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5 Common Data Classification Types

by Chris Brook on Wednesday November 23, 2022

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When it comes to classification, not all data needs to be treated the same way. There are five common categories that organizations can follow.

A company’s data is one of its most valuable assets, and data-driven organizations that use this asset effectively can gain a substantial competitive advantage over market rivals. In this article, we’ll review the different types of data and what you can do to prioritize the protection of the most sensitive data in your organization.

Why Should Data be Classified?

Most modern businesses have extensive data resources. This information can be structured or unstructured and be generated through multiple data streams. A typical company may have structured data resources stored in relational databases with data lakes holding unstructured information. An organization needs to understand the types of data it has so that it can make informed decisions on how to use and protect it.

Not all data needs to be treated the same way. The prevalence of data streams like social media can result in a lot of extraneous data that does not bring any value to the business. This data may not need to be backed up and protected with as much vigilance as other, more valuable information.

Classifying data enables an organization to develop backup and data loss prevention (DLP) policies and procedures tailored to the sensitivity and importance of the information in question. Proper classification makes it possible to afford the right level of protection to data resources and restrict access to sensitive information.

In regulated industries such as healthcare or the financial sector, the importance of data classification increases. Mishandling sensitive information can not only result in data loss but exposes an organization to financial fines and penalties.

5 Common Categories of Data Classification

Data can be classified in several ways. Classifying data as structured or unstructured influences which software solutions are best for storing and extracting value from the information. This can be important when performing analytics and studying market trends with information from multiple sources.

The most common method of classifying data is by its level of sensitivity. The sensitivity of data resources impacts the methods used to store and protect them. In some instances such as email addresses, data can be classified in multiple ways. Organizations must determine which classification makes sense for these data elements.

The following are five common categories used for data classification:

  1. Public data
  2. Private data
  3. Internal data
  4. Confidential data
  5. Restricted data

Public Data

Public data can be important but is accessible to the public. Since this data is openly shared, it is the lowest level of data classification and its public nature makes it unnecessary to protect its use by unauthorized actors. Examples of public data include:

  • The names of companies and members of their executive team
  • Physical and email addresses
  • Press releases and promotional material
  • Company organizational charts and job descriptions

Private Data

Private data requires a greater level of security than public data. This data should not be available for public access and is often protected through traditional security measures such as passwords. Compromised private data can pose a risk to an individual or an organization. Private data can include:

  • Email addresses and other personal contact information
  • Employee identification numbers
  • Smartphone content
  • Personal email content

Internal Data

The use of an organization’s internal data is usually limited to its employees. Internal data can have different security requirements that affect who can access it and how it can be used. Examples include:

  • Business plans and marketing strategies
  • System IP addresses
  • Internal company websites
  • Financial data and revenue forecasts

Confidential Data

The next level of data classification is confidential data. This information should only be accessed by a limited audience that has obtained proper authorization. Methods like identity and access management (IAM) tools are used to control access to confidential data. The loss of confidential data is harmful to individuals and organizations. Confidential data includes:

  • Social Security, driver’s license, and other personally identifying numbers 
  • Credit card and banking information
  • Medical and health information
  • Employee records
  • Biometric identifiers

Restricted Data 

Restricted data is the classification used for an organization’s most sensitive information. Access to this data is strictly controlled to prevent its unauthorized use. It needs to be encrypted for additional protection. The loss of restricted data can severely impact an organization or the individuals whose information is compromised. Examples of restricted data are:

  • Protected health information (PHI) as defined by regulatory agencies
  • Financial and tax data
  • Information that is secured by confidentiality agreements
  • Intellectual property

Confidential and restricted data should be afforded similar levels of protection. At times it can be difficult to differentiate between these two classifications.

Prioritizing the Protection of Critical and Sensitive Data Resources

Identifying critical and sensitive data resources with data classification enables an organization to prioritize the way the information is handled and how it is accessed. It’s a necessary first step toward developing a data classification policy and implementing the proper controls to maintain data security and availability.

Once a company’s sensitive and critical data has been identified, tools to help prevent its loss can be implemented in the environment. Digital Guardian offers cloud-based SaaS data loss prevention solutions, including Network DLP and Endpoint DLP, that provides customers with multiple benefits that include:

  • Deep visibility into an organization’s sensitive data resources
  • Flexible controls to protect data before it gets lost
  • Cross-platform support for Windows, macOS, and Linux systems
  • Comprehensive data classification

Digital Guardian’s DLP solution helps businesses understand and protect their data resources. The solution is powered by Amazon Web Services (AWS), allowing companies to deploy the tool without expending internal technical resources. The cloud-based nature of this DLP solution enables it to scale as a business grows.

Companies can schedule a free demo to see how Digital Guardian’s DLP solution can help them meet the many challenges of effectively protecting data resources.

Conclusion

Every organization uses data, but not all data requires the same level of protection. That’s why it’s crucial to understand the five common data classification types, including: 

  • Public
  • Private
  • Internal
  • Confidential, and
  • Restricted

With effective data classification, you’ll gain insight into which data is most sensitive, which should be prioritized and which should be given the maximum protection.

 

Tags:  Data Classification

Chris Brook

Chris Brook

Chris Brook is the editor of Data Insider. He is a technology journalist with a decade of experience writing about information security, hackers, and privacy. Chris has attended many infosec conferences and has interviewed hackers and security researchers. Prior to joining Digital Guardian he helped launch Threatpost, an independent news site which is a leading source of information about IT and business security for hundreds of thousands of professionals worldwide.

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