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What is Data Classification? Types, Examples & Steps

By Securiti Research Team
Published July 19, 2023

Data is one of the fastest-growing assets in the world. It drives breakthroughs, informs decision-making, enhances product experiences, and improves patient care.

But how can an organization make sense of the massive volume of data that is scattered across different clouds and systems?

Just as a librarian must organize, categorize, and label all the books for readers to find what they need, a company must also categorize data to drive value and manage data risk. Data classification gives data users and data security, privacy, governance, and compliance teams the visibility they need to perform their responsibilities.

This blog will dig deeper into data classification, why it matters, what challenges organizations face while handling petabytes of data, and the best practices to classify data efficiently.

What is Data Classification?

At the very basic level, data classification can be defined as the practice of organizing and categorizing data for its improved understanding and efficient use.

Surveys reveal that 55% of organizations report having dark data in their corporate environment. To the uninitiated, dark data is something that’s routinely collected and stored by organizations globally, but it is not utilized for any specific purpose and is left unattended. Gartner believes that businesses keep dark data for compliance reasons, but retaining it increases costs, security, and compliance risks.

Data classification delivers a complete understanding of all the data across the organization, including shadow and dark data. Shadow data is unknown to the team, like a server running a database. Dark data, on the other hand, is the data that organizations know exists in their environment, but they don’t have context around it, such as classification details. Hence, data classification effectively sifts through data assets to identify and discover data, groups it by shared attributes, and classifies it at the data element level.

For instance, a data classification tool would first discover personal and sensitive data across on-premises, private, and multi-cloud environments to classify data elements, such as Date of Birth, Name, Address, or SSN. It would then enrich the data with metadata contexts, such as document type, age, or location. Finally, it maps it to the classification taxonomy, which could include confidential, sensitive, or public categories.

Organizing data according to context, regulatory policy, or sensitivity levels gives organizations a complete picture of their data landscape, the intended purpose of individual datasets, and their usage across departments. With these insights at their disposal, organizations are better prepared to report on data, protect it, meet compliance, and serve other business purposes.

What is the Purpose of Data Classification & Why Does It Matter?

Discovering and classifying dark data isn’t the sole concern for organizations with multi-cloud or hyperscale data environments. Various other reasons emphasize the need for a comprehensive policy and a robust data classification tool.

Creates a Foundation for Formulating Specific Policies

A robust data classification tool doesn’t just classify or label data according to its sensitivity, such as confidential, sensitive, internal, or public. In fact, it goes a level beyond and categorizes the data according to data element type and other attributes. This granular classification level is the basis for setting up specific policies, standards, and controls to manage and protect the data at various levels.

With appropriate policies, businesses can implement clear guidelines for access controls, storage controls, encryption, and similar security and privacy measures to protect data against unauthorized use and disclosure.

Helps with Cloud Migration Projects

The age of multi-cloud adoption is at its peak, with 85% of organizations indicating that they have deployed applications in two or more clouds. During migration, organizations tend to move large amounts of datasets to the cloud, such as Google Cloud Provider (GCP) or Amazon Web Server (AWS).

Data classification enables organizations to identify duplicate or obsolete data before the data is migrated to the cloud to reduce storage costs and improve data management. Moreover, organizations can leverage classification insights to efficiently determine the security policies and controls for different categories.

Take, for instance, Amazon Simple Storage Service (S3), which offers different storage classes. If the organization classifies its data prior to migration, it can select the appropriate storage class for the data. For instance, if the dataset contains sensitive data and is frequently accessed, it can be stored in the Standard class, and for less sensitive and infrequently accessed data, the Intelligent-Tiering class would be the best fit.

Separates Intellectual Property Data for Protection

Intellectual property (IP) data is something an entity or individual owns. IP data may include copyrights, trade secrets, proprietary information, patents, etc. Data classification can play a critical role in helping security teams categorize IP data separately to implement additional security and access controls, such as least privilege access, data masking, and encryption. With proper data classification of IP data and appropriate security controls, organizations can reduce the risk of unauthorized access or infringement. Protecting IP data is a serious concern regarding data regulatory laws like the PCI DSS or the International Traffic in Arms Regulation (ITAR) that govern government or military-associated data.

