This article provides a complete, practical walkthrough of the Splitout Code Automation 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:
   Automating PII Removal from CSV Files with n8n, Google Drive, and OpenAI
   
   Meta Description:
   Discover how to automate the detection and removal of personally identifiable information (PII) from CSV files stored in Google Drive—using n8n, OpenAI, and cloud-based workflow automation.
   
   Keywords:
   n8n, workflow automation, PII removal, OpenAI GPT-4, Google Drive API, data privacy, CSV file sanitization, No-code automation, data compliance, AI data analysis
   
   Third-Party APIs Used:
   
   1. Google Drive API
   2. OpenAI API
   
   Article:
   
   In today’s digital landscape, ensuring that personally identifiable information (PII) is protected is not just good practice—it’s often a legal necessity. As organizations handle increasing volumes of data, automating privacy-preserving procedures becomes essential. One prime use case is the sanitization of CSV files that may unknowingly carry PII. In this article, we walk through an efficient n8n workflow that leverages Google Drive and OpenAI to automatically detect and remove PII from CSV files—all without writing a single line of manual code.
   
   Overview: What This Workflow Does
   
   This n8n (pronounced “n-eight-n”) workflow automates the entire life cycle of a CSV file posted to a Google Drive folder:
   
   - Monitors a Google Drive folder for new CSV file uploads
   - Downloads the file and extracts its content
   - Uses OpenAI’s GPT model to detect columns containing PII
   - Removes those columns programmatically
   - Uploads the sanitized version of the CSV to a separate Google Drive folder
   
   It’s an elegant solution that combines artificial intelligence, cloud storage, and low-code automation to help ensure data privacy compliance.
   
   Step-By-Step Breakdown
   
   Let’s walk through each component of the workflow and examine how it contributes to the automation pipeline.
   
   1. Google Drive Trigger: Keeping an Eye on New Uploads
   
   The workflow begins with a Google Drive Trigger node. This node is configured to monitor a specific folder, checking every minute for newly created files. As soon as a new file is detected, it passes the file ID and metadata on to the next steps for processing.
   
   2. File Metadata Extraction and Download
   
   Using two nodes in tandem—“Get filename” and “Google Drive”—the workflow extracts the file name and downloads the actual file content using its file ID. This prepares the data for examination and modification.
   
   3. Extract File Content
   
   Next, the "Extract from File" node reads the uploaded CSV file. This step turns the raw file content into structured JSON format that can be easily parsed and analyzed by later nodes.
   
   4. PII Detection with OpenAI
   
   The extracted data is sent to OpenAI’s GPT-4 model via the “OpenAI” node. A system prompt instructs the AI to analyze the tabular data and return only the column names that contain PII (e.g., names, emails, phone numbers). The AI’s response is concise and formatted for seamless downstream use.
   
   5. Results Parsing and Merging
   
   The "Get result", "Get filename", and "Extract from File" nodes feed into a "Merge" node, consolidating the PII columns, file metadata, and the original data into a common data structure for transformation.
   
   6. Remove PII Columns: Automated Data Sanitization
   
   At the heart of the privacy automation is a custom Code node called “Remove PII columns.” Here’s what it does:
   
   - Parses the AI’s response to extract the list of PII columns
   - Iterates through each row of data
   - Removes any key-value pairs where the key matches one of the PII columns
   - Converts the sanitized data back into CSV format
   - Generates a new filename with a suffix (_PII_removed) to distinguish the cleaned file
   
   7. Upload Sanitized File to Drive
   
   Finally, the cleaned CSV file is uploaded using another Google Drive node to a separate folder named "processed"—helping to maintain a clean audit trail and prevent accidental distribution of sensitive information.
   
   Why This Workflow Matters
   
   This n8n workflow saves considerable manual effort in identifying and cleaning potentially sensitive files. Here’s why you should consider adopting or adapting it:
   
   - Ensures Compliance: Helps enforce policies under GDPR, HIPAA, and similar data protection frameworks.
   - Reduces Manual Work: Automatically processes data without human intervention.
   - Leverages AI Smartly: Uses OpenAI’s language model to analyze data with accuracy and nuance.
   - Keeps Data Secure: By uploading sanitized versions only to a final destination folder, the original file remains untouched elsewhere.
   
   Prerequisites
   
   To run this workflow, you’ll need:
   
   - Access to an n8n instance (self-hosted or cloud)
   - Authorized connection to a Google Drive API account
   - Valid OpenAI API credentials
   
   Conclusion
   
   This PII-removal workflow showcases the true power of combining low-code platforms with cutting-edge AI. By using n8n to orchestrate Google Drive and OpenAI, organizations can ensure sensitive data is cleaned, fast and reliably. Whether you’re a data privacy officer, an automation enthusiast, or a compliance engineer, this solution can be a game changer for handling CSV files safely and responsibly.
   
   With a growing focus on responsible data practices, automation like this isn't just helpful—it’s becoming indispensable.
   
   Ready to cut down on manual sanitization and scale your privacy workflows? Plug into n8n.

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.