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US-20260127167-A1 - AI-POWERED MACROS TO PROCESS COMPLEX NLP QUERIES ACROSS DOMAINS

US20260127167A1US 20260127167 A1US20260127167 A1US 20260127167A1US-20260127167-A1

Abstract

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.

Inventors

  • Chandra Biksheswaran Mouleeswaran
  • Sathya Prakash Rajagopal
  • Gaspar Modelo-Howard
  • Alok Tongaonkar

Assignees

  • PALO ALTO NETWORKS, INC.

Dates

Publication Date
20260507
Application Date
20260105

Claims (20)

  1. 1 . A system, comprising: a processor configured to: process a natural language query; perform 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); execute a plurality of RQLs in a ranked order to obtain results; aggregate the obtained results for the search result; and output the search result; and a memory coupled to the processor and configured to provide the processor with instructions.
  2. 2 . The system of claim 1 , wherein the performing of the cross-domain search to generate the search result using the plurality of data source domains further includes to use a planner, executor, and aggregator to collect distinct results from each of the plurality of data source domains.
  3. 3 . The system of claim 1 , wherein the performing of the cross-domain search to generate the search result using the plurality of data source domains further includes to use a planner, executor, and aggregator to collect distinct results from each of the plurality of data source domains and further use an application programming interface (API).
  4. 4 . The system of claim 1 , wherein the plurality of data source domains includes a configuration data set, an Identity and Asset Management (IAM) data set, and a vulnerability data set.
  5. 5 . The system of claim 1 , wherein the RQL is generated for RQL for multi-domain security applications.
  6. 6 . The system of claim 1 , wherein the LLM is trained for performing automated entity extraction for multi-domain security applications.
  7. 7 . The system of claim 1 , wherein the processor is further configured to: generate an output graph of assets in response to the natural language query.
  8. 8 . A method, comprising: 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); executing a plurality of RQLs in a ranked order to obtain results; aggregating the obtained results for the search result; and outputting the search result.
  9. 9 . The method of claim 8 , wherein the performing of the cross-domain search to generate the search result using the plurality of data source domains further includes using a planner, executor, and aggregator to collect distinct results from each of the plurality of data source domains.
  10. 10 . The method of claim 8 , wherein the performing of the cross-domain search to generate the search result using the plurality of data source domains further includes using a planner, executor, and aggregator to collect distinct results from each of the plurality of data source domains and further using an application programming interface (API).
  11. 11 . The method of claim 8 , wherein the plurality of data source domains includes a configuration data set, an Identity and Asset Management (IAM) data set, and a vulnerability data set.
  12. 12 . The method of claim 8 , wherein the RQL is generated for RQL for multi-domain security applications.
  13. 13 . The method of claim 8 , wherein the LLM is trained for performing automated entity extraction for multi-domain security applications.
  14. 14 . The method of claim 8 , further comprising: generating an output graph of assets in response to the natural language query.
  15. 15 . A system, comprising: a processor configured to: means for processing a natural language query; perform 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); means for executing a plurality of RQLs in a ranked order to obtain results; means for aggregating the obtained results for the search result; and means for outputting the search result; and a memory coupled to the processor and configured to provide the processor with instructions.
  16. 16 . The system of claim 15 , wherein the performing of the cross-domain search to generate the search result using the plurality of data source domains further includes to use a planner, executor, and aggregator to collect distinct results from each of the plurality of data source domains.
  17. 17 . The system of claim 15 , wherein the performing of the cross-domain search to generate the search result using the plurality of data source domains further includes to use a planner, executor, and aggregator to collect distinct results from each of the plurality of data source domains and further use an application programming interface (API).
  18. 18 . The system of claim 15 , wherein the plurality of data source domains includes a configuration data set, an Identity and Asset Management (IAM) data set, and a vulnerability data set.
  19. 19 . The system of claim 15 , wherein the RQL is generated for RQL for multi-domain security applications.
  20. 20 . The system of claim 15 , wherein the processor is further configured to: generate an output graph of assets in response to the natural language query.

