Lindy Data Handling

Overview

Best practices for secure and compliant data handling with Lindy AI.

Prerequisites

• Understanding of data privacy requirements
• Knowledge of applicable regulations (GDPR, CCPA, HIPAA)
• Access to data classification documentation

Instructions

Step 1: Data Classification

Step 2: PII Detection and Redaction

Step 3: Secure Data Pipeline

Step 4: Data Retention Management

Step 5: GDPR Compliance

For detailed implementation code and configurations, load the reference guide: Read(${CLAUDE_SKILL_DIR}/references/implementation-guide.md)

Data Handling Checklist

Output

• Data classification system
• PII detection and redaction
• Secure data pipeline
• Retention management
• GDPR compliance handlers

Error Handling

Resources

• Lindy Privacy Policy
• Lindy Security
• GDPR Guidelines

Next Steps

Proceed to lindy-enterprise-rbac for access control.

Examples

Basic usage: Apply lindy data handling to a standard project setup with default configuration options.

Advanced scenario: Customize lindy data handling for production environments with multiple constraints and team-specific requirements.