Network Risk Remediation via Machine Learning Models
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Solution Overview
Problem
Network administrators face difficulties in understanding and addressing network risk due to the complexity of identifying contributing factors and severity of risks, as well as lacking actionable insights for remediation.
Innovation Solution
The use of machine learning models, specifically natural language processing and deep learning techniques, to assess, interpret, and remediate network risk by processing risk reports, identifying contributing factors, and suggesting actionable mitigation strategies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If machine learning models are used to automatically assess and remediate network risk, then productivity and automation extent are improved, but device complexity increases
Solution Approach 1:
The system enables automated self-service through machine learning models that independently assess risk reports, identify contributing factors, generate remediation actions, and predict outcomes without requiring manual analysis. The ML models process risk data autonomously, reducing the need for human intervention in routine risk management tasks.
Solution Approach 2:
Machine learning models serve as intermediaries between raw risk data and actionable insights. The models translate complex risk reports into structured risk scores, identify underlying factors, and generate remediation recommendations, acting as a bridge that simplifies the interaction between network administrators and complex risk data.
2Measurement precision
If machine learning models process and analyze risk reports to identify contributing factors, then measurement precision is improved, but difficulty of detecting and measuring increases
Solution Approach 1:
Manual analysis of risk reports is replaced with machine learning-based automated analysis. The ML models process risk report text, extract contributing factors, and generate risk assessments algorithmically, substituting human analytical processes with automated computational methods that improve consistency and precision.
Solution Approach 2:
The system creates structured representations (copies) of unstructured risk report data through ML processing. The models generate standardized risk assessments, factor identifications, and remediation recommendations that replicate the essential information from diverse risk reports in a consistent, analyzable format.
Data Source
AI summary
A method, computer system, and computer program product are provided for mitigating network risk. A plurality of risk reports corresponding to a plurality of network devices in a network are processed to determine a multidimensional risk score for the network. The plurality of risk reports are analyzed using a semantic analysis model to identify one or more factors that contribute to the multidimensional risk score. One or more actions are determined using a trained learning model to mitigate one or more dimensions of the multidimensional risk score. The outcomes of applying the one or more actions are presented to a user to indicate an effect of each of the one or more actions on the multidimensional risk score for the network.