Software Reconciliation Framework for OS Patch Management
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Solution Overview
Problem
Current compliance and configuration control tools face challenges in efficiently reconciling and associating granular file and directory changes with authorized operating system updates or patches, leading to increased effort for system administrators and reduced tool effectiveness.
Innovation Solution
The Software Reconciliation Framework (SRF) tool compares change data with software manifests to identify and promote approved changes, marking non-matching changes for further analysis, thereby reducing the burden on administrators and enhancing change management processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If compliance tools monitor all file changes granularly, then change detection precision is improved, but administrator workload and time consumption increase significantly
Solution Approach 1:
The patent introduces a software manifest as an intermediary data structure that contains pre-defined information about authorized software updates. This manifest acts as a mediator between the granular file change monitoring system and the administrator, automatically matching detected changes against known authorized updates without requiring administrator intervention for each individual change.
Solution Approach 2:
The system performs preliminary actions by pre-generating and storing software manifests that contain information about expected file changes from authorized software updates. This preliminary preparation allows the system to automatically reconcile and approve changes that match the manifest, eliminating the need for administrators to manually review these pre-authorized changes.
2Reliability
If the system reconciles every detected change manually, then change approval accuracy is improved, but processing speed and productivity decrease
Solution Approach 1:
The patent segments the change approval process into two distinct paths: automated approval for changes that match the software manifest (high-volume, routine changes) and manual review for changes that do not match (low-volume, exceptional changes). This segmentation allows the system to maintain high accuracy through manifest-based verification while achieving high productivity through automated processing of routine changes.
Solution Approach 2:
The system implements self-service by enabling automated self-approval of software update changes through manifest matching. The reconciliation process automatically compares detected file changes against the software manifest and autonomously approves matching changes without administrator intervention, reserving manual review only for non-matching exceptional cases.
3Reliability
If the system monitors all file changes in detail, then compliance control effectiveness is improved, but system complexity and operational overhead increase
Solution Approach 1:
The software manifest serves as an intermediary that simplifies the reconciliation process by containing pre-processed information about authorized updates. Instead of requiring the system to analyze and interpret every granular file change against compliance policies, the manifest provides a ready-reference structure that automatically identifies authorized changes, reducing operational complexity while maintaining compliance effectiveness.
Data Source
AI summary
Apparatus and methods are disclosed for implementing software reconciliation frameworks to process changes detected to software installed on computer hosts. According to one embodiment, a method includes receiving change data describing changes to one or more software components stored on a computer-readable storage device, determining installed software on a computer associated with the computer-readable storage device, receiving a manifest comprising a description of file changes associated with a software patch or update for the installed software, and comparing the change data to the manifest. Based on the comparing, if the change data matches the manifest, the changes are promoted, and if the change data does not match the manifest, the changes are marked for further analysis.