Vulnerability Remediation via ML Correlation
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Solution Overview
Problem
Current systems lack a standardized approach for identifying and remediating operational vulnerabilities in devices, leading to reactive measures and potential outages, as customers often remain unaware of vulnerabilities until an incident occurs.
Innovation Solution
A system and method for identifying potential operational vulnerabilities by correlating operational vulnerability announcements from vendors with configuration items in a private network, and performing remediation actions such as generating problem records or applying workarounds, utilizing a machine learning engine to optimize solutions and predict outage probabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If customers manually monitor vendor announcements for operational vulnerabilities, then they can identify vulnerabilities, but they remain unaware until an outage occurs or vendor support contacts them
Solution Approach 1:
The system performs preliminary actions by proactively collecting operational vulnerability announcements from multiple vendors, processing them through machine learning models, and correlating them with customer device inventories before outages occur. This advance preparation enables the system to identify and alert customers about vulnerabilities affecting their devices prior to any operational impact, eliminating the reactive waiting period.
Solution Approach 2:
The patent introduces an intermediary vulnerability management system that acts as a mediator between vendors and customers. The system collects announcements from vendors, processes them through machine learning models, correlates them with customer device configurations, and delivers tailored alerts to customers. This intermediary layer transforms unstructured vendor announcements into actionable intelligence without requiring customers to manually monitor multiple vendor sources.
2Reliability
If a standardized framework for vulnerability notification is implemented, then customers receive timely alerts, but vendors must disclose vulnerabilities without public announcement
Solution Approach 1:
The system segments the vulnerability management process into distinct functional modules: announcement collection from vendors, machine learning-based processing and classification, device inventory correlation, alert generation, and remediation tracking. Each module handles a specific aspect of the workflow, making the overall complex framework manageable and maintainable while providing comprehensive vulnerability coverage.
Solution Approach 2:
The patent creates a universal vulnerability management platform that serves multiple functions: it collects announcements from various vendors, processes different types of vulnerability data through machine learning models, correlates with diverse device inventories, generates customized alerts, and tracks remediation status. This multi-functional system handles the complexity internally while presenting a simplified interface to both vendors and customers.
3Measurement precision
If machine learning models are used to process vulnerability announcements, then solution accuracy improves, but processing complexity increases
Solution Approach 1:
The machine learning models are trained in advance on historical vulnerability data, vendor announcements, and device configuration patterns. This preliminary training phase enables the models to automatically classify new vulnerability announcements, extract relevant parameters, and correlate them with device inventories without requiring complex real-time processing, thereby achieving high accuracy while managing system complexity.
Data Source
AI summary
Utilities for addressing operational vulnerabilities in devices of a private IT environment are described to improve uptime and overall operational maturity. Operational vulnerability announcements from various hardware and software vendors are structured and correlated with configuration items to determine whether a device in a private network is subject to an operational vulnerability announcement. When a correlation is determined, one or more remediation actions may be performed, such as generating a problem record, generating an incident record, generating a knowledgebase article, generating an event, generating a task, generating an alert, generating a work item, upgrading or downgrading software of an affected device, and/or applying a workaround, among other examples. A machine learning engine may be used to identify optimized solutions and provide probabilities of an outage occurring with and/or without a recommended solution.