Distributed Code Auditing for Continuous Policy Compliance
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Solution Overview
Problem
Current software development frameworks lack a comprehensive system to evaluate the holistic cybersecurity posture of technology components throughout their lifecycle, failing to automate policy and best practice compliance checking and vulnerability mapping, which can lead to operational and cybersecurity risks.
Innovation Solution
A system for continuous policy-aware vulnerability mapping, security posture determination, and attack path planning, utilizing an indexing service to process and index source code, perform code audits, and a monitoring service to continuously monitor for changes, with a compliance engine to verify policy configurations and produce automated recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If comprehensive code auditing and policy verification are implemented throughout the software lifecycle, then cybersecurity posture and compliance assurance are improved, but system complexity and resource requirements increase
Solution Approach 1:
The system segments the code auditing process into multiple independent services including indexing service, monitoring service, compliance engine, and recommendation engine. Each service handles specific aspects of compliance verification, allowing the complex task to be distributed across modular components that can be managed and scaled independently.
Solution Approach 2:
The indexing service performs preliminary processing and indexing of source code before compliance checking occurs. This advance preparation organizes the codebase into a structured format with metadata, enabling faster and more efficient compliance verification later without requiring re-parsing of raw code during actual compliance checks.
2Reliability
If continuous monitoring and automated compliance verification are implemented, then operational risks are reduced and policy adherence is improved, but processing time and computational resources increase
Solution Approach 1:
The system performs preliminary indexing and metadata extraction during code commits, preparing the codebase in advance for compliance checking. This allows the monitoring service to quickly query already-processed code without performing full parsing during continuous monitoring, significantly reducing processing time for compliance verification.
Solution Approach 2:
The system replaces manual compliance checking with automated computational processes. The compliance engine automatically verifies policy configurations and generates recommendations without human intervention, while the monitoring service continuously tracks code changes programmatically, eliminating the time cost of manual review while maintaining comprehensive coverage.
3Measurement precision
If detailed code indexing and metadata extraction are performed, then vulnerability mapping precision is improved, but processing overhead and storage requirements increase
Solution Approach 1:
The indexing service extracts metadata and creates indexes selectively based on what is needed for compliance verification, rather than duplicating or storing entire codebases. It focuses on extracting specific properties such as component relationships, dependency information, and policy-relevant attributes, storing only the essential data needed for precise vulnerability mapping without excessive storage overhead.
Data Source
AI summary
A system for continuous contextual policy-aware vulnerability mapping, security posture determination and attack planning and simulation, comprising an indexing service configured to create a dataset by processing and indexing source code of a project by a developer, perform a code audit on the indexed source code, store results from the code audit in the dataset, gather additional information relating to the provided project as intended and as operated, store the additional information in the dataset, and store the dataset into memory; and a monitoring service configured to continuously monitor the project for source code and operational changes and performance and make changes to the dataset as needed.


