Unpredictable Code Section Analysis for Sensitive Information Review
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Solution Overview
Problem
Large-scale software projects with millions of lines of code are difficult to analyze for security vulnerabilities, as manual reviews are time-consuming and prone to missing compromised code that can lead to data breaches, especially in collaborative environments where security standards vary among contributors.
Innovation Solution
A code and text processing tool analyzes a corpus of code to identify potentially sensitive information by removing irrelevant patterns and frequently occurring sections, determining an unpredictability score for remaining sections, and prioritizing those with high unpredictability for further review.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual review of code is performed, then security analysis can be conducted, but time consumption and expense increase significantly
Solution Approach 1:
The patent extracts and removes frequently occurring code sections that are unlikely to contain sensitive information. By identifying and excluding these common patterns (such as standard library functions, boilerplate code, and frequently reused segments), the system reduces the volume of code requiring manual review while maintaining security analysis effectiveness on the remaining unique sections.
Solution Approach 2:
The patent divides the large-scale codebase into manageable sections by identifying frequently occurring patterns and separating them from unique code segments. This segmentation allows reviewers to focus exclusively on the non-repetitive portions of the code where sensitive information is most likely to be found, significantly reducing analysis time while maintaining comprehensive security coverage.
2Reliability
If comprehensive code analysis is performed on large-scale projects, then security vulnerabilities can be detected, but the complexity and cost of analysis increase
Solution Approach 1:
The patent extracts frequently occurring code sections and removes them from the analysis set. By identifying patterns that appear multiple times throughout the codebase and excluding them from detailed review, the system reduces analysis complexity while maintaining the ability to detect security vulnerabilities in the remaining unique code sections.
3Measurement precision
If manual review of all code sections is conducted, then sensitive information can be identified, but productivity decreases due to the vast amount of code
Solution Approach 1:
The patent extracts and removes frequently occurring code sections that are unlikely to contain sensitive information. By eliminating these redundant sections from the review process, the system maintains precise identification of sensitive information in the remaining code while significantly improving reviewer productivity through reduced analysis volume.
Solution Approach 2:
The patent segments the codebase by separating frequently occurring patterns from unique sections. This segmentation enables reviewers to focus their attention on the smaller subset of unique code where sensitive information is most likely to be found, thereby maintaining measurement precision while enhancing productivity.
Data Source
AI summary
Code and text processing and analysis tools are provided for finding potentially sensitive information in a corpus of code. A request is received to analyze a corpus of code or text to find potentially sensitive information. Information unlikely to be sensitive is removed from the corpus of code or text to generate a modified copy of the corpus of code or text. Frequently occurring sections may also be removed as unlikely to be sensitive. An unpredictability score is determined for the remaining sections of the modified copy of the corpus of code or text, and the unpredictability score is used to prioritize unpredictable sections for review. Selectable section identifiers of the prioritized sections may be sorted in a code or text navigation interface and reviewed in a code or text viewing interface to determine whether the selected sections incorrectly contain sensitive information. If so, corrective actions may be triggered.