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data-classification

Design data-classification schemes mapping sensitivity levels to security controls.

1|Updated Jun 20, 2026
Search Tags:#security#classification#pii#access-control#sensitivity#data-governance
One-click install
npx skills add https://github.com/shafibabar/SDLC-Artifact-Factory --skill data-classification-shafibabar
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: data-classification
Source: https://github.com/shafibabar/SDLC-Artifact-Factory/tree/main/skills/data-classification
Command: npx skills add https://github.com/shafibabar/SDLC-Artifact-Factory --skill data-classification-shafibabar

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This skill solves the ambiguity of data protection by establishing a canonical sensitivity taxonomy and mechanical control mapping, ensuring that every dataset is protected according to its actual risk profile.

Core Features & Use Cases

  • Sensitivity Taxonomy: Implements a fixed four-level hierarchy (Public, Internal, Confidential, Restricted) to eliminate taxonomy drift.
  • Inherit-Max Propagation: Automatically calculates the sensitivity of derived datasets based on the highest sensitivity of their inputs.
  • Control Mapping: Provides a direct contract for security architects to implement access, encryption, retention, and masking controls based on the assigned level.

Quick Start

Use the data-classification skill to design a sensitivity scheme for the new customer-onboarding dataset by applying the inherit-max propagation rule.

Frequently Asked Questions about data-classification

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

FAQPage Schema
How do I map data sensitivity levels to security controls?▼

Data classification for DAMA-DMBOK governance requires establishing a fixed sensitivity hierarchy and mechanical control mapping to ensure consistent protection and compliance across all datasets and pipelines.

How do I calculate the sensitivity of derived datasets?▼

To calculate the sensitivity of derived datasets, apply the inherit-max propagation rule, which automatically assigns the highest sensitivity level from any input data to the newly generated dataset or graph projection.

What is the best way to implement Secure-by-Design access control?▼

Implementing Secure-by-Design access control requires a direct contract that maps sensitivity levels to concrete security controls, ensuring every dataset is protected according to its actual risk profile during the design phase.

How do I prevent sensitivity taxonomy drift in data governance?▼

Preventing sensitivity taxonomy drift requires implementing a fixed four-level hierarchy—Public, Internal, Confidential, Restricted—to eliminate ambiguity and establish a canonical classification scheme for all data assets.

Can I automate PII detection during the dataset design phase?▼

Automating PII detection during the dataset design phase is supported by applying a standardized data-classification scheme that maps identified sensitive information to concrete security controls for compliance.

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