AI Web Content Processing for Vulnerability Assessments
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Solution Overview
Problem
Existing security management systems face challenges in efficiently processing and maintaining vulnerability data from sources like HTML and CSS, leading to significant maintenance efforts and potential cyber threats due to volatility in these data sources.
Innovation Solution
A machine learning-based approach utilizing natural language processing (NLP), inductive logic programming (ILP), and automation to process web content, generate vulnerability checks, and determine exploitability, replacing traditional parsers and enabling efficient data source addition and maintenance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional parsers are used to process vulnerability data from HTML and CSS sources, then vulnerability assessments can be performed, but significant maintenance effort and time are required due to volatility in data sources
Solution Approach 1:
The patent replaces traditional mechanical parsers (which manually interpret HTML/CSS tags and structures) with an AI-based system that uses natural language processing and machine learning models to automatically understand and process vulnerability data from web sources, eliminating the need for continuous manual maintenance of parser configurations
Solution Approach 2:
The AI-based system automatically adapts to changes in vulnerability data sources without human intervention. The machine learning models continuously learn from new data patterns and self-adjust to maintain accurate vulnerability assessments, eliminating the need for manual parser updates and reconfiguration
2Productivity
If traditional parsers key off HTML and CSS tags, then vulnerability content can be extracted, but significant maintenance is required to keep parsers updated
Solution Approach 1:
The patent replaces the mechanical parsing approach (which relies on fixed HTML/CSS tag structures) with AI-based natural language processing that understands vulnerability content semantically, allowing the system to process various web formats without maintaining complex parser rules for each format
Solution Approach 2:
The AI-based system provides universal processing capability that can handle multiple vulnerability data formats and web source types through a single unified approach, eliminating the need to maintain separate parsers for different HTML/CSS structures and vulnerability disclosure formats
3Reliability
If developers maintain parsers to handle vulnerability data volatility, then data processing remains accurate, but time is consumed that could be spent on reducing technical debt or adding features
Solution Approach 1:
The AI-based vulnerability processing system is self-maintaining and self-updating through continuous learning from new vulnerability data patterns. This eliminates the need for developers to spend time maintaining parser accuracy, allowing them to focus on adding new features and reducing technical debt while the system automatically adapts to data volatility
Solution Approach 2:
By replacing manual parser maintenance with AI-based automated processing, the system maintains high data processing accuracy without requiring developer intervention, thereby freeing up developer time for more valuable activities
Data Source
AI summary
Various embodiments include systems and methods to implement processing of web content for vulnerability assessments. A plurality of documents comprising web content may be obtained from multiple different web sources, and the documents may be parsed to determine a set of discrete document chunks. Parsing the documents includes determining whether a document satisfies a segmentation condition for segmenting the document into multiple discrete document chunks using a named-entity recognition system configured to segment the document based at least in part on a vulnerability identification. The discrete document chunks may be stored in a database, where vulnerability information is indexed such that each respective entry in the database corresponds to a respective vulnerability identification and a respective discrete document chunk.