ML-Based IT Vulnerability Change Request Matching
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Solution Overview
Problem
In information technology environments, identifying and addressing vulnerabilities often requires multiple change requests, which can be redundant and inefficient, as existing systems lack automated methods to match vulnerability responses with existing change requests, leading to duplicate work and increased downtime.
Innovation Solution
A process utilizing a trained machine learning model to automatically match new vulnerability responses with existing change requests, reducing the need for duplicate submissions by associating identified vulnerabilities with closest matching change requests, thereby streamlining the remediation process and avoiding redundant work.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple change requests are created to address vulnerabilities, then comprehensive remediation is achieved, but redundant work and increased downtime occur
Solution Approach 1:
The system combines multiple change requests into a single unified change request by automatically identifying when multiple vulnerability responses target the same IT assets. The machine learning model analyzes change request descriptions, asset mappings, and vulnerability data to detect overlaps, then merges them into one comprehensive change request that addresses all identified vulnerabilities simultaneously, eliminating redundant submissions and reducing downtime.
Solution Approach 2:
The system creates a universal change request template that can address multiple different vulnerabilities across various IT assets through a single submission. The machine learning model generates comprehensive change requests that incorporate multiple vulnerability responses, allowing one change request to serve multiple remediation purposes rather than requiring separate requests for each vulnerability.
2Reliability
If multiple change requests are created to address vulnerabilities, then comprehensive remediation is achieved, but duplicate work and process complexity increase
Solution Approach 1:
The system combines multiple change requests into a single unified change request by automatically identifying when multiple vulnerability responses target the same IT assets. The machine learning model analyzes change request descriptions, asset mappings, and vulnerability data to detect overlaps, then merges them into one comprehensive change request that addresses all identified vulnerabilities simultaneously, eliminating redundant submissions and reducing downtime.
Solution Approach 2:
The system implements feedback loops where the machine learning model continuously learns from merged change request outcomes. By analyzing whether merged change requests successfully address all vulnerabilities and whether they require splitting, the system refines its merging decisions, improving the accuracy of change request consolidation and reducing management complexity over time.
3Measurement precision
If manual matching of vulnerability responses to change requests is performed, then accurate association is achieved, but time and resource consumption increase
Solution Approach 1:
The system enables self-service automated matching where the machine learning model independently performs the association of vulnerability responses with existing change requests without human intervention. The model analyzes change request descriptions, maps IT assets, and identifies matches autonomously, freeing human operators from manual matching tasks while maintaining high accuracy through continuous learning and validation mechanisms.
Solution Approach 2:
The system replaces the manual mechanical process of matching vulnerability responses to change requests with an automated machine learning-based system. The machine learning model processes and compares data structures, descriptions, and asset mappings algorithmically, substituting human cognitive and manual work with automated computational processes that are both faster and scalable.
4Productivity
If automated matching using machine learning is implemented, then processing speed and efficiency are improved, but system complexity increases
Solution Approach 1:
The system introduces an intermediary machine learning model layer between the vulnerability response data and the change request management system. This intermediary automatically processes matching logic, analyzes descriptions, maps assets, and generates match recommendations, shielding the underlying complexity of machine learning operations from the rest of the system while providing clean, standardized outputs that integrate with existing change request workflows.
Data Source
AI summary
A machine learning model is trained based at least on previous change requests, wherein each of the previous change requests are associated with a controlled management of a lifecycle of a change to an information technology environment. A security vulnerability of the information technology environment is identified. Using the trained machine learning model, a corresponding match score for each of a plurality of pending change requests is determined for the security vulnerability. An indication of whether a resolution specification for the security vulnerability is to be linked with one of the plurality of pending change requests selected based on a factor associated with its corresponding match score is received.