Quick Start Guide

Get up and running with GoMask.ai in just a few minutes! This guide will walk you through creating your first data masking routine.

Prerequisites

Before you begin, make sure you have:

• ✅ A GoMask.ai account (sign up here)
• ✅ Access to a database you want to mask
• ✅ Basic understanding of your data structure

Step 1: Access the Dashboard

1. Log in to your GoMask.ai account at datafactory.gomask.ai
2. You'll see the main dashboard with an overview of your routines and recent activity

Step 2: Create Your First Connector

Connectors tell GoMask.ai how to access your data sources.

1. Navigate to Connectors in the sidebar
2. Click "Add New Connector"
3. Choose your database type (PostgreSQL, MySQL, SQL Server, Oracle)
4. Fill in the connection details:

{
  "name": "Production Database",
  "type": "postgresql",
  "host": "your-db-host.com",
  "port": 5432,
  "database": "your_database",
  "username": "your_username",
  "password": "your_password"
}

5. Click "Test Connection" to verify connectivity
6. Save your connector

:::tip Use read-only credentials when possible to ensure maximum security. :::

Step 3: Create a Masking Routine

Now let's create your first masking routine:

1. Go to Routines in the sidebar
2. Click "Create New Routine"
3. Choose "Masking Routine" as the type

Basic Configuration

Fill in the routine details:

• Name: "Customer Data Masking"
• Description: "Mask customer PII for development environment"
• Source Connector: Select the connector you just created
• Destination: Choose where masked data should go

Table Selection

1. GoMask.ai will automatically scan your database structure
2. Select the tables you want to include in the masking routine
3. For this example, let's select a customers table

AI-Powered Column Analysis

GoMask.ai will automatically analyse your columns and suggest masking rules:

Review and Customise Rules

You can customise any of the suggested rules:

1. Click on any column to modify its masking rule
2. Choose from various masking techniques:
   • Synthetic Generation: Create realistic fake data
   • Tokenisation: Replace with consistent tokens
   • Encryption: Reversible encryption
   • Shuffling: Randomise within the dataset
   • Custom Functions: Use your own masking logic

Example: Customising Email Masking

// Custom email masking function
function maskEmail(originalEmail) {
  const [username, domain] = originalEmail.split('@');
  const maskedUsername = generateRandomString(username.length);
  return `${maskedUsername}@example.com`;
}

Step 4: Run Your First Masking Job

1. Review your routine configuration

2. Click "Save & Execute"

3. Choose execution options:

   • Full Scan: Process all data
   • Sample Run: Process a subset for testing
   • Scheduled: Set up recurring execution

4. Monitor the progress in real-time:

🟡 Initialising routine...
🟢 Connected to source database
🟡 Analyzing data structure...
🟢 Applied masking rules to 15,847 records
🟡 Writing to destination...
🟢 Masking complete! Processed 15,847 records in 2m 34s

Step 5: Validate Your Results

After the routine completes:

1. Review the execution summary:

   • Records processed: 15,847
   • Success rate: 100%
   • Execution time: 2m 34s
   • Data integrity checks: ✅ Passed

2. Sample the masked data:

   -- Original data
   SELECT first_name, last_name, email FROM customers LIMIT 5;
   
   -- Masked data
   first_name | last_name | email
   -----------|-----------|------------------
   Sarah      | Johnson   | [email protected]
   Michael    | Chen      | [email protected]
   

3. Verify data relationships:

   • Foreign key constraints maintained
   • Data types preserved
   • Statistical distributions similar

Step 6: Set Up Monitoring (Optional)

Enable monitoring to track your routine's performance:

1. Go to Settings → Notifications

2. Configure alerts for:

   • Execution failures
   • Performance degradation
   • Data quality issues

3. Set up webhooks for integration with your monitoring tools

Next Steps

Congratulations! You've successfully created and executed your first masking routine. Here's what to explore next:

🔧 Advanced Features

• Custom Masking Functions
• Routine Versioning
• Performance Optimisation

👥 Team Collaboration

• Team Management
• Role-Based Access Control

🔗 Integrations

• CI/CD Integration
• Webhook Configuration

📊 Compliance

• GDPR Compliance
• Audit Logging

Common Next Actions

Automate Your Routine

Set up your routine to run automatically:

# Using the GoMask.ai CLI
gomask routine schedule \
  --routine-id "your-routine-id" \
  --cron "0 2 * * *" \
  --timezone "UTC"

Create Additional Routines

• Development Environment: Daily refresh with latest data
• Testing Environment: Weekly full refresh
• Analytics Environment: Monthly aggregated data

Integrate with Your Workflow

Add GoMask.ai to your existing data pipeline:

# GitHub Actions example
- name: Refresh Test Data
  uses: gomask-ai/github-action@v1
  with:
    routine-id: ${{ secrets.ROUTINE_ID }}
    api-key: ${{ secrets.GOMASK_API_KEY }}

Troubleshooting

Having issues? Check these common solutions:

For more help, visit our troubleshooting guide or contact [email protected].

🎉 You're now ready to protect your sensitive data with GoMask.ai!

Questions? Check out our video tutorials or contact us at [email protected].