Faulty Control Detection Using Shared Incident-Code Embeddings
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Solution Overview
Problem
Existing systems struggle to identify which controls are related to incidents, making it difficult to determine which controls should be modified or improved to prevent future incidents, and they are unable to identify faulty controls before incidents occur, leading to inefficient and resource-intensive training of machine learning models.
Innovation Solution
A computing system uses machine learning to map incident data and control data to the same vector space, identifying faulty controls and generating recommendations for modifications, and trains a machine learning model using a weighted training dataset to improve the efficiency and accuracy of identifying faulty controls.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If machine learning models are trained to identify faulty controls, then the ability to detect and prevent incidents is improved, but the training time and computational resources required increase significantly
Solution Approach 1:
The system performs preliminary actions by proactively identifying faulty controls before incidents occur. The machine learning model continuously monitors and analyzes control configurations, detecting potential faults in advance, which allows for preventive maintenance and avoids the need for extensive retraining after incidents happen.
Solution Approach 2:
The system implements self-service through automated machine learning models that continuously learn from incident data and control information without requiring manual intervention. The model automatically updates its knowledge base and identifies faulty controls, reducing the need for repeated manual training processes.
2Measurement precision
If machine learning models are trained with comprehensive control data to improve detection accuracy, then the precision of fault identification increases, but the complexity of the training process and data processing increases
Solution Approach 1:
The system segments the training process by dividing control data into manageable subsets and processing them through the machine learning model in stages. This allows the model to learn from diverse control configurations without being overwhelmed by the entire dataset at once, reducing training complexity while maintaining detection accuracy.
Solution Approach 2:
The machine learning model acts as an intermediary that automatically processes and synthesizes comprehensive control data. It transforms raw control configurations and incident reports into structured knowledge, eliminating the need for manual data processing and reducing the complexity of training while improving detection precision.
3Productivity
If existing systems manually analyze controls to identify faults, then resource consumption is reduced, but the productivity and speed of fault identification decrease
Solution Approach 1:
The system replaces manual mechanical analysis of controls with automated machine learning models. The ML model processes control data and identifies faults algorithmically, dramatically increasing identification speed while optimizing resource consumption through efficient automated processing rather than human manual review.
Solution Approach 2:
The system changes the operational parameters by transitioning from manual analysis to automated machine learning processing. This parameter change enables the system to handle larger volumes of control data more efficiently, improving productivity while managing computational resources through optimized algorithmic processing.
Data Source
AI summary
In some aspects, a computing system may use machine learning to determine whether a control is faulty or generate recommendations to make a modification to a control. may identify a portion of problematic computer code that implements the faulty control through the use of machine learning. A computing system may use machine learning to generate embeddings that map incident data and control data (e.g., computer-readable code of a control) to the same vector space. Further, a computing system may use a weighting mechanism that may be used to weight each sample used to train a machine learning model, which may allow a model to train more efficiently.