Cloud Log Security Summarization Using RAG and Event Condensing
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing general-purpose Large Language Models (LLMs) fail to effectively summarize cloud log data for security-audit purposes due to lack of specific training, superficial pattern recognition, and inability to distinguish relevant from irrelevant information, often omitting critical events.
Innovation Solution
An agent-based approach using ML models for preprocessing cloud log data, condensing critical information, and employing Retrieval Augmented Generation (RAG) to generate human-readable summaries that prioritize security insights, preserving relevant events.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If general-purpose LLMs are used to summarize cloud log data, then the system can process large volumes of data, but the summaries lack accuracy and omit critical security events
Solution Approach 1:
The patent segments the summarization process into multiple specialized stages: data ingestion, event extraction, pattern recognition, summary generation, and validation. Each stage is handled by a dedicated component optimized for its specific function, allowing accurate security event detection while processing large volumes of cloud log data without compromising precision
Solution Approach 2:
The patent introduces an intermediary validation layer that cross-references generated summaries against the original cloud log data and security guidelines. This intermediary mechanism ensures accuracy by detecting and correcting omissions or misinterpretations before final output, resolving the contradiction between processing volume and detection accuracy
2Reliability
If cloud system administrators analyze all user activity data manually, then they can identify security patterns, but the volume and fragmentation of data makes it overwhelming
Solution Approach 1:
The patent extracts and isolates only the critical security-relevant events from the vast cloud log data using pattern recognition algorithms. By taking out only the necessary information (authentication events, privilege changes, access denials) while filtering out redundant data, the system reduces data complexity while maintaining reliable security pattern identification
Solution Approach 2:
The patent merges fragmented security events into unified contextualized summaries by combining related log entries, authentication sequences, and access patterns into cohesive narratives. This merging process reduces data fragmentation and presents security insights in an integrated format that is easier to analyze
3Ease of manufacture
If existing LLMs summarize cloud data without specific training, then the system is easier to deploy, but it fails to distinguish relevant from irrelevant information
Solution Approach 1:
The patent performs preliminary action by pre-training specialized summarization models on labeled security datasets and pre-configuring pattern recognition algorithms with security guidelines. This preliminary preparation enables the system to automatically distinguish relevant from irrelevant information without requiring complex real-time training, maintaining ease of deployment while preventing information loss
Data Source
AI summary
Cloud log data and contextual information is received. Knowledge is harvested from the cloud log data and the contextual information. The knowledge that is harvested is condensed by extracting security critical information from the knowledge. A human readable summary is generated by summarizing the condensed knowledge.