Description

CROSS REFERENCE TO OTHER APPLICATIONS This application is a continuation of U.S. patent application Ser. No. 18/755,386, entitled AI-POWERED MACROS TO PROCESS COMPLEX NLP QUERIES ACROSS DOMAINS filed Jun. 26, 2024 which is incorporated herein by reference for all purposes, which claims priority to U.S. Provisional Patent Application No. 63/568,851 entitled AI-POWERED MACROS TO PROCESS COMPLEX NLP QUERIES ACROSS DOMAINS filed Mar. 22, 2024, which is incorporated herein by reference for all purposes. BACKGROUND OF THE INVENTION A firewall generally protects networks from unauthorized access while permitting authorized communications to pass through the firewall. A firewall is typically a device or a set of devices, or software executed on a device, such as a computer, which provides a firewall function for network access. For example, firewalls can be integrated into operating systems of devices (e.g., computers, smart phones, or other types of network communication capable devices). Firewalls can also be integrated into or executed as software on computer servers, gateways, network/routing devices (e.g., network routers), or data appliances (e.g., security appliances or other types of special purpose devices). Firewalls typically deny or permit network transmission based on a set of rules. These sets of rules are often referred to as policies. For example, a firewall can filter inbound traffic by applying a set of rules or policies. A firewall can also filter outbound traffic by applying a set of rules or policies. Firewalls can also be capable of performing basic routing functions. BRIEF DESCRIPTION OF THE DRAWINGS Various embodiments of the invention are disclosed in the following detailed description and the accompanying drawings. FIG. 1 illustrates an overview of an architecture for AI-powered macros to process complex natural language processing (NLP) across domains in accordance with some embodiments. FIG. 2 illustrates multi-domain query examples in accordance with some embodiments. FIG. 3 illustrates a processing view for a multi-domain search architecture for AI-powered macros to process complex NLP across domains in accordance with some embodiments. FIG. 4 illustrates an example entity extraction in accordance with some embodiments. FIGS. 5A-D illustrate preliminary testing results of the experiment performed in this first case study in accordance with some embodiments. FIG. 6 illustrates an architecture and problem-solving diagram for generating an RQL in accordance with some embodiments. FIG. 7 is a flow diagram for AI-powered macros to process complex natural language processing (NLP) across domains in accordance with some embodiments. FIG. 8 is another flow diagram for AI-powered macros to process complex NLP across domains in accordance with some embodiments. DETAILED DESCRIPTION The invention can be implemented in numerous ways, including as a process; an apparatus; a system; a composition of matter; a computer program product embodied on a computer readable storage medium; and/or a processor, such as a processor configured to execute instructions stored on and/or provided by a memory coupled to the processor. In this specification, these implementations, or any other form that the invention may take, may be referred to as techniques. In general, the order of the steps of disclosed processes may be altered within the scope of the invention. Unless stated otherwise, a component such as a processor or a memory described as being configured to perform a task may be implemented as a general component that is temporarily configured to perform the task at a given time or a specific component that is manufactured to perform the task. As used herein, the term ‘processor’ refers to one or more devices, circuits, and/or processing cores configured to process data, such as computer program instructions. A detailed description of one or more embodiments of the invention is provided below along with accompanying figures that illustrate the principles of the invention. The invention is described in connection with such embodiments, but the invention is not limited to any embodiment. The scope of the invention is limited only by the claims and the invention encompasses numerous alternatives, modifications, and equivalents. Numerous specific details are set forth in the following description in order to provide a thorough understanding of the invention. These details are provided for the purpose of example and the invention may be practiced according to the claims without some or all these specific details. For the purpose of clarity, technical material that is known in the technical fields related to the invention has not been described in detail so that the invention is not unnecessarily obscured. TECHNICAL CHALLENGES FOR PROCESSING QUERIES ACROSS DOMAINS Generally, a powerful feature of Artificial Intelligence (AI)/machine learning (ML) (e.g., generally also referred to herein as AI) is the ability to han