Machine-Learning Confidence Ratings for IT Change Risk
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Solution Overview
Problem
Risk assessments for IT infrastructure changes are often based on limited human analysis and can be incorrect, leading to misclassification of changes as low or medium risk when they actually pose a high risk, resulting in potential IT downtime and resource wastage.
Innovation Solution
An automated system using historical data and machine learning models to generate a change confidence rating for IT changes, identifying high-risk changes for additional scrutiny and recommending implementer training, thereby improving the accuracy of risk assessment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If human analysis is used to assess IT change risk, then the process is simple and quick, but the accuracy and reliability of risk assessment deteriorates
Solution Approach 1:
The patent replaces human-based mechanical risk assessment with an automated machine learning system. The ML model processes historical change data, technology item information, and implementer profiles to generate risk predictions, eliminating the need for manual analysis while improving accuracy and consistency in risk assessment.
Solution Approach 2:
The system enables self-service risk assessment by automatically evaluating changes without requiring human expertise. The ML model independently analyzes historical data patterns, identifies risk factors, and generates confidence ratings, allowing the system to assess its own performance and reduce dependency on human judgment.
2Reliability
If automated ML-based risk assessment is implemented, then risk assessment accuracy improves, but system complexity and implementation cost increases
Solution Approach 1:
The system performs preliminary actions by pre-processing and storing historical change data, technology item information, and implementer profiles in a structured format. This preparation enables the ML model to quickly generate accurate risk assessments without requiring complex real-time analysis during actual change implementation.
Solution Approach 2:
The patent introduces an intermediary layer in the form of a machine learning model that mediates between raw data and risk assessment decisions. This intermediary processes historical data patterns and transforms them into actionable risk predictions, simplifying the overall system architecture while maintaining high accuracy.
3Measurement precision
If comprehensive historical data is collected and analyzed, then change confidence rating accuracy improves, but data processing time and resource consumption increases
Solution Approach 1:
The system applies partial action by selecting and processing only the most relevant historical data features and patterns necessary for accurate risk assessment. Rather than analyzing all available data comprehensively, the ML model identifies and processes key risk indicators, reducing processing time while maintaining measurement precision through targeted data selection.
Data Source
AI summary
Techniques for identifying a risk of failure for a planned change to a technology item in a technology infrastructure are disclosed. Historical technology change data are retrieved, including data relating to at least one of: (i) prior technology changes by an implementer or coordinator of the planned change, (ii) a process relating to the planned change, or (iii) prior technology changes relating to the technology item or a related technology item. A change confidence rating for a risk of failure for the planned change is generated, based on the historical technology change data. The change confidence rating is applied, including at least one of: (i) generating a user interface illustrating a risk level for the technology change, (ii) flagging for or initiating a supplemental review of the technology change, or (iii) recommending implementer training relating to the technology change.