NLP Security Document Parsing for Faster Threat Mitigation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing cloud-based security systems present security-related data in unstructured, text-heavy documents that are difficult for users to comprehend, requiring manual analysis by technical professionals, leading to significant time delays in identifying and mitigating security issues.
Innovation Solution
A system utilizing natural language processing techniques to analyze unstructured documents, identify notable security features, and provide prescribed mitigation actions, reducing the need for manual analysis by technical professionals.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If unstructured text-heavy documents are used to present security data from multiple providers, then comprehensive security information is captured, but the document becomes difficult to comprehend and analyze
Solution Approach 1:
The patent segments the unstructured security document into structured components including security issues, affected resources, and mitigation actions. Each security issue is broken down into discrete elements that can be independently analyzed and processed, transforming the monolithic text-heavy document into organized, manageable units that improve comprehensibility while preserving comprehensive security information.
Solution Approach 2:
The patent extracts key security features from the unstructured document using natural language processing. Notable security features such as security issues, affected resources, and mitigation actions are extracted and isolated from the surrounding text, separating essential information from unnecessary content to improve document comprehensibility.
2Measurement precision
If manual analysis by technical security professionals is used to identify key security information, then accurate identification is achieved, but significant time delays occur
Solution Approach 1:
The patent introduces natural language processing technology as an intermediary between the unstructured security document and the security professional. The NLP system processes the document, extracts key security features, and presents structured findings to professionals, maintaining identification accuracy while dramatically reducing the time required for analysis.
Solution Approach 2:
The patent replaces the manual mechanical process of reading and analyzing text with automated natural language processing systems. The NLP technology performs semantic analysis, entity recognition, and information extraction that previously required human professionals to manually review entire documents, thereby reducing analysis time while maintaining accuracy.
3Reliability
If the complete unstructured document is reviewed to understand security issues, then thorough understanding is achieved, but the time required increases to hours or days
Solution Approach 1:
The patent performs preliminary processing of the security document by pre-processing the text, segmenting it into meaningful units, and pre-extracting potential security features before final analysis. This preliminary action prepares the data in advance, enabling faster and more reliable understanding of security issues without requiring complete manual review of the entire document.
Solution Approach 2:
The patent transforms the document from an unstructured text format with unknown information density to a structured format with identified key parameters such as security issues, affected resources, and mitigation actions. This parameter transformation allows the system to focus on critical information elements, achieving thorough understanding much faster than reviewing the complete unstructured document.
Data Source
Figure 1
Figure 2
Figure 3
AI summary
Disclosed herein is a system for minimizing the amount of time it takes to process and understand an unstructured, text-heavy document that includes important security-related information. The system uses a model that implements natural language processing techniques to semantically analyze an unstructured, text-heavy document, in order to identify notable security features useable to resolve the security issues in a more effective and efficient manner (e.g., without the need for a technical security professional). More specifically, the system receives an unstructured document that includes text which may be provided by multiple different security providers. The system is configured to apply the model to various text segments (e.g., a phrase, a sentence, a paragraph, etc.) included in the unstructured document to identify notable security features. The model can then associate the notable security features with a prescribed set of mitigation actions that can be implemented to resolve a security issue.