This article provides a complete, practical walkthrough of the Postgres Webhook Automation 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:  
   Proactive Suspicious Login Detection Using n8n: A Deep Dive into Intelligent Security Workflows
   
   Meta Description:  
   Discover how to build an intelligent suspicious login detection system in n8n using IP geolocation, GreyNoise threat intelligence, and user agent analysis. Learn how to proactively monitor user logins, detect anomalies, and notify users of potential cyber threats.
   
   Keywords:  
   n8n workflow, suspicious login detection, cybersecurity automation, GreyNoise, IP geolocation, threat intelligence, user agent detection, Gmail alert, Slack notification, login anomaly detection, IP-API, UserParser, security automation, Postgres query, browser anomaly, device fingerprint
   
   Article:
   
   In the age of rising cybersecurity threats, identifying and mitigating suspicious login attempts has become a cornerstone of digital safety. Leveraging low-code tools like n8n, organizations can automate this task efficiently. The “Suspicious Login Detection” workflow in n8n is a comprehensive, intelligent, and highly adaptable solution for flagging potential security breaches in real-time.
   
   This article provides an in-depth look at a carefully crafted workflow that uses event-driven data to assess login risk, correlate it with historical behavior, and promptly inform both system administrators and users. Built by Milorad and tagged under “SecOps,” this automation exemplifies the future of responsive cybersecurity.
   
   A Multi-Layered Threat Detection Strategy
   
   At its core, this n8n workflow listens for new user login events using a manual trigger or an HTTP webhook. Upon receiving a login event, it extracts essential data such as:
   
   - IP address
   - User ID
   - Timestamp
   - User agent string
   - Login page URL
   
   With that information, it branches out into multiple analysis paths to determine whether the login attempt is legitimate or suspicious.
   
   Threat Intelligence from GreyNoise
   
   Using the GreyNoise Community API, the workflow analyzes the IP for known scanning behavior or noise on the internet. Depending on the classification (malicious, benign, unknown) and trust level, the workflow assigns one of three priorities:
   
   - 🔴 High: Unknown IPs or those with malicious intent
   - 🟡 Medium: Suspicious but not confirmed
   - 🟢 Low: IPs confirmed to be part of legitimate services (RIOT true)
   
   Alerts containing IP information, user ID, timestamp, and GreyNoise threat viz links are sent to Slack for immediate review by the SecOps team.
   
   Geolocation Analysis using IP-API
   
   Another leg of the analysis involves querying IP-API to fetch geolocation data. By comparing the current login’s city and country with that of the user's last 10 login attempts (retrieved through a Postgres query), the workflow determines whether this login is from a new location.
   
   A mismatch triggers the “New Location” node, prompting further investigation, and may ultimately result in emailing the user about a potentially suspicious login attempt.
   
   Device and Browser Fingerprinting with UserParser
   
   The third analytical angle is device and browser identification. Using the UserParser API, the workflow decodes the user agent string to extract details like:
   
   - Browser name
   - Operating system
   - Device type
   
   This data is compared against the user’s historical login metadata. If a new device, browser, or OS is detected, it flags the event for increased scrutiny.
   
   Comprehensive Threat Context via Data Synthesis
   
   All collected insights — from GreyNoise, IP-API, and UserParser — are funneled through a “Complete login info” merge node. This enriched context gives the downstream logic a unified basis for determining suspicious activity based on geolocation and device anomalies, even if each one individually wasn't sufficient to raise a red flag.
   
   User Notifications via Email
   
   When a login anomaly is confirmed (new location or new device), the workflow checks whether the user has a valid email on file. If so, it crafts a well-formatted, informative HTML email containing:
   
   - Username
   - Timestamp
   - Location details
   - Device and OS fingerprint
   
   This message aims to alert the user of an unfamiliar login and advises them to reset their password if the activity looks unfamiliar.
   
   Designed for Efficiency and Scale
   
   From start to finish, the workflow covers the following competencies:
   
   - Real-time event monitoring via webhooks
   - IP reputation analysis with GreyNoise
   - Geolocation checks using IP-API
   - User agent parsing for fingerprinting
   - Historical context from login databases
   - Slack alerts for internal teams
   - Email notifications to users
   
   Every component is modular — meaning organizations can enhance, disable, or add more nodes depending on their specific security requirements or preferred APIs.
   
   Third-Party APIs Used
   
   The following third-party endpoints are seamlessly integrated into the workflow:
   
   1. GreyNoise Community API  
      Purpose: Identify whether an IP is known to participate in widespread scanning or malicious behavior.  
      API Documentation: https://docs.greynoise.io/docs/community-api
   
   2. IP-API  
      Purpose: Geolocate IP addresses to determine the city and country of login attempts.  
      API Documentation: https://ip-api.com/docs
   
   3. UserParser  
      Purpose: Decode user agent strings into meaningful device, OS, and browser information.  
      API Documentation: https://www.userparser.com/docs/user-agent-and-geoip-lookup-api-v1.1/
   
   4. Gmail (via n8n Gmail OAuth2 credentials)  
      Purpose: Send email alerts to users about suspicious login activity.
   
   5. Slack API  
      Purpose: Send internal notifications to a predetermined Slack channel for SecOps monitoring.
   
   6. PostgreSQL  
      Purpose: Retrieve the user’s historical login data and profile details for behavioral comparison.
   
   Conclusion
   
   The n8n “Suspicious Login Detection” workflow empowers teams to automate threat detection without sacrificing depth of analysis. Through the integration of trusted third-party intelligence sources and internal user behavior history, it proactively flags and communicates potential breaches before damage occurs.
   
   Ideal for startups to enterprises, this modular and scalable solution demonstrates the powerful utility that no-code/low-code automation can bring to cybersecurity, turning reactive workflows into proactive digital watchdogs.
   
   Whether your aim is to protect user accounts, comply with security regulations, or respond more quickly to potential threats, this workflow is an excellent launchpad for elevating your security automation game.

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.