AI Macro Query Planning for Cross-Domain Security Search
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing security tools struggle to handle complex queries that span multiple domains due to limitations in precomputed caches and customized user interfaces, leading to inefficient and incomplete responses, especially when dealing with dynamic and multi-domain queries.
Innovation Solution
An AI-powered macro system processes natural language queries using a planner, executor, and aggregator to convert them into a structured query language (RQL) through an intermediate representation, enabling cross-domain searches across various security domains.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If precomputed caches and customized user interfaces are used to handle security queries, then response speed is improved, but the ability to handle complex multi-domain queries deteriorates
Solution Approach 1:
The patent introduces an intermediate representation layer that translates natural language queries into a structured query language (RQL). This intermediary translation layer enables the system to handle complex multi-domain queries while maintaining efficient processing, as the RQL serves as a mediating formal language that bridges natural language and the underlying data structures.
Solution Approach 2:
The patent segments the query processing into distinct components: natural language understanding, translation to intermediate representation, RQL generation, and execution. This segmentation allows each component to be optimized independently, enabling the system to maintain fast response times while handling complex queries across multiple security domains.
2Device complexity
If existing security tools are used to process queries, then device complexity is reduced, but the completeness and accuracy of security insights deteriorates
Solution Approach 1:
The patent creates a universal query processing system that can handle diverse security queries across multiple domains (cloud security, network security, endpoint security, etc.) through a single unified interface. The RQL framework provides multi-functionality, allowing the same system architecture to process various types of security queries without requiring separate specialized tools for each domain.
3Ease of operation
If natural language queries are processed directly without structured translation, then ease of operation is improved, but measurement precision and query accuracy deteriorates
Solution Approach 1:
The patent introduces an intermediate representation as a mediating layer between natural language input and structured query execution. This intermediary representation captures the semantic meaning of natural language queries while maintaining enough structure to ensure accurate translation into RQL, thereby preserving both user convenience and query precision.
Solution Approach 2:
The system incorporates feedback mechanisms where the translation process from natural language to RQL can be refined based on query results and user interactions. This feedback loop allows the system to learn from previous queries and improve the accuracy of translations, maintaining high precision while keeping the interface naturally language-based.
Data Source
AI summary
Techniques for AI-powered macros to process complex natural language processing (NLP) across domains are disclosed. In some embodiments, a system, a process, and/or a computer program product for AI-powered macros to process complex NLP across domains includes processing a natural language query; performing a cross-domain search to generate a search result using a plurality of data source domains using a resource query language (RQL) and a Large Language Model (LLM); and outputting the search result.