Data classification is the process of organizing data into categories based on its type, sensitivity, and importance. This helps organizations manage, protect, and use data more effectively by applying appropriate levels of security, compliance controls, and access policies.

Key Objectives:

• Protect Sensitive Data: Ensure confidential or regulated information is properly secured.

• Improve Data Management: Make it easier to locate, retrieve, and use data efficiently.

• Support Compliance: Help meet legal and industry requirements like GDPR, HIPAA, or ISO standards.

• Enhance Risk Management: Identify and reduce potential security threats by prioritizing high-risk data.

Common Classification Levels:

1. Public: Information that can be freely shared (e.g., marketing materials).

2. Internal/Private: Data meant for internal use only (e.g., company policies).

3. Confidential: Sensitive data restricted to specific roles (e.g., employee records).

4. Highly Confidential/Restricted: Critical data with strict access (e.g., trade secrets, financial records, personal health information).

Methods of Classification:

• Manual Classification: Users label data based on predefined rules.

• Automated Classification: Uses AI or rule-based engines to scan and categorize data automatically.

• Hybrid: Combines manual and automated approaches.

Benefits:

• Strengthens data security and privacy

• Reduces the risk of data breaches

• Streamlines compliance and auditing processes

• Improves data governance and policy enforcement