Context-Based Insight System for Cloud Security Alert Correlation
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Solution Overview
Problem
Current cloud computing systems face inefficiencies, inaccuracies, and inflexibility in analyzing and reporting security incidents due to rigid methodologies that struggle to accommodate changing security alert combinations.
Innovation Solution
A context-based insight system utilizing a security alert generative language model to generate accurate, flexible, and efficient security incident reports by correlating security alerts and providing contextual insights and remediation actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If rigid methodologies are used to analyze security alerts, then system stability is maintained, but adaptability to changing security alert combinations deteriorates
Solution Approach 1:
The patent transforms the analysis approach by changing from rigid predefined rules to dynamic parameter-based analysis using LLMs. The system adjusts its analysis parameters based on the specific combination of security alerts, enabling adaptability without increasing operational complexity.
Solution Approach 2:
The patent replaces traditional mechanical rule-based security analysis systems with an AI/LLM-based system. This substitution eliminates the need for rigid methodologies while maintaining systematic analysis through automated intelligent processing.
2Loss of information
If existing security incident reports are generated, then basic security information is provided, but report quality and usefulness deteriorates
Solution Approach 1:
The patent introduces LLMs as intermediary systems between security alert data and final incident reports. These intermediaries process raw alert data, add contextual information, and generate high-quality reports, thereby improving information quality without sacrificing efficiency.
Solution Approach 2:
The system performs preliminary contextual analysis and alert correlation before generating final reports. This preliminary action enriches the report content with relevant context and insights, improving quality while maintaining efficient automated processing.
3Measurement precision
If comprehensive security analysis is performed, then analysis accuracy is improved, but processing time increases
Solution Approach 1:
The LLM-based system performs self-service analysis by automatically correlating alerts, identifying patterns, and generating reports without requiring manual intervention. This self-service approach maintains high accuracy while reducing processing time through automated intelligent processing.
Solution Approach 2:
The system maintains continuous analysis and reporting operations without interruption. The LLMs continuously process security alerts and generate updated reports in real-time, ensuring accurate analysis while minimizing time loss through uninterrupted automated processing.
Data Source
AI summary
The disclosure focuses on using a context-based insight system to determine security incident reports that include security incident insights and remediation actions based on various combinations of security alerts in cloud computing systems. The context-based insight system uses a security alert generative language model (GLM) to generate security incident reports based on correlated security alerts within a security incident and the attack-type contexts of those security alerts. By using the security alert GLM guided by attack-type contexts to generate security incident reports, the context-based insight system provides understandable text narratives that provide clear and accurate insights into security incidents including remediation actions to address the security incidents as a whole rather than just reporting individual security alerts of the security incident. Further, the context-based insight system dynamically updates the security incident report as additional related security alerts are detected and received.