data-leak-prevention

Enforce field-level masking and redaction for sensitive data in APIs, logs, and storage.

Updated Mar 29, 2026

Search Tags:#compliance #api-security #privacy #data-masking #secure-logging #data-exposure #data-leak

Ready to run

npx skills add https://github.com/marquesfelip/agents-and-skills --skill data-leak-prevention

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: data-leak-prevention
Source: https://github.com/marquesfelip/agents-and-skills/tree/main/skills/data-leak-prevention
Command: npx skills add https://github.com/marquesfelip/agents-and-skills --skill data-leak-prevention

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Prevent sensitive data exposure across APIs, logs, and storage by enforcing field-level controls and safe data handling.

Core Features & Use Cases

Field-level data masking and redaction across API responses, logs, and storage
Automated auditing of serializers, DTOs, and data models for leakage
Compliance-oriented logging and error handling with sanitized outputs
Configurable policy for sensitive fields and caller-based exposure

Quick Start

Review an API response and enable field-level masking rules to scrub sensitive data.

Frequently Asked Questions about data-leak-prevention

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

FAQPage Schema

How do I prevent sensitive data exposure in API responses and logs?▼

Prevent sensitive data exposure by enforcing field-level data masking and redaction rules across API responses, logs, and storage. This ensures PII, secrets, and internal identifiers are scrubbed before output.

What is field-level data masking for compliance?▼

Field-level data masking is the process of filtering specific sensitive fields from API responses and logs to meet data-protection requirements. It enforces explicit response schemas and caller-based exposure controls to prevent unauthorized data leaks.

How do I audit serializers and data models for data leaks?▼

Audit serializers and data models for data leaks by applying automated checks that review data flows for PII, secrets, and internal identifiers. This process enforces explicit response schemas and field filtering to ensure safe data handling.

Does this approach support safe logging and encrypted storage for PII?▼

Yes, safe logging and encrypted storage for PII are supported through compliance-oriented logging and error handling with sanitized outputs. It enforces TLS transit and redaction to meet data-protection requirements.

Can I configure custom policies for sensitive fields and caller-based exposure?▼

Yes, you can configure policies for sensitive fields and caller-based exposure to control data visibility. This allows specific response schemas and field filtering rules to be applied dynamically based on the requester.