Device Policy Correlation for Multi-Source Configuration Conflicts
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Solution Overview
Problem
Conventional device management systems struggle to efficiently manage and enforce policies across devices due to dynamic and complex OEM configurations, leading to conflicts and inconsistencies that are difficult to detect and resolve.
Innovation Solution
A computer-implemented method using machine learning and text analytics to correlate and resolve conflicts between device management settings from multiple sources, providing a single interface for administrators to handle and prioritize settings based on predefined precedence orders.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If device management systems use multiple configuration sources (OEM, MDM, EMM, UEM), then device management capabilities are enhanced, but configuration conflicts and inconsistencies increase
Solution Approach 1:
The system performs preliminary correlation analysis of device configurations from multiple sources before deployment. By analyzing and identifying conflicts in advance using text analytics and machine learning, the system prevents inconsistent configurations from being applied to devices, thereby maintaining reliability while supporting multiple management sources.
2Adaptability or versatility
If device configurations are dynamically updated from multiple sources, then system flexibility improves, but conflict detection difficulty increases
Solution Approach 1:
The system introduces an intermediary correlation analysis component that sits between multiple configuration sources and the device deployment process. This intermediary uses text analytics and machine learning to automatically analyze configurations, identify conflicts, and provide resolution recommendations, thereby simplifying conflict detection while maintaining system flexibility.
3Measurement precision
If manual review of device configurations is performed, then conflict accuracy improves, but processing time increases
Solution Approach 1:
The system replaces manual mechanical review processes with automated machine learning-based text analytics. The machine learning models automatically analyze configuration texts, identify conflicts, and generate resolution recommendations with high accuracy, eliminating the need for manual review while maintaining detection precision and significantly improving processing speed.
4Reliability
If comprehensive configuration analysis is performed across all sources, then conflict identification improves, but system complexity increases
Solution Approach 1:
The system segments the comprehensive configuration analysis into distinct modular components: text analytics module, machine learning correlation module, conflict identification module, and resolution recommendation module. Each module handles a specific aspect of the analysis, making the overall system more manageable and maintainable while still providing comprehensive conflict identification across all configuration sources.
Data Source
AI summary
A method, system, and computer program product for correlating dynamic device configurations from multiple sources. The method may include identifying device management settings from a device management system. The method may also include receiving source settings from a second source. The method may also include analyzing individual words from the device management settings and the source settings. The method may also include analyzing strings, integers, and Booleans from the device management settings and the source settings. The method may also include identifying, based on the analyzing individual words and the analyzing strings, integers, and Booleans, corresponding settings from the device management settings and the source settings. The method may also include determining that the corresponding settings are conflicting settings. The method may also include flagging, based on the determining, conflicts of the corresponding settings.