Automate Cybersecurity Analysis with n8n and MITRE ATT&CK

Opening Problem Statement

Meet Alex, a cybersecurity analyst at a mid-sized enterprise responsible for monitoring a flood of SIEM (Security Information and Event Management) alerts every day. Alex’s team spends hours manually analyzing each alert to understand the underlying attack techniques, potential impact, and remediation steps. This manual triage consumes valuable time—often leading to delayed responses, overlooked threats, and inefficient use of resources.

In real terms, Alex’s team receives hundreds of alerts daily. Each alert requires cross-referencing the MITRE ATT&CK framework to identify tactics, techniques, and procedures (TTPs), searching through historical patterns for context, and drafting specific actions for containment. This process can take up to 20 minutes per alert. Multiply that by hundreds and you get hundreds of lost hours monthly, which means higher risks and increased chances for attackers to succeed.

This is exactly where this n8n workflow comes in—automating the detection, enrichment, and contextualization of SIEM alerts with the power of AI and the comprehensive MITRE ATT&CK knowledge base.

What This Automation Does

In simple terms, when a new SIEM alert or chat message comes in, this n8n workflow automatically:

• Extracts relevant TTPs from the alert data using an AI Agent trained specifically on MITRE ATT&CK.
• Enriches the alert with actionable remediation steps tailored to the specific attack technique discovered.
• Cross-references historical alert patterns to provide contextual insights and trends.
• Retrieves relevant external cybersecurity resources for further understanding and validation.
• Updates Zendesk tickets with this enriched, structured information to streamline incident handling.
• Embeds MITRE ATT&CK data into a Qdrant vector store to enable fast similarity searches and improved alert analysis over time.

The benefits are clear—security teams cut down alert investigation times drastically, increase accuracy with AI-driven tagging, and improve response quality by having precise remediation guidance at their fingertips.

Prerequisites ⚙️

• n8n account with access to install community nodes.
• OpenAI API key configured for GPT-4o and embedding generation (used for AI analysis and vector embeddings) 🔐
• Zendesk account with API access to manage and update tickets 📧
• Google Drive access for storing and retrieving the MITRE ATT&CK JSON dataset 📁
• Qdrant vector database instance configured and accessible to n8n for embedding storage and retrieval 💾

*Optional:* For self-hosting n8n, consider using services like Hostinger for reliable infrastructure.

Step-by-Step Guide

Step 1: Set Up the Chat Message Trigger

Navigate to the n8n editor, click on “+” → select the When chat message received node (@n8n/n8n-nodes-langchain.chatTrigger). This is your trigger listening for incoming SIEM chat alerts or messages.

Configure options if needed (usually defaults suffice). This node starts the workflow on new incoming chat messages containing alert data.

Once set, you should see webhook information indicating it’s ready to receive messages.

Step 2: Configure the AI Agent for Cybersecurity Analysis

Add an AI Agent node (@n8n/n8n-nodes-langchain.agent) next in the flow. This agent is trained with a custom system message specifically to process SIEM alert data based on MITRE ATT&CK.

Paste the following system message under options for best results:

You are a cybersecurity expert trained on MITRE ATT&CK and enterprise incident response. Your job is to:
1. Extract TTP information from SIEM data.
2. Provide actionable remediation steps tailored to the alert.
3. Cross-reference historical patterns and related alerts.
4. Recommend external resources for deeper understanding.

Ensure that:
- TTPs are tagged with the tactic, technique name, and technique ID.
- Remediation steps are specific and actionable.
- Historical data includes related alerts and notable trends.
- External links are relevant to the observed behavior.

This setup instructs the AI to focus on security incident details and produce structured, actionable outputs.

Step 3: Enable OpenAI’s GPT-4o Model for Natural Language Processing

Connect an OpenAI Chat Model node (@n8n/n8n-nodes-langchain.lmChatOpenAi) using GPT-4o.

Set your OpenAI credentials under this node’s settings to authenticate API calls.

This node is the language model backend powering the AI Agent’s understanding and response generation.

Step 4: Import MITRE ATT&CK Data from Google Drive

Add a Google Drive node (n8n-nodes-base.googleDrive).

Configure it to download the MITRE ATT&CK JSON file by specifying the Google Drive file ID:

1oWBLO5AlIqbgo9mKD1hNtx92HdC6O28d

This file contains comprehensive cybersecurity framework data used later for contextual embedding and queries.

Step 5: Extract and Split MITRE JSON Data

Connect an Extract From File node (n8n-nodes-base.extractFromFile) set to parse JSON from the downloaded file.

Follow this with a Split Out node (n8n-nodes-base.splitOut) to separate the JSON array into individual MITRE entries for processing.

Step 6: Prepare Documents for Vector Embedding

Add a Default Data Loader node (@n8n/n8n-nodes-langchain.documentDefaultDataLoader) to convert MITRE data elements (description, IDs, kill chain phases) into a format suitable for embedding.

Use expression mode for JSON data:

{{ $json.description }}

Map fields like ID, name, kill chain phases, and external references in metadata settings.

Step 7: Split Text Into Manageable Tokens

Add a Token Splitter node (@n8n/n8n-nodes-langchain.textSplitterTokenSplitter) to break long descriptions into chunks or tokens optimized for embeddings.

