Risk Prioritization via Data Cleaning and CWE Mapping
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Solution Overview
Problem
Current methods lack effective systems for prioritizing and managing risks associated with data vulnerabilities, weaknesses, defects, bugs, and failures across various settings, failing to systematically map these issues to compliance standards like Common Weakness Enumeration (CWE) and generate actionable reports.
Innovation Solution
A method and system that receive and analyze data sets for duplicates, evaluate false positives and tool errors, create an input file, compare it to compliance standards, map findings to CWE standards, and generate risk prioritization reports, using a rules engine to identify and prioritize weaknesses, defects, bugs, flaws, and vulnerabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If data sets are analyzed without removing duplicates and evaluating false positives, then analysis coverage is maintained, but measurement precision deteriorates due to inaccurate risk identification
Solution Approach 1:
The system performs preliminary actions by removing duplicates and evaluating false positives/negatives before the main risk analysis. This preprocessing step ensures that the subsequent compliance standard comparison operates on clean, deduplicated data, improving measurement precision without significantly increasing overall complexity.
Solution Approach 2:
The patent introduces an intermediary processing layer between raw data reception and compliance analysis. This intermediary layer handles duplicate removal, false positive evaluation, and data cleaning, acting as a mediator that improves the quality of input data for the main analysis engine.
2Reliability
If comprehensive compliance standards are applied to identify all weaknesses, then reliability improves, but productivity deteriorates due to extensive analysis time
Solution Approach 1:
The system extracts and removes duplicate findings from the data set before compliance analysis. By taking out redundant information that would otherwise require processing against compliance standards, the system maintains comprehensive coverage for unique issues while reducing overall analysis time and improving productivity.
Solution Approach 2:
The patent applies partial action by focusing compliance analysis only on unique, validated findings after duplicate removal and false positive evaluation. Rather than applying comprehensive standards to all raw data including duplicates, the system applies standards selectively to cleaned data, maintaining reliability while improving analysis speed.
3Loss of information
If detailed risk prioritization and mapping to CWE standards is performed, then information completeness improves, but loss of time increases due to extensive processing
Solution Approach 1:
The system performs preliminary mapping to CWE standards and risk prioritization on cleaned, deduplicated data before final report generation. This preliminary action ensures that time-consuming mapping and prioritization processes operate on unique findings only, reducing overall processing time while maintaining complete risk information in the final reports.
Data Source
AI summary
Methods for managing and prioritizing risk include receiving a data set and analyzing the data set for duplicates, false positives, false negatives, and tool errors. Said duplicates, false positives, false negatives and results of tool errors are removed from the data set, creating an input file. The input file is compared against compliance standards to identify any weaknesses, defects, bugs, flaws, vulnerabilities, and/or failures in the input file. The compared input file is mapped to Common Weakness Enumeration standards. A risk prioritization can be generated based on the mapped results. At least one report can be generated based on the risk prioritization.