This article provides a complete, practical walkthrough of the Splitout Zendesk Send Triggered n8n agent. It connects HTTP Request, Webhook across approximately 1 node(s). Expect a Intermediate setup in 15-45 minutes. One‑time purchase: €29.

What This Agent Does

This agent orchestrates a reliable automation between HTTP Request, Webhook, handling triggers, data enrichment, and delivery with guardrails for errors and rate limits.

It streamlines multi‑step processes that would otherwise require manual exports, spreadsheet cleanup, and repeated API requests. By centralizing logic in n8n, it reduces context switching, lowers error rates, and ensures consistent results across teams.

Typical outcomes include faster lead handoffs, automated notifications, accurate data synchronization, and better visibility via execution logs and optional Slack/Email alerts.

How It Works

The workflow uses standard n8n building blocks like Webhook or Schedule triggers, HTTP Request for API calls, and control nodes (IF, Merge, Set) to validate inputs, branch on conditions, and format outputs. Retries and timeouts improve resilience, while credentials keep secrets safe.

Third‑Party Integrations

• HTTP Request
• Webhook

Import and Use in n8n

1. Open n8n and create a new workflow or collection.
2. Choose Import from File or Paste JSON.
3. Paste the JSON below, then click Import.
4. Show n8n JSON

   Title:  
   Supercharging Cybersecurity Ticketing with AI and MITRE ATT&CK in n8n
   
   Meta Description:  
   Discover how an advanced n8n workflow integrates AI, the MITRE ATT&CK framework, and external APIs to enrich cybersecurity alerts, automate ticket analysis, and enhance threat response via OpenAI and Qdrant vector stores.
   
   Keywords:  
   n8n, MITRE ATT&CK, cybersecurity automation, Qdrant, SIEM alerts, OpenAI GPT-4o, embeddings, vector store, Zendesk integration, LangChain, incident response, threat intelligence
   
   Third-Party APIs Used:
   
   1. OpenAI API (GPT-4o, Embeddings)
   2. Qdrant (Vector Database)
   3. Google Drive API (Data Source for MITRE JSON)
   4. Zendesk API (Ticketing System Integration)
   
   Article:
   
   Integrating AI with MITRE ATT&CK to Enhance Cybersecurity Workflows in n8n
   
   As cybersecurity threats grow in scale and complexity, incident response teams are constantly searching for smarter, faster ways to triage alerts. This is where intelligent automation, powered by large language models and interoperable platforms like n8n, comes into play. This article delves into a powerful no-code/low-code n8n workflow that augments cybersecurity alerts with real-time AI analysis and MITRE ATT&CK framework intelligence — seamlessly transforming raw incident data into structured insights and automated ticket updates.
   
   📦 Overview: What Does the Workflow Do?
   
   This workflow takes a focused approach to interpreting, enriching, and acting on SIEM-generated alerts. It integrates OpenAI's GPT-4-powered agents, vector embeddings, the MITRE ATT&CK knowledge base, and Zendesk ticketing to create a dynamic, responsive threat investigation system. Here’s what it accomplishes:
   
   - Collects alerts from a chat interface or Zendesk support tickets.
   - Uses LangChain-integrated AI agents to process alert text and extract relevant MITRE TTPs.
   - Embeds and stores MITRE techniques in a Qdrant vector database.
   - Queries the vector store in real-time to enrich incoming alerts.
   - Generates detailed remediation steps and external references.
   - Automatically updates Zendesk tickets with insights.
   
   🧠 How AI and MITRE Work Together
   
   At the core of the workflow’s reasoning capability is GPT-4o running via OpenAI’s API. The AI agent is primed with a specialized system message, framing it as a cybersecurity expert trained in the MITRE ATT&CK framework and enterprise incident response practices. This ensures consistent extraction of:
   
   - Tactics and techniques with labels (e.g., Execution - PowerShell - T1059.001)
   - Actionable remediation tasks for network, endpoint, and patch management
   - Historical patterns associated with similar activity
   - Links to MITRE ATT&CK and other vetted reference content
   
   💡 Vector Store Power: Qdrant + Embeddings
   
   To supercharge contextual understanding, MITRE ATT&CK techniques and metadata are transformed into vector embeddings using OpenAI’s “text-embedding-3-large” model. These embeddings are then loaded into a Qdrant collection via n8n’s vector store integration, making them queryable for similarity-based matches.
   
   For any alert, the workflow queries Qdrant to find the closest MITRE entries by semantic similarity, leveraging pre-trained embeddings. That means an incident described in varied wording — such as “strange traffic on port 443” — can still resolve to the correct TTP like “T1001.003: Protocol or Service Impersonation”.
   