Step 8: Generate Embeddings with OpenAI

Add one or more Embeddings OpenAI nodes (@n8n/n8n-nodes-langchain.embeddingsOpenAi) configured with the “text-embedding-3-large” model and 1536 dimensions.

These nodes convert the MITRE texts into vector embeddings for semantic search and similarity tasks.

Step 9: Insert Embeddings into Qdrant Vector Store

Use the Embed JSON in Qdrant Collection node (@n8n/n8n-nodes-langchain.vectorStoreQdrant) to insert the generated embeddings into a Qdrant collection named “mitre”.

This vector store becomes your searchable knowledge base for MITRE ATT&CK data.

Step 10: Query Qdrant to Contextualize Incoming Alerts

An AI Agent node uses the Qdrant vector store via the Query Qdrant Vector Store node to find matching TTP entries relevant to incoming SIEM alerts or chat messages.

Step 11: Parse and Format AI Agent’s Structured Output

Add a Structured Output Parser node (@n8n/n8n-nodes-langchain.outputParserStructured) to convert the AI Agent’s HTML-formatted response into structured JSON for easier consumption.

Step 12: Loop Over Zendesk Tickets for Mass Processing

Pull all tickets via the Get all Zendesk Tickets node (n8n-nodes-base.zendesk), split into batches with Loop Over Items (n8n-nodes-base.splitInBatches) to process individually.

Step 13: Run AI Agent on Each Ticket, Update with MITRE Data

For each ticket, the workflow sends the ticket subject and description to the AI Agent to extract TTP info clearly tagged with MITRE framework context.

Then, the Update Zendesk with Mitre Data node updates the ticket with:
– Internal note summarizing alert
– Custom fields for MITRE technique ID and tactic
This ensures the ticket captures actionable security context directly in Zendesk.

Step 14: Continue the Loop

The Move on to next ticket node (n8n-nodes-base.noOp) allows the loop to continue for all tickets processed.

Customizations ✏️

• Adjust AI Agent System Message: Refine the AI Agent’s instructions to adapt to different security frameworks or organizational needs by editing the systemMessage in the AI Agent node.
• Change Data Source File: Replace the Google Drive MITRE JSON file ID with another file to update or customize your MITRE data set.
• Expand Zendesk Fields: In the Update Zendesk with Mitre Data node, add additional custom fields or tags to capture more context, such as priority or related incident IDs.
• Swap OpenAI Model Versions: Use other OpenAI models for embeddings or chat, like GPT-4 or GPT-3.5, by modifying the respective nodes’ model parameters.
• Enable Advanced Memory Buffers: Modify the Window Buffer Memory node to hold longer conversation history or alert sequences for more nuanced AI context.

Troubleshooting 🔧

Problem: “OpenAI API key invalid or expired”.
Cause: Incorrect or outdated OpenAI API credentials.
Solution: Go to Credentials → OpenAI Api, re-enter or update your API key, and test connection.

Problem: “Google Drive file download fails”.
Cause: Permissions issue or wrong file ID.
Solution: Verify you have correct access to the Google Drive file. Check the file ID in the Google Drive node and ensure the OAuth2 credential has necessary scopes.

Problem: “Zendesk API update fails”.
Cause: API rate limit exceeded or incorrect Zendesk ticket ID.
Solution: Confirm ticket IDs coming from the Loop Over Items node are correct. Check Zendesk API limits. Add retry or throttling logic if needed.

Pre-Production Checklist ✅

• Verify OpenAI credentials and test basic API calls.
• Check Google Drive access and file retrieval.
• Confirm Qdrant collection “mitre” exists and nodes are properly connected.
• Test Zendesk API credentials with a sample ticket update.
• Run the workflow with sample data to validate AI Agent output and ticket updates.
• Ensure proper mapping of fields in each node for correct data flow.

Deployment Guide

Activate the workflow in n8n after testing is complete. Ensure your n8n instance can reach all external APIs (OpenAI, Zendesk, Google Drive, Qdrant).

Monitor workflow executions regularly via n8n’s execution log to catch and resolve errors promptly.

Consider setting up alerting or notifications on workflow failures to maintain incident response quality.

FAQs

Q: Can I use this workflow with other ticketing systems besides Zendesk?
A: Yes. The workflow logic can be adapted to other APIs like ServiceNow or Jira by replacing the Zendesk nodes accordingly.

Q: Does this workflow consume many OpenAI API credits?
A: It depends on alert volume. Embeddings and GPT-4o calls can add up, so monitor usage or consider batching.

Q: Is my data secure in this workflow?
A: Data is processed within your n8n instance and through configured APIs. Ensure secure credentials storage and API access policies.

Q: Can this handle high volumes of alerts?
A: Yes, n8n’s splitInBatches and asynchronous execution help process large alert volumes efficiently.

Conclusion

By following this guide, you’ve automated the time-intensive process of analyzing cybersecurity SIEM alerts, enriched with MITRE ATT&CK context and AI-powered recommendations.

You’ve saved countless hours previously spent on manual research and pasting information into tickets. Plus, your security team’s ability to respond accurately and quickly has drastically improved, reducing risk exposure.

Next steps could include integrating automated alert escalation, enriching other data sources such as threat intelligence feeds, or expanding vector store use for automated similarities detection across incidents.

Embrace this workflow to bring smart automation into your cybersecurity operations with n8n and AI — making your security more proactive and efficient.