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kaandgnshkaandgnsh/Claude-Blue/skills/data-security/data-classification/SKILL.md

data-classification

Skill0 starsChanged 11 days agoMITView on GitHub

Design and operationalize a data classification scheme — levels and handling rules, sensitivity labels (Microsoft Purview, Google Drive labels), auto-labelling with sensitive information types, default labels and encryption — and measure adoption. Use when creating or revising classification policy, deploying labels, or deciding how a data set must be protected.

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2 rules. About 0 are common, 2 are distinctive, and 0 are very specific to this project.

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  • ≈2

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  1. Distinctive:

    Create labels and sublabels with clear names and tooltips.

    line 26
  2. Distinctive:

    When classifying a dataset, ask: legal/regulatory obligations?

    line 34

≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

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Files
SKILL.md

Data Classification

Scheme (keep it to 3–5 levels)

LevelExamplesHandling
PublicMarketing, published docsNo restrictions
InternalPolicies, most emailEmployees only; no public sharing
ConfidentialCustomer data, contracts, source code, financials pre-releaseNeed-to-know; encryption in transit/at rest; external sharing only with approval/secure channels; DLP monitoring
RestrictedCredentials/keys, PCI cardholder data, health data, M&A, crown-jewel IPStrict access lists, encryption with org-controlled keys, no external sharing, logging of all access, DLP blocking

Regulated categories (GDPR special categories, PHI, PCI, export-controlled) map to the top levels with additional legal requirements.

Implementation (Microsoft Purview example)

  1. Create labels and sublabels with clear names and tooltips; publish label policies to users with a default label (e.g., Internal) and mandatory labelling for documents/email where culture allows.
  2. Protection: encryption with permissions (Restricted), content marking (headers/watermarks), container labels for Teams/SharePoint sites (privacy, external sharing).
  3. Auto-labelling: client-side recommendations and service-side policies using SITs (credit card numbers, national IDs, credentials), exact data match for customer records, trainable classifiers.
  4. Integrate with DLP policies keyed on labels.
  5. Measure: % of files labelled per site/workload, label downgrades (with justification), auto-label simulation results.

Decision helper

When classifying a dataset, ask: legal/regulatory obligations? impact of disclosure (customers, finances, safety)? integrity criticality? who must access it? Choose the highest applicable level; document the owner and review date.

Related skills

data-discovery, dlp-alert-triage, data-retention-review, dpia

References

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