Automated Vulnerability Mitigation via NLP Analysis
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Solution Overview
Problem
Current security measures are inadequate in protecting computer resources from vulnerabilities until patches are released, leaving systems exposed to malicious attacks for 30-60 days after a vulnerability is detected.
Innovation Solution
A method using Natural Language Processing (NLP) and machine learning to analyze Common Vulnerability Exposure (CVE) data, identify affected resources, and automatically enforce intermediate mitigation measures, such as reducing functionality, to protect systems until a permanent patch is applied.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If current security measures wait for patches to be released, then system functionality is maintained, but systems remain exposed to malicious attacks for 30-60 days
Solution Approach 1:
The system performs preliminary actions by automatically detecting vulnerabilities through NLP analysis of CVE data and applying intermediate mitigation measures before permanent patches are released. This proactive approach reduces the exposure window from 30-60 days to a minimal timeframe by pre-establishing protective rules that limit vulnerability exploitation while maintaining system functionality.
Solution Approach 2:
The patent introduces an intermediary solution between vulnerability detection and patch deployment. The intermediary mitigation measures include automated rule generation that selectively limits vulnerable resource functionality rather than complete shutdown, serving as a temporary protective layer that bridges the gap until permanent patches are available.
2Reliability
If manual vulnerability mitigation is performed, then security protection is provided, but significant time and labor are required
Solution Approach 1:
The system enables self-service automation where the vulnerability mitigation process operates autonomously without manual intervention. The system automatically ingests CVE data, performs NLP analysis to extract vulnerability details, generates protective rules, identifies affected resources, and applies mitigation measures automatically. This eliminates the need for manual security teams to analyze and respond to each vulnerability, dramatically improving mitigation speed and productivity.
Solution Approach 2:
The patent replaces manual mechanical processes with automated computational systems. Natural Language Processing algorithms substitute for manual vulnerability analysis, automated rule generation replaces manual security policy creation, and systematic resource identification replaces manual scanning. This mechanical-to-automated substitution transforms a labor-intensive process into an efficient automated system that can respond to vulnerabilities in real-time.
3Reliability
If functionality of vulnerable resources is reduced for mitigation, then security is improved, but system capability is temporarily limited
Solution Approach 1:
The system applies local quality by selectively limiting functionality only of the specific vulnerable resources identified through NLP analysis and resource matching, rather than shutting down entire systems. The mitigation rules are precisely targeted to affect only the affected components, maintaining the functionality of unrelated system parts and minimizing operational impact while providing security protection.
Solution Approach 2:
The mitigation approach is dynamic and temporary rather than static and permanent. The system dynamically adjusts resource functionality based on vulnerability presence, automatically restoring full functionality when patches are deployed. This dynamic approach allows the system to adapt its operational state to security requirements without permanent functional limitations, balancing security needs with operational capabilities.
Data Source
AI summary
A method provides an intermediate mitigation of a vulnerability in a particular computer system. One or more processors receive a description of a vulnerability of a computer system to a malicious attack. The processor(s) perform an NLP analysis of the description of the vulnerability in order to extract risk information related to the vulnerability, where the risk information includes an identity of a type of vulnerable computer system resource in the computer system. The processor(s) match the vulnerable computer system resource to a computer system resource in a particular computer system, and perform an intermediate mitigation action that reduces a functionality of the computer system resource in the particular computer system until a solution is implemented that both restores the functionality of the computer system resource in the particular computer system and mitigates the vulnerability of the particular computer system to the malicious attack.