Data Classification Framework: Categorizing Data for Governance

A data classification framework is a systematic approach to categorizing data based on its sensitivity, regulatory requirements, and value to the organization. Classification enables appropriate security controls, access management, and handling procedures - ensuring that sensitive data receives stronger protection while less sensitive data remains accessible for legitimate use.

Without classification, organizations either over-protect everything (limiting data utility) or under-protect sensitive data (creating compliance and security risks). A well-designed framework balances protection with accessibility.

Classification Dimensions

Sensitivity Classification

The most common classification dimension - how sensitive is this data if exposed?

Public: Information intended for public disclosure. No restrictions on access or sharing.

• Marketing materials
• Published financial reports
• Public-facing product information

Internal: Information for internal use only. Not harmful if exposed, but not intended for public.

• Internal procedures and guidelines
• General business communications
• Non-sensitive operational data

Confidential: Sensitive business information that could harm the organization if exposed.

• Financial projections and planning data
• Strategic initiatives and plans
• Customer lists and business intelligence

Restricted: Highly sensitive data with significant harm potential. Strictest controls required.

• Personal identifiable information (PII)
• Payment card data (PCI)
• Health information (PHI)
• Trade secrets and intellectual property

Regulatory Classification

What regulations apply to this data?

PII (Personal Identifiable Information): Data that can identify individuals - subject to GDPR, CCPA, and similar privacy regulations.

PHI (Protected Health Information): Health-related personal data - subject to HIPAA and healthcare regulations.

PCI (Payment Card Industry): Credit card and payment data - subject to PCI-DSS requirements.

Financial: Financial reporting data - subject to SOX, SEC, and financial regulations.

Export Controlled: Data subject to export restrictions based on content or origin.

Business Classification

How valuable or critical is this data to business operations?

Mission Critical: Essential for core business operations. Loss or corruption would severely impact business.

Business Important: Supports significant business processes. Loss would cause moderate disruption.

Business Operational: Used in daily operations but easily recreated or recovered.

Archive: Historical data retained for reference but not actively used.

Designing a Classification Framework

Define Classification Levels

Create clear, mutually exclusive levels:

Level 1 - Public
  Definition: Information approved for public release
  Examples: Press releases, marketing content
  Handling: No special handling required

Level 2 - Internal
  Definition: Non-sensitive business information
  Examples: Internal procedures, meeting notes
  Handling: Do not share externally without approval

Level 3 - Confidential
  Definition: Sensitive business information
  Examples: Financial data, customer information
  Handling: Need-to-know access, encrypted storage

Level 4 - Restricted
  Definition: Highly sensitive or regulated data
  Examples: PII, PHI, payment data
  Handling: Strict access control, encryption, audit logging

Create Classification Criteria

Provide clear guidance for classifiers:

Questions to Determine Sensitivity:

1. Would exposure cause harm to individuals?
2. Would exposure cause financial or competitive harm?
3. Is this data subject to regulatory requirements?
4. Are there contractual obligations for this data?

Classification Decision Tree:

Is data subject to specific regulations (PII, PHI, PCI)?
  → Yes: Restricted
  → No: Continue

Would unauthorized disclosure cause significant business harm?
  → Yes: Confidential
  → No: Continue

Is data intended only for internal use?
  → Yes: Internal
  → No: Public

Map Classifications to Controls

Each classification level should have associated security controls:

Control	Public	Internal	Confidential	Restricted
Access Control	None	Authentication	Need-to-know	Approval required
Encryption at Rest	Optional	Optional	Required	Required
Encryption in Transit	Optional	Recommended	Required	Required
Audit Logging	Optional	Basic	Detailed	Comprehensive
Data Masking	None	None	Context-dependent	Required for non-production
Retention	Standard	Standard	Per policy	Regulatory minimum

Implementing Classification

Initial Classification

Classify existing data assets:

1. Inventory data assets: Catalog databases, tables, and datasets
2. Apply classification criteria: Evaluate each asset against criteria
3. Assign classifications: Label with appropriate levels
4. Document rationale: Record why each classification was assigned
5. Review and approve: Owner approval for classifications

Ongoing Classification

Maintain classification as data changes:

New Data Sources: Classify before making data available Data Changes: Re-evaluate when data content changes significantly Periodic Review: Annual review of existing classifications Regulation Changes: Update when compliance requirements change

Automated Classification

Tools can assist classification:

Pattern Detection: Automatically identify PII patterns (SSN, email, credit card numbers) Machine Learning: Train models to suggest classifications based on content Metadata Analysis: Infer classification from data lineage and source systems

Automated classification should suggest, not decide. Human review remains important for accuracy.

Classification Challenges

Mixed Sensitivity Data

Tables often contain data at multiple sensitivity levels:

Options:

• Classify at the highest level present (conservative but restrictive)
• Implement column-level classification and controls
• Separate sensitive columns into restricted tables

Classification Drift

Classifications become outdated:

• Data becomes more sensitive (new regulations, business changes)
• Data becomes less sensitive (aggregation, anonymization)
• Original classification was incorrect

Regular review processes catch drift before it causes problems.

Over-Classification

Tendency to classify everything as highly sensitive:

Problems: Restricts legitimate access, increases cost, creates governance fatigue Solutions: Clear criteria, classification review, accountability for over-classification

Under-Classification

Failure to recognize sensitive data:

Problems: Compliance violations, security breaches, privacy incidents Solutions: Training, automated detection, regular audits

Classification and Analytics

Analytics Access Implications

Classification affects who can analyze what data:

• Public and Internal data typically available for broad analytics
• Confidential data requires business justification for access
• Restricted data needs specific approval and often anonymization or aggregation

Data Products and Classification

Data products inherit classification from source data:

• A dashboard using Restricted data must also be Restricted
• Aggregated or anonymized outputs may have lower classification
• Classification should flow through data lineage automatically

Metric Governance Integration

Classification supports metric governance:

• Metrics using sensitive data need appropriate access controls
• Metric definitions should reference classification of underlying data
• Self-service analytics should respect classification boundaries

A data classification framework is foundational infrastructure for governance. It enables risk-appropriate protection that keeps sensitive data safe while allowing legitimate use of less sensitive information - the balance every organization needs.