Complete AI safety suite: test 9 guardrail layers with Groq LLM

Who's It For

AI developers, automation engineers, and teams building chatbots, AI agents, or workflows that process user input. Perfect for those concerned about security, compliance, and content safety.

What It Does

This workflow demonstrates all 9 guardrail types available in n8n's Guardrails node through real-world test cases. It provides a comprehensive safety testing suite that validates:

• Keyword blocking for profanity and banned terms
• Jailbreak detection to prevent prompt injection attacks
• NSFW content filtering for inappropriate material
• PII detection and sanitization for emails, phone numbers, and credit cards
• Secret key detection to catch leaked API keys and tokens
• Topical alignment to keep conversations on-topic
• URL whitelisting to block malicious domains
• Credential URL blocking to prevent URLs with embedded passwords
• Custom regex patterns for organization-specific rules (employee IDs, order numbers)
• Each test case flows through its corresponding guardrail node, with results formatted into clear pass/fail reports showing violations and sanitized text.

How to Set Up

• Add your Groq API credentials (free tier works fine)
• Import the workflow
• Click "Test workflow" to run all 9 cases
• Review the formatted results to understand each guardrail's behavior

Requirements

• n8n version 1.119.1 or later (for Guardrails node)
• Groq API account (free tier sufficient)
• Self-hosted instance (some guardrails use LLM-based detection)

How to Customize

• Modify test cases in the "Test Cases Data" node to match your specific scenarios
• Adjust threshold values (0.0-1.0) for AI-based guardrails to fine-tune sensitivity
• Add or remove guardrails based on your security requirements
• Integrate individual guardrail nodes into your production workflows
• Use the sticky notes as reference documentation for implementation

This is a plug-and-play educational template that serves as both a testing suite and implementation reference for building production-ready AI safety layers.