Machine Learning Patch Automation for Centralized Server Updates
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Solution Overview
Problem
Existing server patching processes in enterprise computing systems are time-consuming, prone to human error, and lack centralized scheduling, coordination, and intelligent analysis, leading to potential reputational loss and user dissatisfaction due to system outages.
Innovation Solution
An intelligent patch management system integrating a user interface module, machine learning module, and centralized tracking module to automate, monitor, and assess server patching processes, utilizing machine learning to analyze vulnerabilities, schedule patches intelligently, and coordinate maintenance windows.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual server patching procedures are used, then flexibility in handling individual server issues is maintained, but the process becomes time-consuming and prone to human error
Solution Approach 1:
The system enables self-service through automated patch scheduling and execution. The machine learning module automatically analyzes server vulnerabilities, schedules patches during optimal maintenance windows, and executes patching operations without requiring manual intervention, thereby reducing both time consumption and human error while maintaining reliability
Solution Approach 2:
The patent replaces manual mechanical patching operations with an automated computer-based system. The centralized tracking module and machine learning algorithms substitute for human decision-making and execution, enabling automated vulnerability assessment, scheduling, and patch deployment across multiple servers
2Reliability
If centralized scheduling and coordination mechanisms are implemented, then patching consistency and coordination across distributed servers is improved, but system complexity increases
Solution Approach 1:
The centralized tracking module serves multiple functions including vulnerability assessment, patch scheduling, coordination, and monitoring within a single integrated system. This multi-functional approach improves patching consistency across distributed servers while managing complexity by consolidating operations rather than multiplying separate systems
Solution Approach 2:
The system implements feedback mechanisms where the machine learning module continuously monitors patch execution results and uses this information to refine future scheduling decisions. This feedback loop ensures consistent patching outcomes while adapting to changing system conditions, reducing the need for complex manual coordination
3Productivity
If intelligent analysis and learning from previous patching processes is added, then patching efficiency and decision-making accuracy are improved, but computational resources and system complexity increase
Solution Approach 1:
The machine learning module performs preliminary analysis of server vulnerabilities, maintenance window availability, and patch dependencies before actual patching occurs. By pre-scheduling and pre-analyzing patch requirements, the system improves patching efficiency and reduces the computational burden during execution, as the heavy analysis work is completed in advance
Data Source
AI summary
A machine learning computing system identifies a vulnerability associated with a server. Based on information associated with the server and a knowledge base, the computing system schedules an interval for patching the server in a centralized tracking module. Based on the knowledge base and the vulnerability, the computing system creates, validates, and deploys the patch job. During patch job execution, the computing system monitors the status of the patch job at the server and transmits status updates to a user interface module. After expiration of the interval, the computing system generates an assessment report for the executed patch job. The computing system updates the knowledge base based on the assessment report to improve future decisioning processes. Based on the success or failure of the patch job, the computing system, upon a failure indication, automatically reschedules an interval for patching the server.