   🔄 Ticket Automation with Zendesk
   
   Once an enriched summary is generated, the workflow pushes the findings directly into Zendesk:
   
   - It loops over all relevant tickets.
   - For each, the AI analyzes the alert and returns a formatted HTML summary.
   - The workflow updates the ticket, adding:
      - An internal note with the threat context.
      - Custom fields tagged with tactic and technique.
      - A pointer to remediation and external documentation.
   
   This provides SOC analysts with dozens of minutes saved per alert — who now receive automatic intelligence to act quickly.
   
   📂 Loading MITRE Data from Google Drive
   
   To load the source data, the workflow begins by downloading a curated MITRE ATT&CK JSON file from Google Drive. It then splits out the entries, enhances each with metadata, and processes it with OpenAI embeddings before storing it in the Qdrant vector collection named “mitre”.
   
   🕹️ Conversational Interface: Chat-Driven Threat Intel
   
   An optional chat component can trigger this logic interactively. Analysts can simply drop in an alert message via a chat interface integrated with n8n. The AI Agent responds with enriched detail — translating the unstructured input into structured TTPs and helping guide investigation steps.
   
   🔥 Why This Workflow Matters
   
   This fusion of AI, vector search, and automation marks a significant step forward for security operations. Rather than requiring analysts to manually compare alerts with MITRE pages, look for context clues, and draft response actions — the system does that automatically, and intelligently.
   
   This becomes extremely useful in environments with:
   
   - High alert volume and limited SOC staff
   - Limited in-house understanding of MITRE TTPs
   - A need to connect different tools (SIEM, knowledge base, ticketing)
   
   🌐 APIs and Tools in Action
   
   To achieve this intelligent automation, the workflow uses the following third-party services:
   
   - 🧠 OpenAI API – for language model processing (GPT-4o) and embeddings
   - 💾 Qdrant – to store and retrieve contextual knowledge on MITRE techniques
   - ☁️ Google Drive API – to host and retrieve MITRE ATT&CK data as usable JSON
   - 📩 Zendesk API – for reading and updating cybersecurity tickets seamlessly
   
   🎯 Final Thoughts
   
   In an era where delay means damage, timely threat attribution and guided remediation are everything. This n8n workflow is a powerful example of how low-code automation, when paired with cutting-edge AI and cybersecurity intelligence like MITRE ATT&CK, can transform how security teams operate.
   
   The combination of contextual chat, vector analysis, and live integration with ticketing systems empowers teams to move from reactive to proactive — closing the loop between alert, knowledge, and action.
   
   Security meets simplicity — that’s the power of intelligence, embedded.
   
   — Written by your friendly AI assistant 👾

5. Set credentials for each API node (keys, OAuth) in Credentials.
6. Run a test via Execute Workflow. Inspect Run Data, then adjust parameters.
7. Enable the workflow to run on schedule, webhook, or triggers as configured.

Tips: keep secrets in credentials, add retries and timeouts on HTTP nodes, implement error notifications, and paginate large API fetches.

Validation: use IF/Code nodes to sanitize inputs and guard against empty payloads.

Why Automate This with AI Agents

AI‑assisted automations offload repetitive, error‑prone tasks to a predictable workflow. Instead of manual copy‑paste and ad‑hoc scripts, your team gets a governed pipeline with versioned state, auditability, and observable runs.

n8n’s node graph makes data flow transparent while AI‑powered enrichment (classification, extraction, summarization) boosts throughput and consistency. Teams reclaim time, reduce operational costs, and standardize best practices without sacrificing flexibility.

Compared to one‑off integrations, an AI agent is easier to extend: swap APIs, add filters, or bolt on notifications without rewriting everything. You get reliability, control, and a faster path from idea to production.

Best Practices

• Credentials: restrict scopes and rotate tokens regularly.
• Resilience: configure retries, timeouts, and backoff for API nodes.
• Data Quality: validate inputs; normalize fields early to reduce downstream branching.
• Performance: batch records and paginate for large datasets.
• Observability: add failure alerts (Email/Slack) and persistent logs for auditing.
• Security: avoid sensitive data in logs; use environment variables and n8n credentials.

FAQs

Can I swap integrations later? Yes. Replace or add nodes and re‑map fields without rebuilding the whole flow.

How do I monitor failures? Use Execution logs and add notifications on the Error Trigger path.

Does it scale? Use queues, batching, and sub‑workflows to split responsibilities and control load.

Is my data safe? Keep secrets in Credentials, restrict token scopes, and review access logs.