Cloud Tag Standardization for Inconsistency Detection and Remediation
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Solution Overview
Problem
Inconsistent tagging of assets in cloud-native networks leads to challenges in policy enforcement and identification, as inconsistencies can unknowingly arise during asset onboarding, especially with thousands of assets in cloud-based systems.
Innovation Solution
A method involving collecting tags from all resources, converting them to a desired format, calculating similarity scores, creating tag pairs, and using machine learning models to identify and suggest accurate tags, with visual representations and machine learning models for tag suggestions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual tagging of assets is performed in cloud environments, then asset identification and policy enforcement are enabled, but tag inconsistencies such as typos and semantic errors occur leading to security risks
Solution Approach 1:
The system continuously monitors and compares tags across assets, providing feedback through similarity scoring and inconsistency detection. When tag variations are detected (e.g., 'prod' vs 'production'), the system analyzes frequency patterns and provides corrective feedback to maintain tag consistency, thereby preventing security risks while preserving ease of tagging.
Solution Approach 2:
The patent replaces manual mechanical tagging processes with automated computational analysis. Machine learning models and algorithms automatically detect tag inconsistencies, calculate similarity scores, and suggest corrections, substituting human manual verification with automated systems that eliminate typos and semantic errors while maintaining ease of operation.
2Reliability
If automated tag detection and remediation systems are implemented, then tag consistency and security are improved, but system complexity increases
Solution Approach 1:
The system performs self-service by automatically detecting tag inconsistencies, calculating similarity scores, and generating remediation suggestions without requiring complex external intervention. The automated analysis and comparison mechanisms operate autonomously to maintain tag consistency, reducing the need for complex manual management processes.
Solution Approach 2:
The patent transforms the complexity issue by changing parameters from manual verification to automated computational analysis. By using similarity scoring algorithms and frequency-based remediation, the system manages complexity through standardized computational parameters rather than complex procedural workflows, maintaining reliability while controlling system complexity.
3Measurement precision
If comprehensive tag analysis is performed across all assets, then all inconsistencies are detected, but processing time and computational resources increase
Solution Approach 1:
The system applies partial action by focusing analysis on tag pairs that exceed a similarity threshold, rather than uniformly analyzing all possible tag combinations. This selective approach maintains high detection accuracy for inconsistent tags while reducing unnecessary computational processing of already-consistent tags, thereby balancing precision with processing time.
Solution Approach 2:
The patent implements preliminary action by establishing similarity thresholds and frequency benchmarks before full analysis. Tags are pre-filtered based on initial similarity scores, and remediation suggestions are prepared in advance based on common patterns, enabling faster comprehensive analysis while maintaining detection accuracy across all assets.
Data Source
AI summary
Systems and methods for detecting and remediating inconsistent tags in cloud-native networks include collecting tags from all resources in a cloud environment; converting each of the tags to a desired format and extracting unique tags in the desired format; calculating a similarity score between all of the unique tags in the desired format and creating tag pairs based on the similarity scores; and selecting a suggested tag for each of the tag pairs based on a number of appearances of each of the tags in the tag pairs. In various embodiments the steps can further include identifying a new resource in the cloud environment; and utilizing one or more machine learning models to determine if the new resource has inaccurate tags, and providing tag suggestions based thereon.