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What is a Data Classification Matrix?

by Chris Brook on Monday February 13, 2023

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Having a data classification matrix, part of a comprehensive data classification policy, can help organizations better determine the risk level of data.

Like it or not, data privacy is a critical component of today’s corporate world. Whether it’s simply online information about the company or the names, addresses, and salaries of its board members, each piece of data must be reviewed so that it can be properly protected. This task can seem daunting, but a data classification matrix can help to make the process a little easier. 

What is a Data Classification Matrix?

Data classification can be a cumbersome and challenging process for any organization, but by creating a data classification matrix, a company can quickly determine the right security settings to apply to their data and keep all their security specifications in one place. A data classification matrix can be part of a comprehensive data classification policy

How to Create a Data Classification Matrix

There are several templates to create a data classification matrix, and it’s best to pick a template that best suits your needs. Here’s an example of a matrix with four classification levels: public, internal, confidential, and restricted.






Risk level

No risk





This is data that’s disclosed to the public such as general details about the company. It carries no risk and is openly revealed to the public.

This data is known to most or all company employees. If this data is revealed, it may have low or no impact on the company.

This information is created for the internal use of the company, and it’s not meant to be revealed. If revealed, it may have a moderate impact on the company.

This is the most confidential data and revealing it can lead to huge financial or reputational losses to the company.

Access rights

Open to public

Low limitations

Available to company employees, generally on a need-to-know basis

Very sensitive information available only to some top-level employees.


This data has no harmful impact

If this data is published, it can lead to some inconvenience.

If this data ends up in the wrong hands, it can lead to losses but not business-critical losses.

The impact of this data being revealed to the public can be devastating to the company and possibly its customers.


Data that’s available on the company website

Public press releases

Public seminars

Employee data

Employee roles and responsibilities

Company event data

Business sensitive data

Intellectual property

Data protected by regulations

Company supplier data

User credit card data

Client HIPAA information


Storage options

Can be posted on a website, blog, or a publicly accessible portal

A computer or server that’s available to all or most employees


A server or a virtual server that’s available only to certain teams

Highly secure server or virtual server that can be accessed by only a few top-level employees

Other security considerations

No major considerations

Must be protected by a username/password mechanism

Should be accessible only by organization insiders or other authorized recipients

Must be stored in an encrypted form

Must travel over the network in an encrypted format

Should be accessible to only a few teams

There must be access controls on this data

Highest level encryption

Protected with multi-level authentication

File-level encryption


Audit controls

No audit controls required

Some level of monitoring or reviewing might be required

Data stewards are given the responsibility of monitoring and reviewing the system for potential misuse. In case of possible misuse, it may be reported to higher authorities, depending on the severity of the case.

Data stewards have the responsibility of monitoring and reviewing the system for potential misuse or unauthorized access.

A backup plan must be present to quickly act if something has gone wrong.


An organization can enter types of data in this matrix according to their industry and assign them levels of privacy.

Best Practices to Create a Data Classification Matrix

Here are the steps to follow while creating a data classification matrix:

  • Discuss with experts: Discuss with in-house data experts or hire an agency that can guide you to the correct framework for your data types.
  • Set a goal: Before you create a classification matrix, you must define a goal. Each data type should be mapped to the correct class so it can be given the correct protection. This reduces the risk faced by sensitive information if a data breach were to happen.
  • Define the scope: It might not be possible to regulate all data in a company. This is especially true for big organizations. By defining the scope of the matrix, you can classify only the data you want to regulate.
  • Assign responsibilities: Assigning ownership to data makes it easier to classify. Not all types of data may have an owner but creating ownership becomes simpler once the scope of the matrix is defined.
  • Assign safety grades: There are generally three to four safety grades according to the risk level of the data. A company can have more safety grades according to their requirements. However, it’s best not to make the data classification matrix too complicated.
  • Assign safety measures: According to the safety grades, the data in the organization will be protected by safety measures.
  • Maintenance of the matrix: Since data changes throughout its lifetime, its risk level also changes. Accordingly, its safety grades and measures should be changed. This can be done if the matrix is regularly reviewed and updated.


A data classification matrix can help you keep track of the security required for the different types of data at your company. This can include:

  • Who should have access to that data
  • Where that data should be stored
  • Who has responsibility for maintaining that data, and
  • The audit requirements for that data

Taking the time to create a data classification matrix can help to prevent a much larger security concern further down the road. To learn more about data classification, download our Definitive Guide to Data Classification.


Tags:  Data Classification

Chris Brook

Chris Brook

Chris Brook is the editor of Digital Guardian’s Data Insider blog. He is a cybersecurity writer with nearly 15 years of experience reporting and writing about information security, attending infosec conferences like Black Hat and RSA, and interviewing hackers and security researchers. Prior to joining Digital Guardian–acquired by Fortra in 2021–he helped launch Threatpost, an independent news site that was a leading source of information about IT and business security for hundreds of thousands of professionals worldwide.

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