What Is Data Classification? Everything You Need To Know

Today’s organizations manage a mind-boggling amount of data. 

This data contains everything from private medical information to calendar invites between coworkers, and can pose a real challenge from a risk governance perspective. 

Your business needs a system for organizing both sensitive and low-priority data. Data classification can help you sort information according to risk level and set proper data security policies. Categorizing data can also help your organization streamline its data protocols. 

It may sound technical, so we’ll provide a simple breakdown of the process. Read on to learn everything you need to know about data classification.

What is data classification?

Data classification is the process of sorting data into different categories. This allows for easier management, security, and storage. 

You can choose your own criteria for categorizing data. Then you can tag the data to make it searchable and trackable. 

Data classification is useful for cybersecurity purposes, but there are other benefits. We’ll touch on why your organization should start classifying its data.

Reasons for data classification

Data classification policies should help you develop a sensible risk management strategy. They can also be useful for creating data security and data retrieval processes. 

The process of categorizing data can also improve your organizational efficiency. For example, you can find and cut duplicate data to reduce storage and backup costs.

Data classification is also needed for regulatory compliance.

Effective data categorization should help your organization:

• Organize data by importance
• Safeguard high sensitivity data
• Streamline data searches and retrieval
• Cut duplicate data
• Comply with data privacy requirements

Now that you know why data classification is worth the effort, we’ll walk you through how it’s accomplished.

How to determine data sensitivity levels

Organizing data by sensitivity levels will help you understand where to focus your risk mitigation efforts.

The levels of data sensitivity range from high to medium to low. It’s helpful to think of data sensitivity in terms of how damaging it would be if lost or stolen.

The more sensitive the data is, the more you need to focus on protecting it.

High sensitivity data 

High sensitivity data is commonly classified as restricted data. This is the data that if compromised, lost, or destroyed, would have a catastrophic impact on your organization. Organizations must place the strictest controls on high sensitivity data.

Examples of high sensitivity data include: 

• Financial records, such as credit card numbers
• Medical records, including protected health information (PHI)
• Employee records, including personally identifiable information like Social Security numbers
• Authentication data, such as login credentials

Medium sensitivity data

Medium sensitivity data is often classified as private data. It’s for internal use but would not have a catastrophic impact if compromised, lost, or destroyed.

Examples of medium sensitivity data include: 

• Internal emails or documents that don’t contain confidential data
• Supplier contracts
• IT service management or telecommunication information

Low sensitivity data

Low sensitivity data is classified as public data. It’s for public use and doesn’t require any confidentiality protections. Still, you may want controls in place to protect against damages.

Examples of low sensitivity data include: 

• Public web pages, such as job postings, blog posts, etc.
• Press releases
• Employee directory

What is data classification based on?

Data is tagged based on a number of factors. These can include security, availability, confidentiality, integrity, and privacy.

The main methods of data classification include:

• User-based classification
• Automated classification
• Content-based classification
• Context-based classification

Many organizations use some combination of automated and user-based classification. Here’s how each classification works in practice.

User-based classification  

Under user-based classification, you manually decide how to classify files. You can flag sensitive documents when they’re created, after an edit, or before a document is released.

Automated classification

Automated data classification categorizes file types by your pre-defined criteria. The two main methods of automated classification are content-based and context-based classification.

Content-based classification

Content-based classification reviews files and documents for sensitive information before classifying them. A risk category is assigned based on what’s inside each file or document.

Context-based classification

Context-based classification uses metadata instead of content to find indicators of sensitive information.

Examples of metadata include:

• The application that created the file (accounting, financial, or healthcare software)
• The user who created the document (e.g., a member of the accounting department)
• The location where a file was created (e.g., accounting department building)

Automated classification tends to be more efficient than user-based classification. But, you should still verify the results manually.

Determine which classification system is right for your organization. Then, you can plan your data classification process.

Data classification process

There are some key steps your organization should take during the data classification process:

• Determine the categories and criteria you will use to classify data.
• Define your objectives and what you would like data categorization to achieve.
• Outline employees’ roles and responsibilities in following data classification protocols.
• Develop security standards that align with data categories, tags, and compliance standards.

Mapping out this process can help provide employees and third parties with a clear framework for categorizing data. 

Here are a few more questions that will help you develop your data classification policy.

Questions to ask for data classification policy 

Other questions that can help you develop your data classification policy include:

• Who creates or owns the information?
• Who is responsible for the integrity and accuracy of the data?
• Where is the information stored?
• What sensitive data do we have?
• Who can access, change, or delete the information?
• How will it affect our business if the data is stolen, destroyed, or altered?
• Is the information subject to any regulations or compliance standards? If yes, what are the penalties for non-compliance?

Answering these questions will help your organization think strategically about your data. Where are you vulnerable? How can you optimize your protection?

Once you can answer those questions, you should be ready to adopt your data classification policy. These are some guiding principles to consider.

Data classification best practices

Use these best practices to build an effective data classification policy:

• Understand Your Data: You need to know what kind of data you have. Analyze your data and all regulations that your organization must follow.
• Create a Data Classification Model: Next, you should build a data classification model. Start with a few basic classification levels. You can add more complex levels as needed.
• Organize Your Data: Decide how to tag your data based on its sensitivity and potential impact. As the sensitivity increases from low to high, the classification level should also increase. Add more restrictions at each level.

Once you’ve taken these steps, you should:

• Validate your results
• Figure out how your results can benefit your organization
• Change classification criteria as needed

After following these practices, you should understand your business’s data better. This will help you develop the best strategy for its management and protection.

Compliance frameworks can be useful for building your data classification policies.

Compliance frameworks for data classification

There are several regulatory security frameworks that you should keep in mind when classifying data.

SOC 2

Systems and Organization Controls (SOC) 2 evaluates how a company’s security aligns with the Trust Services Criteria. These criteria include security, availability, confidentiality, processing integrity, and privacy.

This framework helps your organization manage customer data and third-party partner risk management.

While valuable, implementing SOC 2 can be complicated. Secureframe can help simplify your SOC 2 compliance.

HIPAA

The Health Insurance Portability and Accountability Act (HIPAA) created standards for protecting patient health information (PHI). 

PHI is considered high-risk data. Healthcare organizations must follow strict cybersecurity practices to comply with HIPAA. You need procedures for classifying the data you collect, use, store, or transmit.

You can learn more about streamlining your HIPAA compliance here.

PCI DSS

The Payment Card Industry Data Security Standard (PCI DSS) requires businesses that handle credit card data to protect cardholders’ information. 

Unlike government frameworks, private payment companies (MasterCard, Visa, etc.) enforce PCI DSS compliance.

Here you can accelerate your PCI DSS compliance with Secureframe.

GDPR

The General Data Protection Regulation (GDPR) protects the data of European Union citizens. 

Under GDPR, any organization that handles an EU citizen’s personal data must have a data classification system. Organizations also need a system for tagging data as public, proprietary, or confidential.