Cybersecurity Threat Analysis With NLP and Graph Risk Scoring
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Solution Overview
Problem
The increasing number of cybersecurity threats poses challenges for enterprises in monitoring assets, analyzing activity, reporting vulnerabilities, and taking preventive actions, necessitating a system that can efficiently extract relevant information and implement remediation measures.
Innovation Solution
A cybersecurity asset management system that includes a user input device, display device, processor, and non-transitory memory to receive and process XML files, perform natural language processing, anomaly detection, and graph network analysis, calculating risk scores to identify and address vulnerabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If traditional cybersecurity monitoring systems are used to track enterprise assets and analyze activity, then basic security monitoring is achieved, but the systems cannot efficiently extract relevant information from the exponentially increasing cybersecurity threats
Solution Approach 1:
The patent applies information extraction techniques to isolate and identify relevant cybersecurity threat data from the overwhelming volume of incoming security events. The system extracts specific threat indicators, vulnerability information, and attack patterns from unstructured cybersecurity data sources, enabling focused analysis on critical threats rather than processing all raw data.
Solution Approach 2:
The patent replaces traditional rule-based and manual cybersecurity analysis methods with machine learning models and natural language processing systems. These intelligent systems automatically analyze cybersecurity threats, predict attack patterns, and generate remediation recommendations, substituting mechanical processing with adaptive computational intelligence that improves with experience.
2Reliability
If comprehensive cybersecurity threat data is collected and analyzed, then security coverage is improved, but the complexity of the cybersecurity system increases
Solution Approach 1:
The patent segments the cybersecurity system into distinct functional modules: data collection components, machine learning analysis engines, natural language processing systems, and automated remediation modules. Each module performs a specific function in the threat analysis pipeline, making the overall complex system manageable through functional decomposition and independent optimization of each segment.
Solution Approach 2:
The patent introduces natural language processing as an intermediary layer between raw cybersecurity data and actionable insights. The NLP system translates unstructured threat intelligence, vulnerability descriptions, and attack narratives into structured information that can be processed by analysis models, bridging the gap between diverse data sources and the remediation system.
3Productivity
If manual analysis and response to cybersecurity vulnerabilities is performed, then customization is possible, but the speed and efficiency of remediation is insufficient
Solution Approach 1:
The patent implements preliminary action by pre-training machine learning models on historical cybersecurity threat data and pre-configuring automated remediation playbooks for common vulnerability types. When new threats are detected, the system can immediately apply pre-analyzed patterns and execute pre-approved remediation actions, eliminating the need for manual analysis and response planning during active incidents.
Solution Approach 2:
The patent enables self-service cybersecurity remediation where the system automatically detects vulnerabilities, analyzes their impact, generates remediation recommendations, and executes corrective actions without human intervention. The automated system serves itself by continuously monitoring, learning from new threats, and adapting remediation strategies based on observed attack patterns and system responses.
Data Source
AI summary
A system is provided for reducing a cybersecurity vulnerability of enterprise assets with a cybersecurity asset management and automation system. In one example, a method includes a receiving a first XML file from a data aggregator; receiving a second XML file from a web crawler; enter the second XML file to a natural language processing (NLP) model trained to extract topics, phrases, and entities; perform a temporal and location analysis based on the second XML file, identify an anomaly by inputting the second XML file into an anomaly detection model; perform a graph network analysis based on the first XML file to generate a graph network, perform a risk score calculation based on ensemble scoring determining a threat remediation policy, and displaying results on a display device and implement threat remediation measures based on the threat remediation policy.