AI Vulnerability Routing for Faster Enterprise Remediation
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Solution Overview
Problem
Existing methods struggle to efficiently identify and mitigate vulnerabilities in enterprise systems, requiring manual intervention and leading to delays in remediation, which can compromise security and resources.
Innovation Solution
A system utilizing artificial intelligence and machine learning to automatically detect vulnerabilities, classify their categories, and notify appropriate remediation personnel, eliminating the need for intermediaries and reducing the time to remediation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If traditional manual methods are used to identify and classify vulnerabilities, then the system can process vulnerabilities, but the time to remediation increases and security is compromised
Solution Approach 1:
The system performs self-service by automatically detecting vulnerabilities, classifying them into categories, and routing them to appropriate remediation personnel without requiring manual intervention from security analysts. The machine learning model autonomously processes vulnerability data and makes classification decisions, eliminating the need for human intermediaries in the initial vulnerability processing stage.
Solution Approach 2:
The patent replaces the mechanical manual process of vulnerability identification and classification with an automated machine learning-based system. The neural network model substitutes for human analysts by processing vulnerability data, determining classifications, and generating remediation routes automatically, thereby reducing processing time and improving security response.
2Productivity
If an intermediary is used to determine remediation personnel, then the process can be managed, but the time between vulnerability identification and remediation increases
Solution Approach 1:
The system extracts and eliminates the intermediary role from the vulnerability management process. Instead of using a manual intermediary to determine remediation personnel, the machine learning model directly processes vulnerability data and outputs classification results and remediation routes, removing the unnecessary intermediate step that caused delays.
Solution Approach 2:
The system performs preliminary action by pre-classifying vulnerabilities into categories and pre-determining remediation routes before they are assigned to personnel. The machine learning model processes vulnerability data in advance and generates ready-to-execute remediation plans, eliminating the need for subsequent manual review and routing decisions.
3Loss of time
If automated machine learning is used to detect and classify vulnerabilities, then the time to remediation decreases, but the system complexity increases
Solution Approach 1:
The machine learning model serves multiple functions within a single system: it detects vulnerabilities, classifies them into categories, determines remediation routes, and generates notifications. This multi-functionality consolidates what would otherwise require multiple separate systems and manual processes into one integrated automated platform, managing complexity through functional consolidation rather than multiplication.
4Reliability
If manual vulnerability management is used, then the system is simpler to implement, but security and resource management are compromised
Solution Approach 1:
The system incorporates feedback mechanisms where the machine learning model continuously processes vulnerability data, compares it against known patterns, and adjusts its classification and routing decisions. The system learns from historical vulnerability data and improves its accuracy over time, providing adaptive security management that maintains high reliability while managing complexity through intelligent automation.
Data Source
AI summary
Disclosed are systems and methods for detecting a vulnerability across programs of an enterprise system and notifying remediation agent. The systems and methods utilize artificial intelligence (“AI”) systems to process data received from a particular network, such as systems, software, and software configuration data. The AI systems processes the software and software configuration data and compares the data to known vulnerabilities stored to a database. The system maps the vulnerabilities to attack signatures. When a vulnerability is identified within the network, the AI systems run classification analysis and categorization analysis to determine the probability a vulnerability is a known vulnerability and the category of software it relates to. The AI systems then runs a remediation agent analysis to determine the proper remediation agent to mitigate the vulnerability. Once a remediation agent is determined, a remediation agent is notified of the vulnerability and mitigates the vulnerability by removing or patching.