New patent application published for: Intelligent Alert Remediation Using Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG)

Intelligent Alert Remediation Using Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG)

Claims

1. A method for providing intelligent alert remediation recommendation, the method comprising steps of: receiving a security alert from an External Attack Surface Management (EASM) platform; extracting metadata and contextual information from the security alert and converting the extracted metadata and contextual information into a vector embedding; performing a similarity search over a knowledge base comprising structured and unstructured data using the vector embedding to retrieve relevant remediation data; and generating, via a Large Language Model (LLM), remediation recommendations based on a context-aware prompt constructed from the retrieved data and the alert information.
2. The method of claim 1, wherein the steps include presenting the remediation recommendations through an interactive conversational interface.
3. The method of claim 2, wherein the steps include receiving user input via the interactive conversational interface and providing follow-up guidance based thereon.
4. The method of claim 1, wherein the steps include integrating the remediation recommendations with workflows and features of the EASM platform for implementation.
5. The method of claim 1, wherein the knowledge base is continually updated with threat intelligence and historical remediation data.
6. The method of claim 1, further comprising updating the knowledge base with newly generated recommendations and associated outcomes and incorporating updates into future processes to enhance recommendation precision.
7. The method of claim 1, further comprising incorporating user feedback into the knowledge base to improve future recommendations.
8. The method of claim 1, wherein remediation recommendations are dynamically adapted to an organization's risk posture and emerging cybersecurity threats.
9. The method of claim 1, further comprising enabling a user to request automation scripts or detailed instructions for implementing the remediation within the EASM platform.
10. The method of claim 1, wherein the steps include aggregating structured data and unstructured data retrieved via Retrieval-Augmented Generation (RAG) into a unified dataset for input to the LLM.
11. A non-transitory computer-readable storage medium having computer-readable code stored thereon for programming one or more processors to perform steps of: receiving a security alert from an External Attack Surface Management (EASM) platform; extracting metadata and contextual information from the security alert and converting the extracted metadata and contextual information into a vector embedding; performing a similarity search over a knowledge base comprising structured and unstructured data using the vector embedding to retrieve relevant remediation data; and generating, via a Large Language Model (LLM), remediation recommendations based on a context-aware prompt constructed from the retrieved data and the alert information.
12. The non-transitory computer-readable storage medium of claim 11, wherein the steps include presenting the remediation recommendations through an interactive conversational interface.
13. The non-transitory computer-readable storage medium of claim 12, wherein the steps include receiving user input via the interactive conversational interface and providing follow-up guidance based thereon.
14. The non-transitory computer-readable storage medium of claim 11, wherein the steps include integrating the remediation recommendations with workflows and features of the EASM platform for implementation.
15. The non-transitory computer-readable storage medium of claim 11, wherein the knowledge base is continually updated with threat intelligence and historical remediation data.
16. The non-transitory computer-readable storage medium of claim 11, further comprising updating the knowledge base with newly generated recommendations and associated outcomes and incorporating updates into future processes to enhance recommendation precision.
17. The non-transitory computer-readable storage medium of claim 11, further comprising incorporating user feedback into the knowledge base to improve future recommendations.
18. The non-transitory computer-readable storage medium of claim 11, wherein remediation recommendations are dynamically adapted to an organization's risk posture and emerging cybersecurity threats.
19. The non-transitory computer-readable storage medium of claim 11, further comprising enabling a user to request automation scripts or detailed instructions for implementing the remediation within the EASM platform.
20. The non-transitory computer-readable storage medium of claim 11, wherein the steps include aggregating structured data and unstructured data retrieved via Retrieval-Augmented Generation (RAG) into a unified dataset for input to the LLM.