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 industry best practices and keyword weights to determine confidence values, reducing the need for manual inspection.

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 and time requirements become unreasonably high

Engineering Contradiction:

Improvesecurity posture assessmentVSAvoidinspection efficiency

Core Design Contradiction:

ReliabilityVSProductivity

Solution Approach 1:

The patent replaces manual mechanical inspection with an automated machine learning-based system that scrapes web content and application interfaces to detect configuration settings. The ML model automatically analyzes detected settings against best practices, eliminating the need for manual review while maintaining or improving detection accuracy.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system enables self-service security assessment by automatically detecting SaaS applications, scraping their configuration settings, and evaluating them against security best practices without requiring human intervention. The automated pipeline performs continuous monitoring and assessment independently.

Inventive Principle:

Principle #25Self-service

2Reliability

If manual inspection of configuration settings is performed, then security risks can be identified, but the process is prone to human error and misconfiguration

Engineering Contradiction:

Improvedetection accuracyVSAvoidoperational complexity

Core Design Contradiction:

ReliabilityVSEase of operation

Solution Approach 1:

The patent replaces human manual inspection with an automated machine learning system that objectively evaluates configuration settings against encoded best practices. This eliminates human error, fatigue, and inconsistency while maintaining high detection accuracy through systematic automated analysis.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

3Productivity

If automated detection systems are implemented, then productivity and coverage improve, but system complexity increases

Engineering Contradiction:

Improvedetection throughputVSAvoidsystem architecture complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The patent segments the security assessment system into distinct modular components: SaaS application detection module, web content scraping module, application interface scraping module, machine learning model for setting detection, and best practice evaluation module. Each component handles a specific task, making the overall complex system manageable and maintainable through clear separation of concerns.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The machine learning model serves multiple functions: it detects configuration settings from scraped content, evaluates them against best practices, and identifies security risks. This multi-functional approach reduces the need for separate specialized tools while maintaining comprehensive security assessment capabilities.

Inventive Principle:

Principle #6Universality (Multi-functionality)

4Reliability

If comprehensive configuration settings are monitored, then security coverage improves, but the amount of data to be processed increases

Engineering Contradiction:

Improvesecurity coverageVSAvoiddata processing volume

Core Design Contradiction:

ReliabilityVSQuantity of substance

Solution Approach 1:

The patent extracts only the relevant configuration settings from the scraped web content and application interfaces using the machine learning model. Instead of processing all scraped data, the system identifies and focuses on specific security-relevant settings, reducing the volume of data that requires detailed analysis while maintaining comprehensive security coverage.

Inventive Principle:

Principle #2Taking out (Extraction)

Data Source

PatentUS12475177B2Automated machine learning-based detection of correct implementations for configuration settings of software-as-a-service applications

Publication Date: 2025.11.18 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.