOps teams.

The system provides automated feedback to the development team about detected material changes and required security reviews. Instead of developers initiating notifications, the system monitors code changes and provides feedback about security implications, reversing the communication flow and simplifying developer workflow while maintaining review completeness.