Grammar-Powered RAG for Multi-Domain Security Queries
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Solution Overview
Problem
Existing security tools struggle to handle complex queries that span multiple domains in cloud security posture management and cloud native application protection, as they rely on precomputed caches and customized user interfaces that are time-consuming, error-prone, and unscalable, especially when dealing with dynamic queries involving multiple domains and predicates.
Innovation Solution
Implementing AI-powered macros that utilize a planner, executor, and aggregator to process natural language queries across domains, converting them into a structured query language (RQL) using a Large Language Model (LLM) and Information Retrieval (IR) techniques, enabling efficient and robust handling of multi-domain queries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If precomputed caches and customized user interfaces are used to handle security queries, then query processing can be performed, but the system becomes time-consuming, error-prone, and unscalable when dealing with dynamic multi-domain queries
Solution Approach 1:
The patent introduces an AI-powered macro system as an intermediary layer between the user and the security query infrastructure. This macro system translates natural language queries into structured domain-specific language (RQL) queries, enabling dynamic multi-domain queries without requiring precomputed caches or customized interfaces for each query type. The macro acts as a mediator that bridges the gap between user intent and system execution, improving both efficiency and adaptability.
Solution Approach 2:
The system dynamically changes query parameters and structure based on the specific multi-domain requirements of each query. Instead of using fixed precomputed caches, the AI macro system generates queries with adaptive parameters that match the specific domains and predicates involved in each query, allowing the system to handle diverse query types efficiently without sacrificing versatility.
2Productivity
If AI-powered macros with planner, executor, and aggregator are implemented to process natural language queries, then query processing becomes more efficient and robust, but system complexity increases
Solution Approach 1:
The AI-powered macro system is segmented into three distinct functional components: a planner that generates query strategies, an executor that translates and runs queries, and an aggregator that consolidates results. This segmentation allows each component to specialize in its specific task, improving overall efficiency while making the complexity manageable through clear separation of concerns. Each module can be independently optimized and maintained.
Solution Approach 2:
Despite the segmentation into multiple components, the system maintains universality through a common interface layer that handles natural language input and standardized output formatting. The AI macro system serves multiple functions (planning, execution, aggregation) within a unified architecture, reducing the perceived complexity for users while maintaining high productivity internally.
3Measurement precision
If natural language queries are translated into structured query language using LLM and IR techniques, then query accuracy and completeness improve, but processing time and computational resources increase
Solution Approach 1:
The system performs preliminary actions by pre-processing and storing domain-specific knowledge, vocabularies, and query patterns in structured formats. The AI macro system has access to precompiled domain ontologies and query templates that enable faster translation of natural language to structured queries. This preliminary preparation reduces the computational burden during actual query processing, maintaining high accuracy while reducing processing time.
Data Source
AI summary
Techniques for grammar powered retrieval augmented generation for domain specific languages are disclosed. In some embodiments, a system, a process, and/or a computer program product for grammar powered retrieval augmented generation for domain specific languages includes automatically generating a seed dataset for a domain specific language (DSL) (e.g., a resource query language (RQL), and wherein the RQL is generated for RQL for multi-domain security applications); expanding the seed dataset for the DSL using a Large Language Model (LLM); and validating the seed dataset for the DSL, wherein the seed dataset for the DSL is input to the LLM for fine tune training of the LLM (e.g., fine-tuned for a cloud security application).