Ranked URL Scraping for Unified SaaS Security Feature Prediction
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Solution Overview
Problem
SaaS applications have varying levels of security feature implementations, leading to inefficiencies in evaluating security risks and loss of learning quality in predictive models due to separate evaluation of each feature, which overlooks potential correlations.
Innovation Solution
An automated pipeline that generates intelligent search engine queries for security features, ranks URLs based on relevance, and uses a machine learning model to predict confidence values for multiple security features simultaneously, integrating HTML content analysis to identify implemented features.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If separate evaluation of each security feature is performed, then individual feature assessment is achieved, but efficiency is reduced and learning quality is lost due to omitted correlations
Solution Approach 1:
The patent combines multiple security feature evaluations into a single unified model that processes all features simultaneously. The system integrates evaluation of access management, data retention, policy misconfiguration, and other security features in one computational framework, preserving correlations between features while improving evaluation efficiency through consolidated processing.
2Productivity
If multiple security features are evaluated simultaneously, then evaluation efficiency is improved and learning quality is preserved, but system complexity increases
Solution Approach 1:
The patent implements a universal evaluation model that handles multiple security features through a single multi-functional system. The unified model can evaluate different security features (access management, data retention, policy misconfiguration, etc.) simultaneously using the same computational framework, avoiding the need for separate specialized systems for each feature while maintaining comprehensive evaluation capabilities.
3Loss of information
If public facing documents are analyzed manually, then detailed security feature information is obtained, but time consumption increases
Solution Approach 1:
The patent replaces manual analysis of public facing documents with an automated machine learning-based system. The unified model automatically processes documentation to extract and evaluate security feature implementations, substituting human manual review with computational analysis that achieves the same information extraction goals without the time cost of manual document analysis.
Data Source
AI summary
Automated attribute scraping for security feature implementation with a single trained machine model across security features improves prediction quality and efficiency of predictions. A security feature implementation prediction system (system) generates search engine queries for each security feature based on high importance tokens for the security feature. The system ranks URLs returned from each search engine query for relevance, then preprocess and inputs content for top-ranked URLs into the trained machine learning models. The system identifies implemented security features output based on confidence values output by the trained machine learning model and identifies sentences that describe the implementations in corresponding content for top-ranked URLs.