Data Classification: Compliance, Concepts, and 4 Best Practices

What is Data Classification?

The term data classification refers to processes and tools designed to organize data into categories. The purpose is to make data easier to store, manage, and secure.

Data classification systems support organizations in many efforts, including risk management, compliance, and legal discovery. Additionally, data classification systems can improve the usability and accessibility of data, helping organizations derive more value from their information assets.

Data classification can improve all three fundamental aspects of information security:

• Confidentiality—enabling and application of stronger security measures for sensitive data.
• Integrity—enabling adequate storage provisioning and access controls to prevent data loss, unauthorized modification or corruption.
• Availability—providing controls to make data easily accessible by authorized users.

Why Is Data Classification Important?

Data classification provides an interface for organizations to implement controls and procedures across data formats, structures and storage technologies. Classified data allows an organization to define and implement a single policy for handling sensitive data across multiple systems and data objects.

There are several reasons why data classification is important:

• Context: adds business context to applications and processes. For example, based on data classification, an organization can identify applications that handle sensitive data and define stricter security requirements.
• Compliance: makes it easier to comply, and also proves compliance, with regulatory frameworks such as GDPR, CCPA, HIPAA, and PCI.
• Security: makes the business aware of the data sensitivity, allowing the business to apply the right level of security control.
• Governance: makes it easier to map, track, and control data.

What Are the Four Data Classification Levels?

There are typically four data classification levels in information security:

• Public: data that can be openly shared with anyone.
• Internal: company-wide data that should not be shared externally.
• Confidential: sensitive information shared with specific people or teams.
• Restricted: highly sensitive information available on a need-to-know basis.

What Are the Different Types of Classification of Data?

While data is classified based on each individual business’s needs, there are common types of data classification:

• Data-based classification: describes the nature of the data (e.g., credit card numbers).
• Context-based classification: describes the business context of the data (e.g., sensitive data).
• Source-based classification: describes the source of the data (e.g., customer data).

Challenges of Data Classification

Organizations face several challenges when classifying data:

• False positives: the same data can appear in different formats leading to incorrect classifications.
• False negatives: data may be considered sensitive in a specific context but not in another, leading to incorrect classifications.
• Big data: dynamic data repositories create challenges for classification tools.
• Cost: implementing a data classification policy can be costly depending on the amount of data and controls established.

How Do Compliance Standards Impact Data Classification?

Many regulations require organizations to perform data classification, with various requirements:

• GDPR: requires classification of personal data.
• PCI DSS: requires entities to classify data to determine its sensitivity.
• SOC 2: requires maintenance of confidential information.
• HIPAA: mandates classification procedures for personal health information (PHI).

Data Classification Levels

Data sensitivity levels help determine how classified data should be handled. Common classifications include:

1. Low Sensitivity Data—public information with no access restrictions.
2. Medium Sensitivity Data—intended for internal use that can impact the organization if breached.
3. High Sensitivity Data—regulated data requiring strict controls.

Establishing a Data Classification Policy

A data classification policy should address:

• Objectives—motivation for implementing data classification with measurable KPIs.
• Workflows—define the classification process organizationally.
• Location—identify data storage areas.
• Schema—determine data classification categories.
• Data owners—define roles and responsibilities for data classification.
• Compliance—establish compliance measures.

4 Data Classification Best Practices

1. Conduct a Data Risk Assessment: understand all data requirements and classify accordingly.
2. Create a Data Inventory: locate sensitive data and identify it for protection.
3. Establish Data Security Controls: implement security measures based on data classification levels.
4. Maintenance and Monitoring: ensure ongoing monitoring of dynamic data and adapt classification as needed.

Data Classification With Satori

Satori offers a continuous data discovery and classification approach. This method enhances data protection and compliance by managing data classification more effectively.