SaaS Application Auto-Tagging Across Security Naming Variations
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Solution Overview
Problem
The manual process of distinguishing and tagging sanctioned Software-as-a-Service (SaaS) applications within an organization is labor-intensive and inefficient, especially with the growing number of applications and increasing vulnerability due to sensitive data exposure, making it difficult to manage security effectively.
Innovation Solution
A system that automatically identifies and tags sanctioned SaaS applications by correlating identifying data from different security perspectives using fuzzy matching and creating mappings among application names, leveraging the identity access and management (IAM) system as the authoritative source for sanctioned applications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual processes are used to distinguish and tag sanctioned SaaS applications, then accuracy in identification can be maintained, but productivity and efficiency deteriorate due to labor-intensive operations
Solution Approach 1:
The system enables automatic self-tagging of SaaS applications by correlating identifying data across security systems and using fuzzy matching algorithms to autonomously determine sanctioned status without requiring manual human intervention for each application review
Solution Approach 2:
The patent replaces the manual mechanical process of security personnel reviewing and tagging applications with an automated computational system that uses fuzzy matching algorithms and data correlation to automatically identify and tag sanctioned SaaS applications
2Reliability
If comprehensive security coverage is achieved across all SaaS applications, then security posture improves, but device complexity and system integration requirements worsen
Solution Approach 1:
The system provides a universal tagging mechanism that works across multiple different security systems and perspectives (IAM, CASB, SSPM, DLP) by correlating identifying data and creating unified mappings, enabling single security coverage for diverse SaaS applications through a common approach
Solution Approach 2:
The patent introduces an intermediary correlation system that sits between multiple security systems and the tagging process, using fuzzy matching as a mediator to bridge naming variations and identify the same applications across different security perspectives without requiring direct integration between all security systems
3Adaptability or versatility
If fuzzy matching is used to correlate application names across security systems, then adaptability to naming variations improves, but measurement precision may worsen due to potential false matches
Solution Approach 1:
The system moves beyond single-dimension exact string matching by incorporating multiple dimensions of identifying data (application names, URLs, features, metadata) from different security perspectives, using fuzzy matching across these multiple dimensions to improve adaptability while maintaining precision through cross-validation
Data Source
AI summary
A system has been created to automatically identify and tag sanctioned applications. The disclosed auto-tagging system correlates identifying data of the different security perspectives based on fuzzy matching and creates mappings among application names of the different security perspectives based on correlations in the identifying data. With the mappings, groups of sanctioned applications can be automatically tagged (or recommended for tagging) as sanctioned across the security perspectives despite variations in naming. This automated, rapid propagation of sanctioned state by tagging provides a complete end-to-end view into the state of SaaS applications used in a customer environment and quickly secures these applications.