SaaS Configuration Detection Using ML and Content Scraping

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Solution Overview

Problem

Manually inspecting configuration settings for Software-as-a-Service (SaaS) security posture management (SSPM) is resource-intensive and prone to security risks due to misconfigured settings, which serve as additional attack vectors.

Innovation Solution

An automated SSPM analysis system uses a machine learning model to predict correct configuration settings by scraping web and application content, leveraging natural language processing and a neural network trained with configuration setting weights to ensure compliance with industry standards.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If manual inspection of configuration settings is performed, then security posture can be assessed, but resource consumption increases and security risks arise due to human error

Engineering Contradiction:

Improvesecurity posture assessment accuracyVSAvoidmanual resource efficiency

Core Design Contradiction:

ReliabilityVSProductivity

Solution Approach 1:

The system enables automated self-assessment of security posture by having the SSPM system automatically scrape web content, extract configuration settings, and evaluate them against best practices without requiring manual human inspection, thus eliminating human error while maintaining assessment accuracy

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The patent replaces the mechanical process of manual configuration inspection with an automated computational system that uses web scraping, natural language processing, and machine learning models to assess security posture, thereby improving efficiency while maintaining or enhancing reliability

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Productivity

If automated web scraping is performed to detect configuration settings, then productivity increases, but measurement precision may be insufficient without proper training data

Engineering Contradiction:

Improveautomated detection speedVSAvoidconfiguration setting detection accuracy

Core Design Contradiction:

ProductivityVSMeasurement precision

Solution Approach 1:

The system performs preliminary web scraping to collect configuration settings and security documentation before training the machine learning model, preparing the training corpus in advance to enable accurate automated detection without compromising precision

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The machine learning model is trained using feedback from labeled training data consisting of known correct and incorrect configuration settings, allowing the system to continuously improve its detection precision while maintaining high automated processing speeds

Inventive Principle:

Principle #23Feedback

3Difficulty of detecting and measuring

If comprehensive web content scraping is performed, then detection capability improves, but device complexity increases

Engineering Contradiction:

Improveconfiguration setting detection capabilityVSAvoidsystem architecture complexity

Core Design Contradiction:

Difficulty of detecting and measuringVSDevice complexity

Solution Approach 1:

The system segments the complex detection task into distinct modular components: a web scraping module that collects configuration data, a natural language processing module that extracts settings, and a machine learning evaluation module that assesses security posture, thereby improving detection capability while managing complexity through modular design

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The patent introduces natural language processing as an intermediary layer between web content scraping and machine learning analysis, transforming unstructured web content into structured configuration data that the ML model can efficiently process, thus enhancing detection capability without proportionally increasing overall system complexity

Inventive Principle:

Principle #24Intermediary (Mediator)

Data Source

PatentUS20260044568A1Automated machine learning-based detection of correct implementations for configuration settings of software-as-a-service applications

Publication Date: 2026.02.12 PALO ALTO NETWORKS INC

AI summary

An automated software-as-a-service (SaaS) security posture management (SSPM) system disclosed herein detects and maintains security posture for SaaS applications according to correct implementation of configuration settings. Based on detecting a previously unseen SaaS application with unknown implementation of configuration settings, the SSPM system scrapes the Internet for web content for the SaaS application and preprocesses/inputs the web content into a machine learning model to obtain predictions of correct/incorrect implementation of configuration settings as output. Based on the predictions not having sufficiently high confidence, the SSPM system obtains additional application content by logging into the SaaS application and scraping locally rendered pages therein. The application content is preprocessed/input to the machine learning model to obtain additional high confidence predictions.