n8n enterprise AI security firewall — guardrails for secure agents

Sandeep Patharkar | www.FastTrackAiMastery.com

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2/3/2026

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🛡️ n8n Guardrails: Risk Ranking

This workflow provides a complete testing rig for evaluating text against seven essential AI guardrails used in production systems.
It helps you detect jailbreak attempts, PII exposure, NSFW content, secret key leaks, malicious URLs, topical misalignment, and keyword violations.
Use the included Google Sheet or CSV to batch-test multiple inputs instantly.

## How It Works (Internal Workflow Overview)

1. Load Input Rows

The workflow reads each test entry (Guardrail_Type + Input_Text) from a Google Sheet or CSV.

2. Route to the Correct Guardrail

A Switch node sends the text to the appropriate guardrail:

Jailbreak
PII
Secret Keys
NSFW
URLs
Topical Alignment
Keywords

3. AI Guardrail Evaluation

Each guardrail uses Google Gemini to return:

Pass / Fail
Confidence score
Reasoning
Extracted PII, URLs, or entities (when relevant)

4. Optional Sanitization Layer

Three sanitizers demonstrate how to clean unsafe text:

PII Sanitization
Secret Key Sanitization
URL Sanitization

5. Review Results

Each guardrail node outputs clean JSON, making debugging fast and transparent.

## How to Set Up

1. Load the Test Dataset

Use either:

The included CSV file
The linked Google Sheet

Update only:

Document ID
Sheet name

2. Add Google Sheets Credentials

Create an OAuth2 credential → paste the Google JSON → connect your account.

3. Add Google Gemini Credential

Go to Credentials → Google Gemini (PaLM API) →
Paste your API key → attach it to all Guardrail nodes.

4. Review Sticky Notes

They visually explain:

What each guardrail checks
Why the check is important
Risk scoring and impact

5. Run the Workflow

Click Execute Workflow and inspect:

Each guardrail node’s output
The full execution data

## Requirements

n8n (latest version recommended)
Google Gemini API key
Google Sheets API access
Test dataset: n8n Guardrails test data.csv

## Test Data Included

The included dataset allows instant testing:

Jailbreak prompts
PII samples
API key leaks
NSFW text
Malicious URL examples
Off-topic content
Keyword triggers

## Template Metadata

Template Author: Sandeep Patharkar
Category: AI Safety / Agent Security
Difficulty: Intermediate
Estimated Setup Time: 10–15 minutes
Tags: Guardrails, AI Agents, Safety, Enterprise

## Connect With Me

Author: Sandeep Patharkar**
🔗 LinkedIn: https://www.linkedin.com/in/sandeeppatharkar
🏠 Skool AIC+: https://www.skool.com/aic-plus

n8n Enterprise AI Security Firewall & Guardrails for Secure Agents

This n8n workflow demonstrates how to implement a security firewall and guardrails for AI agents, specifically using Google Gemini Chat Model, by integrating with a Guardrails node. It allows for the filtering and sanitization of AI model inputs and outputs, ensuring secure and compliant interactions.

What it does

This workflow automates the following steps:

Manual Trigger: Initiates the workflow when manually executed.
Edit Fields (Set): Prepares a sample input message for the AI model. In a real-world scenario, this would be dynamic input from an agent or user.
Guardrails: Processes the input message through a Guardrails node. This node is designed to detect and prevent common security vulnerabilities or policy violations, such as PII (Personally Identifiable Information) leakage, secret exposure, or prompt injection attempts.
Switch: Evaluates the output of the Guardrails node. It checks if the is_valid flag, set by the Guardrails node, indicates that the input is safe to proceed.
- If Valid: If the input is deemed valid and safe, the workflow proceeds to interact with the AI model.
- If Invalid: If the input is deemed invalid or unsafe, the workflow logs the issue to Google Sheets, effectively blocking the interaction with the AI model.
Google Gemini Chat Model: (Executed only if the input is valid) Sends the sanitized input to the Google Gemini Chat Model for processing.
Guardrails (Output): (Executed only if the input was valid) Processes the response from the Google Gemini Chat Model through another Guardrails node. This step ensures that the AI model's output also adheres to security and compliance policies before being delivered to the end-user or agent.
Google Sheets (Log Invalid Input): (Executed only if the input was invalid) Records the details of the invalid input in a Google Sheet, providing a log of blocked and potentially malicious requests.

Prerequisites/Requirements

n8n Instance: A running instance of n8n.
Google Sheets Account: A Google account with access to Google Sheets for logging invalid inputs.
Google Gemini API Key: An API key for accessing the Google Gemini Chat Model.
Guardrails Node: The @n8n/n8n-nodes-langchain.guardrails node must be installed and configured in your n8n instance.
Google Gemini Chat Model Node: The @n8n/n8n-nodes-langchain.lmChatGoogleGemini node must be installed and configured in your n8n instance.

Setup/Usage

Import the workflow: Download the JSON provided and import it into your n8n instance.
Configure Credentials:
- Google Sheets: Set up a Google Sheets credential in n8n. You will need to specify the spreadsheet and sheet name where invalid inputs should be logged.
- Google Gemini Chat Model: Configure a Google Gemini API credential for the Google Gemini Chat Model node.
- Guardrails: Ensure the Guardrails node is correctly configured with your desired policies (e.g., PII detection, prompt injection rules).
Customize Input (Edit Fields): Modify the Edit Fields node to provide the actual input you want to test or process. In a production environment, this node would likely be replaced by a trigger that receives input from an AI agent or user.
Execute Workflow: Run the workflow manually to test its functionality. Observe how valid inputs proceed to the Gemini model and how invalid inputs are logged to Google Sheets.
Monitor Google Sheets: Check your configured Google Sheet for entries whenever an invalid input is detected by the Guardrails.

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