This article provides a complete, practical walkthrough of the Code Microsoftoutlook Create Webhook 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:  
   Automated Phishing Detection and Reporting Workflow Using n8n, ChatGPT, and Jira
   
   Meta Description:  
   Discover how to build a powerful security workflow in n8n that detects phishing emails from Gmail and Outlook, generates visual screenshots, analyzes them using ChatGPT, and creates detailed Jira tickets—fully automated and customizable.
   
   Keywords:  
   n8n, phishing detection, email security automation, Gmail, Outlook, hcti.io, ChatGPT, OpenAI, Jira, AI phishing analysis, email screenshot, automated ticketing
   
   Third-Party APIs Used:
   
   - Gmail API (via n8n Gmail Trigger node)
   - Microsoft Graph API (via Outlook Trigger and HTTP request nodes)
   - hcti.io (HTML/CSS to Image Screenshot API)
   - OpenAI API (ChatGPT for phishing analysis)
   - Atlassian Jira Cloud API (Ticket creation and file attachments)
   
   Article:
   
   ---
   
   # Automating Phishing Detection and Reporting with n8n, ChatGPT, and Jira
   
   Email remains a major vector for cybersecurity threats, making phishing detection a critical necessity for any organization. However, manually monitoring and analyzing email threats is labor-intensive and prone to delays. That’s where n8n—a powerful, open-source workflow automation tool—comes in.
   
   In this article, we showcase an advanced n8n workflow that automates the end-to-end process of phishing email detection and reporting. This solution integrates your email service (Gmail or Outlook), visualizes suspicious content, analyzes it using ChatGPT, and automatically logs relevant data in Jira for security teams to act upon. Let’s break down how each part works.
   
   ---
   
   ## 1. Real-Time Email Monitoring
   
   The workflow supports email monitoring from both Gmail and Microsoft Outlook.
   
   - The active Gmail Trigger node monitors new emails every minute.
   - A disabled Outlook Trigger node exists for those who wish to extend the workflow to Microsoft 365 environments.
   - When new messages arrive, key fields such as subject, recipient, body content (HTML/text), and headers are extracted through Set Nodes.
   
   This early step ensures that emails are captured efficiently and that all relevant metadata is ready for processing.
   
   ---
   
   ## 2. Extracting and Formatting Headers
   
   For Outlook emails, additional steps fetch extended metadata:
   
   - The Retrieve Headers of Email node makes an authenticated request to Microsoft Graph API to pull message headers and plain text.
   - The Format Headers node uses JavaScript to convert raw headers into a structured JSON object.
   - These headers play a critical role in phishing detection, as they can reveal spoofing or suspicious routing paths.
   
   This preprocessing primes the content for deeper visual and AI analysis.
   
   ---
   
   ## 3. Visualizing Email Content
   
   To understand and evaluate email design and authenticity, the workflow turns HTML emails into images:
   
   - The HTML content of the email is organized via the Set Email Variables node.
   - With Screenshot HTML, the HTML is sent to the hcti.io API, which converts it into an image.
   - Retrieve Screenshot then collects the generated URL and fetches the binary content.
   
   This visual snapshot helps AI better interpret the format while serving as a useful reference in Jira tickets.
   
   Note: Email data is exposed to a third-party API (hcti.io) here. In environments concerned with privacy, a self-hosted rasterizer is recommended.
   
   ---
   
   ## 4. AI-Powered Phishing Analysis
   
   Now that the email’s layout and metadata are collected and visualized, the workflow leverages OpenAI’s ChatGPT:
   
   - The ChatGPT Analysis node sends the screenshot and structured headers to GPT-4 for analysis.
   - Prompted to determine phishing intent, ChatGPT reviews the components of the email and returns a formatted, human-readable report.
   - The response leverages Jira’s wiki-style syntax, optimizing clarity and consistency for ticketing systems.
   
   With AI reviewing both visual and metadata components, the analysis achieves a depth rarely possible with simple heuristics.
   
   ---
   
   ## 5. Jira Integration for Threat Reporting
   
   Threat intelligence is only valuable when acted upon. To close the loop:
   
   - Create Jira Ticket compiles all data—subject, body, recipient, and AI analysis—into a new Jira task.
   - Rename Screenshot adjusts the filename for better ticket readability.
   - Upload Screenshot of Email to Jira attaches the visual HTML screenshot into the Jira issue.
   
   Together, these steps ensure that phishing attempts are fully documented for cybersecurity teams, complete with both context and visual evidence.
   
   ---
   
   ## Conclusion
   
   This end-to-end automation workflow represents a powerful and scalable approach to email threat detection. By combining:
   
   - Real-time Gmail and Outlook monitoring
   - Intelligent formatting and visualization
   - AI-based phishing analysis
   - Seamless integration into Jira
   
   …it enables organizations to detect and log phishing attempts without human intervention.
   
   Security staff benefit from standardized, thorough, and prompt alerts that can significantly reduce response time and improve organizational defense.
   
   Whether you're an IT administrator, a security analyst, or an automation enthusiast, this n8n flow shows what’s possible when modular tools and machine learning come together.
   
   Ready to try it? Deploy this workflow in n8n and plug in your credentials for Gmail, Outlook, OpenAI, Jira, and hcti.io—and you're set. Phishing threats won’t stand a chance.
   
   ---
   
   By automating phishing detection, you're not just saving time—you're safeguarding your entire organization.

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.