Patch Risk Prediction Model for Distributed Infrastructure
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Solution Overview
Problem
IT organizations face challenges in predicting patch risks and building an optimal patching schedule across geographically distributed servers due to low accuracy of machine learning models and reliance on human tribal knowledge, especially with imbalanced training data and low success rates of software patches.
Innovation Solution
A risk prediction model is generated using a combination of supervised and unsupervised learning, augmented with data from monitoring and configuration data to identify high and low-risk devices, allowing for iterative patching and continuous model updates, and feature enrichment to improve classification accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If machine learning models are used to predict patch risk, then automation of risk prediction is improved, but model accuracy deteriorates due to imbalanced training data with low failure rates
Solution Approach 1:
The system implements feedback loops where patch outcomes are continuously fed back into the training data. Unsupervised learning components analyze post-implementation monitoring data to identify new failure patterns, which then enrich the training dataset. This iterative feedback mechanism allows the model to learn from actual outcomes and continuously improve accuracy despite initial data imbalance.
Solution Approach 2:
The system performs preliminary unsupervised learning analysis on historical data to pre-identify potential failure patterns and risk indicators before supervised training. This preliminary action creates enriched training features that highlight subtle failure modes, enabling the supervised model to achieve higher accuracy even with imbalanced data by pre-preparing the data landscape.
2Productivity
If patches are applied to all devices simultaneously, then patching speed is improved, but infrastructure disruption increases
Solution Approach 1:
The system segments the patching process into multiple phases and groups devices based on risk predictions. High-risk devices are identified and scheduled separately from low-risk devices, allowing staged implementation. This segmentation enables the system to maintain high patching speed while minimizing disruption by applying patches to lower-risk devices first or during less critical periods.
Solution Approach 2:
The system applies different patching strategies to different device groups based on their risk profiles. Critical infrastructure devices receive enhanced monitoring and staged patching, while non-critical devices can receive patches more aggressively. This local quality approach optimizes the balance between patching speed and disruption minimization for each specific device context.
3Measurement precision
If human tribal knowledge is used to build patching schedules, then contextual accuracy is improved, but time consumption and complexity increase
Solution Approach 1:
The system implements self-service through automated machine learning models that independently analyze historical data, identify patterns, and generate patching schedules without requiring extensive human expertise. The unsupervised learning components automatically discover risk indicators and patching optimal timings, eliminating the need for manual analysis of tribal knowledge while maintaining contextual accuracy through data-driven insights.
Solution Approach 2:
The system replaces the mechanical process of manual schedule building based on tribal knowledge with automated machine learning algorithms. The mechanical effort of human analysts reviewing historical data and constructing schedules is substituted by computational models that perform the same analysis automatically, dramatically reducing time consumption while maintaining or improving accuracy through systematic data processing.
4Measurement precision
If comprehensive monitoring is implemented for all devices, then prediction accuracy is improved, but system complexity and resource consumption increase
Solution Approach 1:
The system applies partial monitoring and analysis focused on high-risk devices and critical infrastructure components rather than uniformly comprehensive monitoring across all devices. By concentrating monitoring resources on the most impactful areas identified through preliminary risk assessment, the system achieves high prediction accuracy for critical patches while reducing overall system complexity and resource consumption compared to full-comprehensive monitoring.
Data Source
AI summary
Systems and techniques for implementing a change to a plurality of devices in a computing infrastructure include generating a risk prediction model, where the risk prediction model is trained using a combination of supervised learning and unsupervised learning and identifying, using the risk prediction model, a first set of devices from the plurality of devices having a low risk of failure due to implementing the change and a second set of devices from the plurality of devices having a high risk of failure due to implementing the change. A schedule is automatically generated for implementing the change to the first set of devices. The change is implemented on a portion of the first set of devices according to the schedule. The risk prediction model is updated using data obtained from implementing the change on the portion of the first set of devices.