LangChain Automate

LangChain Automate streamlines cybersecurity incident response by automating the extraction of TTP information from SIEM data and providing actionable remediation steps. This 26-node workflow integrates with various tools, enhancing efficiency in managing alerts and correlating historical patterns, ultimately improving threat detection and response times.

7/8/2025
26 nodes
Complex
manualcomplexlangchainsplitoutextractfromfilesplitinbatchessticky notegoogle drivezendesknoopadvancedfilesstorage
Categories:
Communication & MessagingComplex WorkflowManual Triggered
Integrations:
LangChainSplitOutExtractFromFileSplitInBatchesSticky NoteGoogle DriveZendeskNoOp

Target Audience

This workflow is designed for cybersecurity professionals, incident response teams, and IT security analysts who need to efficiently analyze and respond to security alerts. It is particularly useful for those working with SIEM systems, MITRE ATT&CK framework, and incident ticketing systems like Zendesk. The workflow can also benefit organizations that are looking to integrate AI capabilities into their security operations for enhanced threat detection and remediation.

Problem Solved

This workflow addresses the challenge of efficiently processing and responding to cybersecurity alerts by automating the extraction of Tactics, Techniques, and Procedures (TTPs) from SIEM data. It provides actionable remediation steps tailored to specific alerts, cross-references historical patterns, and recommends external resources for deeper understanding. By integrating with tools like Zendesk, it helps streamline the incident response process and ensures that relevant information is documented and tracked effectively.

Workflow Steps

  • Trigger: The workflow begins with a manual trigger or when a chat message is received, initiating the processing of security alerts.
    2. Extract Data: It pulls data from a Google Drive file containing MITRE ATT&CK information, ensuring that the latest threat intelligence is available.
    3. Process Alerts: Upon receiving a SIEM alert, the workflow utilizes an AI Agent to analyze the alert, extracting TTP information and providing tailored remediation steps while cross-referencing historical data.
    4. Embed Data: Relevant data is embedded into a Qdrant vector store for efficient querying and retrieval.
    5. Zendesk Integration: The workflow retrieves all Zendesk tickets and updates them with MITRE data, ensuring that incident records are enriched with relevant threat context.
    6. Loop Processing: It processes multiple tickets in batches, applying the same analysis and updates to each ticket sequentially.
    7. Output Generation: Finally, the structured output parser formats the AI-generated responses, making them easy to read and actionable.

    • Customization Guide

    Users can customize this workflow by modifying the AI Agent's system messages to fit their specific cybersecurity context or threat landscape. They can also adjust the parameters for embedding models or change the integration settings for different ticketing systems. Additionally, users can add or remove nodes based on their operational needs, such as integrating with other data sources or response tools. Customization of the Google Drive file ID allows users to pull in different datasets as required. To adapt the workflow for different alert types, users can edit the extraction logic and output formatting to align with their reporting standards.