SaaS Compliance Classifier Using CNN Webpage Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Manual review of numerous software-as-a-service (SaaS) products for compliance with regulatory protocols, such as GDPR, is inefficient and impractical at scale, as it requires human analysis of product specifications and documentation, leading to audit and compliance challenges for organizations.
Innovation Solution
A system utilizing machine learning models, such as Convolutional Neural Networks (CNN), to automatically detect compliance by extracting features from webpage content and determining compliance scores based on predefined protocols, allowing for automated risk assessment and proactive measures like traffic blocking or recommending compliant alternatives.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual review of product specifications and documentation is performed to determine compliance, then compliance accuracy is improved, but productivity deteriorates due to the time-consuming nature of human analysis
Solution Approach 1:
The patent replaces the mechanical human review process with an automated machine learning system that uses Convolutional Neural Networks (CNNs) to analyze webpage content and determine compliance. The system automatically extracts features from HTML content, applies trained models to classify compliance status, and generates results without human intervention, thereby maintaining accuracy while dramatically improving productivity.
Solution Approach 2:
The patent introduces a machine learning model as an intermediary between the compliance assessment requirement and the final compliance determination. The trained CNN model acts as a mediator that processes webpage content and translates it into compliance classifications, bridging the gap between raw information and compliance decisions while eliminating the need for manual human review.
2Measurement precision
If manual compliance review is performed for each SaaS product, then compliance determination accuracy is improved, but loss of time increases due to the extensive human effort required
Solution Approach 1:
The patent applies preliminary action by pre-training machine learning models on labeled compliance data before deployment. The CNN models are trained in advance on datasets containing webpage content and corresponding compliance labels, enabling them to rapidly classify new products without requiring real-time human analysis. This pre-computed knowledge allows the system to maintain accuracy while reducing assessment time to minutes per product.
3Productivity
If automated machine learning models are used to detect compliance, then productivity is improved through automation, but device complexity increases due to the implementation of CNN models and feature extraction systems
Solution Approach 1:
The patent segments the compliance assessment system into distinct functional modules: a feature extraction component that processes HTML content, a model training component that creates compliance classifiers, and an inference component that applies models to new products. This modular segmentation manages system complexity by organizing functions into separate, manageable units while maintaining high automated productivity through their coordinated operation.
Data Source
AI summary
The present application discloses a method, system, and computer system for automatically detecting protocol compliance of applications. The method includes determining a URL of a webpage for a software-as-a-service (SaaS) product, extracting body text from the webpage, and using a classifier to determine whether the SaaS product is compliant with one or more protocols.