Learn the best practices for data classification, a process that organizes data into categories based on its sensitivity, value, and risk, for information systems.

Before you begin classifying your data, start by collaborating with different business units to understand their objectives and sensitive data types. For example: Privacy department: Personally Identifiable Information (PII) Engineering teams: Intellectual property and specific projects with external partners Establish a robust process for data access governance and data ownership. This can be managed by tagging the data or maintaining a detailed data catalog. At the end of this process, create a document that summarizes different classification levels, common sensitive data types in each level, and their business impact in case of a breach.

There are many ways to label data, depending on the technology it resides in. For example, Google Workspace has different metadata compared to Amazon S3 or Snowflake. When evaluating a solution that supports automatic classification and labeling, ensure it can label data across all the different platforms you use.

Last updated on Aug 7, 2024

What are the most effective data classification practices?

Data classification is the process of organizing data into categories based on its sensitivity, value, and risk to the organization. It helps to protect data from unauthorized access, misuse, or loss, as well as to comply with regulations and standards. Data classification practices vary depending on the type, size, and industry of the organization, but some common principles and best practices can help to achieve effective data classification. In this article, you will learn about the most effective data classification practices for information systems.

1 Define data classification criteria

When classifying data, it's important to define criteria that reflect the organization's business objectives, legal obligations, and security needs. Common criteria include data sensitivity, data lifecycle, and data ownership. Data sensitivity can refer to the level of confidentiality, integrity, and availability required for the data. Data lifecycle looks at the stages of creation, storage, use, and disposal of data. Data ownership considers the roles and responsibilities of the data owners, custodians, and users; for example, data owners are accountable for classification and protection while custodians are responsible for implementing security measures.

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Ron Reiter
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Before you begin classifying your data, start by collaborating with different business units to understand their objectives and sensitive data types. For example: Privacy department: Personally Identifiable Information (PII) Engineering teams: Intellectual property and specific projects with external partners Establish a robust process for data access governance and data ownership. This can be managed by tagging the data or maintaining a detailed data catalog. At the end of this process, create a document that summarizes different classification levels, common sensitive data types in each level, and their business impact in case of a breach.

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Dwight Turner

Data Security & Privacy Analyst | Food Nerd 🌮 🫛 🥟 | CIPM | CRISC Associate
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Start with the big picture and as much info about your system as possible. Data inventories and data flows will be helpful, but you also need to know the context of your business. For example, which data is most crucial for the bottom line? It is also never too early to consider regulatory compliance. For data privacy, you'll want to know exactly where personally identifiable information is stored, how long it is retained, and how it is protected. All of these details can have an impact on how you classify your data and they may not look the same from one organization to another.

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2 Apply data classification labels

Applying labels to data is the next step in data classification. Labels can be applied manually or automatically, depending on the volume and complexity of data. Visible or invisible labels can be used to indicate a classification level and category, depending on the purpose and audience of data. Common types of labels include metadata, which is information that describes the characteristics and context of data; tags, which are keywords or codes that identify the classification of data; and headers or footers, which are text or symbols at the top or bottom of a document or file that display the classification of data. Such labels can be used to filter, sort, or search data, as well as to enforce security policies and rules. Furthermore, they can be used to inform or warn users about its nature and handling.

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Yair Cohen

Co-Founder & VP Product @ Sentra | ex-Datadog, Microsoft
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There are many ways to label data, depending on the technology it resides in. For example, Google Workspace has different metadata compared to Amazon S3 or Snowflake. When evaluating a solution that supports automatic classification and labeling, ensure it can label data across all the different platforms you use.

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3 Implement data classification policies

The final step in data classification is to implement policies that govern the access, use, and management of data according to its classification. These policies should be clear, consistent, and enforceable, as well as aligned with the organizational culture and values. For example, data security measures and controls like encryption, authentication, authorization, or auditing should be implemented to protect data from unauthorized or inappropriate access. Additionally, rules and procedures should be established for data retention based on its value, relevance, or legal requirements. Furthermore, guidelines and agreements should be created to regulate how data can be transferred, exchanged, or disclosed to internal or external parties based on its purpose, consent, or confidentiality. Data classification is a vital practice for information systems that deal with diverse and sensitive data. By following these effective practices you can enhance the security, compliance, and quality of your data as well as the efficiency and performance of your information systems.

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4 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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What are the most effective data classification practices?

1

2

3

4

1 Define data classification criteria

2 Apply data classification labels

3 Implement data classification policies

4 Here’s what else to consider

Information Systems

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What are the most effective data classification practices?

1

2

3

4

1 Define data classification criteria

2 Apply data classification labels

3 Implement data classification policies

4 Here’s what else to consider

Information Systems

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