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Understanding Data Classification

Data is essential for all organizations regardless of its size. Whether it’s customer information, financial records, intellectual property, or proprietary research, data plays a significant role in the development of business strategies and driving innovation. However, this valuable resource comes with various challenges.

The exponential growth of data coupled with evolving regulatory requirements and threats make it difficult to protect and manage data. This is where data classification steps in as a powerful technique that assists in organizing and protecting data while ensuring its compliance with international regulations.

Data classification is based on categorizing data in terms of its sensitivity, importance, context, regulatory requirements, business requirements, and compliance. It enables organizations to gain better control over their data, allowing them to protect their most critical assets, ensure compliance with data privacy regulations, and make informed decisions.

For this purpose,  the data classification job responsibilities within an organization require categorizing and labeling data based on its sensitivity and importance.

In the article we’ll closely look at the data classification definition, types of data classification, how data is classified, its importance, implementation challenges, and the transformative capability it has on modern data-driven enterprises.

What is Data Classification?

Data classification is a systematic procedure used by organizations to categorize their data based on certain criteria. Here is a quick overview of the data classification process.

1.   Identification of Data

Data classification starts with identifying the various types of data that an organization handles. This data can either be structured (e.g., databases, spreadsheets) or unstructured (e.g., emails, documents, multimedia files).

2.   Content Analysis

In the next stage, organizations analyze the content of data. This involves examining the information contained within the data, such as text, numbers, or multimedia elements. Content analysis helps organizations determine the nature and sensitivity of the data.

3.   Specifying Categorization Criteria

Organizations specify criteria for categorizing data. These criteria can be based on various factors, including:

  • Data Sensitivity: How sensitive the data is. Data can be categorized as public, internal, confidential, or highly confidential based on its sensitivity.
  • Compliance: Considering whether the data needs to comply with specific regulations or industry standards, such as GDPR, PCI DSS, and HIPPA.
  • Purpose of Usage: The intended use of data. Is the data meant to be used for internal operations, customer records, financial transactions, research, or marketing?
  • Duration of Usage: Determining how long the data should be retained. This decision considers whether the data is temporary or permanent.
  • Access Control: Defining who should have access to data and what level of access they should have.

4.   Data Classification Levels

Data classification levels provide a clear structure to understand and manage the vast amounts of data an organization handles. By defining these levels, businesses can quickly determine how each piece of information should be treated, stored, and shared. This structure is essential for keeping sensitive information safe, reducing risks, and ensuring the right people access the right data.

Based on the specified criteria, data is classified into different categories. Common classification levels include:

  • Public Data: This includes information that is publicly accessible and does not pose any significant risk if compromised.
  • Internal Data: Data meant to be used for internal use falls into this category.
  • Confidential Data: This category includes information that is sensitive and needs to be kept safe and only a few people should be allowed to access it.
  • Highly Confidential Data: It includes extremely confidential data, such as trade secrets or personal identification, falling into this category that requires strict controls.

The above data classification levels address a commonly asked question: how is data classified? based on its confidentiality.

5.   Data Classification and Labelling

Once the data is categorized, it is labeled or tagged with metadata that indicates its classification level. This labeling makes it easy to identify and manage data according to its sensitivity.

6.   Access Control

The access control mechanism is implemented to restrict data access based on its classification. For instance, highly sensitive data will only be accessible to an authorized individual.

7.   Data Classification and Handling Policy

Data classification follows specific handling policies. For instance, confidential data requires encryption during transmission and storage.

8.   Data Retention Policies

Data classification plays a significant role in determining organizations’ retention policies. It helps them decide how long the data should be retained and when it should be securely disposed of.

9.   Data Monitoring and Auditing

Organizations can implement monitoring and auditing processes to ensure that data is being handled according to its classification.

10.  Regular Review

Data classification is not a fixed process. Instead, it’s an ongoing process that must be consistently reviewed and updated. This is necessary to ensure that the way data is categorized stays up-to-date and aligns with any shift in the nature of data and the evolving needs of the business.

Stay tuned for our next article that will explore why data classification is important.