Is this skill suitable for endpoint or email security?

No, this skill is specifically designed for cloud-based data storage, databases, and data pipelines. For endpoint or email DLP, specialized agents like Microsoft Purview or Google Workspace DLP should be used.

Does it handle automated de-identification?

Yes, the skill includes workflows and code samples for masking, tokenizing, and redacting sensitive data using cloud-native service APIs.

Can I integrate these DLP controls into my CI/CD pipeline?

Absolutely. This skill includes specific logic for creating DLP scanning gates that can block pipeline progression if sensitive data is detected in output files.

Which regulatory frameworks does this skill help satisfy?

The skill is mapped to NIST CSF 2.0 and AI RMF frameworks, making it ideal for organizations needing to comply with GDPR, HIPAA, and PCI DSS.

What cloud providers are supported by this skill?

This skill provides implementation patterns for Amazon Web Services (AWS) using Macie, Google Cloud Platform (GCP) using the DLP API, and Microsoft Azure using Microsoft Purview.

Cloud Data Loss Prevention (DLP)

Name: Cloud Data Loss Prevention (DLP)
Author: mukul975

bymukul975

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4,121

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보안 및 테스팅

Implements automated data discovery, classification, and protection across multi-cloud environments to safeguard sensitive information.

This skill enables developers and security engineers to automate Data Loss Prevention (DLP) across major cloud platforms including AWS, Azure, and GCP. It provides a standardized approach to discovering sensitive information like PII and PHI, applying automated classification labels, and implementing de-identification techniques such as masking and tokenization. By integrating DLP controls directly into data pipelines and storage configurations, it ensures continuous compliance with frameworks like GDPR and HIPAA while preventing data leakage in production environments.

주요 기능

01Cross-platform support for S3, BigQuery, and Azure Storage accounts

02Automated sensitivity labeling and policy enforcement for cloud storage

03Configurable de-identification through masking, tokenization, and redaction

04Integration of DLP gates into CI/CD and data processing pipelines

054,121 GitHub stars

06Automated sensitive data discovery using AWS Macie, Google Cloud DLP, and Azure Purview

사용 사례

01Auditing cloud environments for unprotected PII, PHI, or financial data

02Sanitizing data sets before moving them from production to analytics environments

03Meeting regulatory compliance requirements for GDPR, HIPAA, and PCI DSS

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add mukul975/anthropic-cybersecurity-skills implementing-cloud-dlp-for-data-protection

For use in Claude.ai and ChatGPT

주요 기능

01Cross-platform support for S3, BigQuery, and Azure Storage accounts

02Automated sensitivity labeling and policy enforcement for cloud storage

03Configurable de-identification through masking, tokenization, and redaction

04Integration of DLP gates into CI/CD and data processing pipelines

054,121 GitHub stars

06Automated sensitive data discovery using AWS Macie, Google Cloud DLP, and Azure Purview

사용 사례

01Auditing cloud environments for unprotected PII, PHI, or financial data

02Sanitizing data sets before moving them from production to analytics environments

03Meeting regulatory compliance requirements for GDPR, HIPAA, and PCI DSS

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add mukul975/anthropic-cybersecurity-skills implementing-cloud-dlp-for-data-protection

For use in Claude.ai and ChatGPT