expanso-remove-pii

Remove personally identifiable information from streaming data using Expanso mapping transformations.

1|Updated Feb 1, 2026
One-click install
npx skills add https://github.com/expanso-io/expanso-skills --skill expanso-remove-pii
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: expanso-remove-pii
Source: https://github.com/expanso-io/expanso-skills/tree/main/skills/recipes/remove-pii
Command: npx skills add https://github.com/expanso-io/expanso-skills --skill expanso-remove-pii

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) components.

What problem does it solve?

This Skill automates the removal of sensitive personally identifiable information (PII) from data streams, ensuring compliance with regulations like GDPR and PCI-DSS.

Core Features & Use Cases

  • PII Redaction: Removes or transforms sensitive data like credit card numbers, IP addresses, and email addresses.
  • Compliance Focused: Implements a multi-step process to meet strict data privacy standards.
  • Use Case: Process customer interaction logs in real-time, removing PII before sending them to an analytics platform to maintain user privacy.

Quick Start

Run the remove PII pipeline with sample data using the provided run script.

Frequently Asked Questions about expanso-remove-pii

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I remove personally identifiable information from streaming data pipelines?

Removing PII from streaming data involves a multi-step pipeline that hashes IP addresses, pseudonymizes user names, and generalizes location data to ensure sensitive information is redacted or transformed before output.

What is the best way to automate GDPR and PCI-DSS compliance for data streams?

Automating GDPR and PCI-DSS compliance for data streams requires a processing pipeline that identifies and transforms sensitive fields like credit card numbers and email addresses, ensuring strict data privacy standards are met during transit.

How does data pseudonymization work for IP addresses and user names?

Data pseudonymization for IP addresses and user names works by applying mapping transformations within a data pipeline, replacing or hashing the original sensitive values with generalized or pseudonymized data outputs.

Can I output redacted data to HTTP endpoints or files?

Yes, you can output redacted data to HTTP endpoints or files. The pipeline utilizes mapping transformations for data processing and supports various destinations to securely route the transformed data.

What are the limitations of using automated PII redaction in data pipelines?

Automated PII redaction in data pipelines focuses strictly on hashing IPs, pseudonymizing names, and generalizing locations. It may not cover all edge cases of unstructured sensitive data without prior identification or mapping transformations configured.