Introduction to Data Classification

The Digital revolution has changed everything around us; nowadays, organizations of all types and across all industries utilize digital solutions to facilitate work operations and reduce costs. People worldwide are using technology to work, study, socialize, entertainment, shopping, or online banking, to name only a few. The sum of people interactions with technology generates a large amount of digital data, combined with the data produced by organizations worldwide create a massive amount of digital data.

The total data created, captured, copied, and consumed worldwide is forecasted to increase rapidly, reaching 59 zettabytes in 2020, According to Statista. Most data are created digitally and never find their way into papers. Data classification is suggested to categorize data into groups to simplify data retrieving, processing, and storage to handle the increased volume of data.

This article will define data classification, appreciate its benefits and advantages to organizations, and list popular data classification types and schemes used in commercial and government agencies.

What is meant by data classification?

Data classification is an integral part of any data security and compliance program. It helps an organization understand where its sensitive and regulated data is stored, both on local data centers and in the cloud, to impose the required security and security policy measures to protect it from internal and external threat actors.

Data classifications achieve many benefits for organizations:

• Help organizations discover unused data, so they can eliminate it to reduce storage and maintenance costs.
• Tag data so it becomes easy to search and retrieve. For example, data tagged as "marketing" will contain only marketing information.
• Speed up the search process, as we only need to search within a particular data category instead of searching the entire data repository.

Data Classification types

Different industries accept three main data classification types.

• Content-based: The user examines each document and classifies it according to the sensitivity of its content.
• Context-based: The user checks the application, user/s, or data location and classifies it accordingly.
• User-based: This is a manual process where a user inspects each document and determines its importance or useless to organization work. And tag it accordingly.

Standard classifications of data

Organizations commonly use the following classifications when categorizing data:

• Confidential or Sensitive: These are used interchangeably and have many types. Examples of such information include:

  • Intellectual property and trade secrets
  • Vendors contract
  • Email messages containing sensitive data
  • Accounts passwords
  • Personal data (address, social security number, passport number, driver's license number, health information, etc.)

Financial data (credit/debit card number, bank account information)

• Public: contains any information that can be revealed to the public. Examples of such data include organization contact information and agent addresses.
• Private: is the data that is not critical. However, it is not meant to be disclosed to the public. Examples of such data include a company's marketing activities, emails that do not contain confidential info, and customer names and images.
• Proprietary: includes an organization work process inform for example

Government Classification Scheme

Government agencies use different classification schemes or labels.

• Top secret: Such as intelligence communications or spy names. This information requires a high level of protection.
• Secret: This includes very sensitive information that may harm national security or affect crime investigations.
• Confidential: Such as entities working with the government on some projects.
• Sensitive unclassified: This type is for official use only.
• Unclassified: Data that may be publicly released with authorization.

Data classifications benefits

The most obvious benefits of data classification appear in helping organizations achieve the following two objectives:

Data security

Data classification helps an organization better protect its data assets by knowing important data within its IT environment. By doing so, the following can be achieved:

• Adjust the security controls according to the sensitivity of the data. For example, applying encryption to protect confidential information.
• Adjust security access controls by specifying who has access to this data.
• Assess potential risks more accurately—for example, the impact of a data breach or a ransomware attack on organization work.

Regulatory Compliance

There are various regulatory programs an organization could be subject to. For example, the GDPR requires protecting European Union citizens' data, while the PCI DSS requires protecting credit cardholder information. By classifying data, an organization knows which data items are subject to which regulation to enforce the necessary security controls and auditing procedures accordingly.

Summary

Data classification can be a daunting task for organizations, especially if there is a large volume of data that needs to be categorized. Automated solutions exist to aid in data classifications; however, the organization management must determine the categories used to tag and classify the data.