What Is Data Classification? Everything You Need To Know

According to Statista, global data creation is projected to grow to more than 180 zettabytes by 2025.

While only a small percentage of this newly created data is kept, organizations are still challenged with managing more data than ever before. This data includes 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.

To help you get started, we’ll provide a simple breakdown of the data classification process. Read on to learn how to determine data sensitivity levels, what methods you can use to classify data, what steps and best practices you need to follow to create a data classification policy, and more.

What is data classification?

Data classification is the process of sorting data into different categories. This allows for easier data 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 comes after the data discovery process. The latter is relatively simple. You scan your environment to determine where structured and unstructured data resides. It will likely be spread across databases, cloud storage services, and files like PDFs and emails, among other sources. Then, within these discovered data sources, you identify different types of data and assign them classification labels. This classification process is more complex.

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

Benefits of data classification

Effective data categorization is a key part of any information security policy. It has many benefits, which we’ll go over below.

Risk management

Data classification policies should help you develop a sensible risk management strategy. Once you identify the value of your data, you can implement security measures to minimize the risk of that data being altered, stolen, or destroyed.

Data security and retrieval

Data classification can also be useful for creating data security and retrieval processes by helping you to:

  • Organize data by importance
  • Safeguard high sensitivity data
  • Streamline data searches and retrieval

Doing so can help your organization reduce user access to sensitive data, install the right data protection technologies, and optimize resource utilization for less critical data.

Organizational efficiency

Data classification policies can also help improve your organizational efficiency. For example, you can find and cut duplicate data to reduce storage and backup costs.

Regulatory compliance

Data classification can also help your organization comply with data privacy requirements and other rules and regulations by putting appropriate security controls in place and making data searchable and retrievable within specified timeframes.

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. If this data were compromised, lost, or destroyed, it 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 (PII) 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 security 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 type of data 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:

  • Define your objectives and what you would like data categorization to achieve. To start, clearly define your primary goals for data categorization. Do you want to inform regulatory compliance processes, increase employee productivity, or reduce data management and storage costs? All of the above? This step should involve stakeholders from security, compliance, and legal.
  • Determine the categories and criteria you will use to classify data. Once you understand why you’re classifying your data, you can better determine how to do so. There are multiple ways you can organize data: using metadata, tags, file type, character units, and size of data packets are just a few examples. You should also establish classification levels at this stage.
  • Outline employees’ roles and responsibilities in following data classification protocols. Employees should clearly understand they’re responsible and accountable for their use of sensitive and low-priority data. Risk mitigation steps and automated policies should be documented. This will allow employees to know to move or archive PHI if unused for 180 days, for example, or how to detect and report control failures or violations.
  • Develop security standards that align with data categories, tags, and compliance regulations. Once data has been classified by category, tag, and/or compliance regulations, you can determine appropriate security controls for protecting it. For example, medical, credit card, and personally identifiable information (PII) must be handled appropriately for different regulations and therefore may require unique security standards.
  • Periodically re-evaluate your classification criteria and process. Data classification is not a one and done process. You should periodically review your classification criteria and process as a whole to keep up with changing regulations and business objectives. This may be done on an annual basis or at whatever frequency is possible based on available resources.

Mapping out this process can help provide employees and third parties with a clear framework for categorizing data. This framework is also known as a data classification policy.

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. Below 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 level of 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: All results, whether classified manually or automatically, should be reviewed and validated for accuracy. Create a process that clearly identifies who is involved and what steps are required to review and validate these results.
  • Figure out how your results can benefit your organization: Once you’ve validated your results, you can analyze them to determine their best use. Maybe they can be used to streamline workflows or enhance a data security policy that benefits your organization.
  • Change classification criteria as needed: Your classification criteria may need to be updated due to changes in business or new regulations. So you should establish a process not only for discovering and classifying new data but also for periodically reviewing your criteria.

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.

Still unsure of what to include in your data classification policy? Use our template as a foundation to quickly create your own.

Compliance frameworks for data classification

Compliance frameworks can be useful for building your data classification policies. There are several regulatory security frameworks that you should keep in mind when classifying data.


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.


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.


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.

Learn how you can accelerate your PCI DSS compliance with Secureframe.


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.