Generative AI Incident Response System for Security Context
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Cybersecurity incident response systems face challenges in providing context and clear remediation actions to operators, often relying on machine-readable data that lacks human-friendly context and root cause analysis.
Innovation Solution
A system utilizing generative artificial intelligence (AI) to process natural language inputs, associate them with incident response actions, and generate queries executable on a security database, thereby initiating mitigation actions based on the results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If machine-readable data structures are used for cybersecurity incident response, then data storage and retrieval efficiency is improved, but human-friendly context and usability deteriorate
Solution Approach 1:
The patent introduces natural language processing as an intermediary layer between machine-readable security data and human operators. The system translates structured security alerts into natural language explanations that convey context, root causes, and remediation recommendations, allowing operators to understand incidents without learning specialized query languages while maintaining efficient machine data processing
2Measurement precision
If detailed security data is provided to operators, then measurement precision is improved, but information overload and difficulty in identifying root cause worsen
Solution Approach 1:
The patent segments security incident information into distinct components: detected threats, root causes, contextual information, and remediation actions. The natural language processing system organizes these segmented elements into a coherent narrative structure, allowing operators to process information systematically rather than being overwhelmed by raw data volume
Solution Approach 2:
Natural language processing acts as an intermediary that filters and synthesizes detailed security data into meaningful insights. The system extracts relevant information from structured data, translates it into natural language, and presents it in a simplified format that maintains analytical precision while reducing presentation complexity
3Ease of operation
If natural language processing is used to translate security queries, then ease of operation is improved, but loss of context and reliance on statistics worsen
Solution Approach 1:
The patent implements feedback mechanisms where the natural language processing system continuously learns from operator interactions with security incidents. The system analyzes which contextual information operators find most useful and adjusts its translations accordingly, preserving relevant context while maintaining natural language simplicity. This feedback loop prevents information loss by adapting to actual user needs
Data Source
AI summary
A system and method for providing cybersecurity incident response is presented. The method includes receiving an incident input based on an event in a computing environment; generating an input for a generative artificial intelligence (AI) based on the received incident input; utilizing the generative AI to generate an output based on the generated input; utilizing the generative AI to associate the received incident input with an incident response action of a plurality of incident response actions; generating a query based on the received incident; executing the query on a security database, the security database including a representation of the computing environment; and initiating a mitigation action based on a result of the executed query and the associated incident response action.