Supports & Streamlines Privacy Operations

Datasets often contain personally identifiable information (PII) and identities, i.e., sensitive data related to individuals, such as users or consumers. The knowledge of the PII and its people owners is highly essential for organizations to fulfill various privacy obligations, such as privacy impact assessments (PIAs), data protection impact assessments (DPIAs), data subject requests (DSRs), or breach notification obligations.

Data classification conveys needed insights to organizations, enabling them to operationalize their operations for data privacy and protection.

Enables Compliance with Laws

Data is governed by various data protection laws and compliance standards globally, such as European Union’s General Data Protection Regulation (EU GDPR), Health Insurance Portability and Accountability Act (HIPAA), Payment Card Industry Data Security Standard (PCI DSS), Sarbanes-Oxley Act (SOX), and Gramm–Leach–Bliley Act (GLBA), etc.

Data classification is critical in helping organizations comply with these mandates by discovering and classifying the data type. By knowing the data type, one can understand the relevant law that applies, such as for credit card data, PCI DSS would apply.

Take, for instance, the PCI DSS standards. A data classification tool can assist an organization in identifying and classifying a cardholder’s data as per the definition of PCI DSS, such as PAN, cardholder name, etc. With this knowledge, companies can apply relevant controls to protect the data via security measures like encryption and access controls and set appropriate retention policies. For privacy, the categorized data can be used to honor consent preferences, respond to DSRs, etc.

Data Classification Challenges

Organizations face various challenges when it comes to discovering and categorizing data, especially sensitive data. Let’s take a look at some of the most common challenges.

  • Variety: Data is available in structured, semi-structured, and unstructured formats. Structured data is usually tabular, split into headers, rows, and columns. Unstructured data exists in various formats, such as document files, media files, CSV, etc. Data may also exist in different system types across different environments, such as on-premise systems, private clouds, multi-cloud, and SaaS applications. It is challenging to discover data across diverse formats and environments and classify it accordingly.
  • Volume: The growing adoption of cloud and multi-cloud environments has caused organizations to store and generate massive volumes of data, now reaching Petabytes in size. Categorizing data for data governance or security purposes is difficult, requiring modernized solutions built for petabyte-scale data classification.
  • Velocity: Due to the growing adoption of the cloud and its ease of rapid deployment, organizations are now generating data at high velocity. It is challenging to accurately and timely classify data when it is being generated rapidly.
  • Accuracy: It is important that data must be classified with high accuracy since different departments across an organization rely on accurate data. However, a simplistic approach to classification can result in a high volume of false positives (FP). For instance, using a simple keyword-matching methodology to classify data may result in false positives if the tool doesn’t consider the context or sensitivity of data.
  • Siloed Classification Approaches: Organizations use distinct data classification tools and technologies across different clouds, applications, and data types. Each tool has its classification taxonomy, which makes it difficult for teams to align and thus creates challenges for the team.

Steps for Effective Data Classification

Data classification tools and methodologies vary across different organizations, but the core steps remain the same. The following steps are important when discovering and classifying data.

1. Data Asset Discovery

To classify all the data across an organization’s data environment, it is necessary to discover all the existing data. First, discover all the assets across the environment, including native and shadow data assets. Usually, organizations have many unmanaged or shadow data assets, which are unauthorized or unapproved systems or applications in the environment, running without proper oversight. These systems exist in various accounts or geographies, with some of them containing sensitive data. Therefore, it is critical to have a comprehensive approach that discovers all the assets first.

2. Data Labeling

Next comes the classification phase, which usually starts with creating a data classification policy based on an organization’s needs, such as for data security, governance, or compliance purposes. The classification tool must provide granular level labeling in that it should offer element level labeling, such as name, phone number, credit card number, etc. It should be followed by category-level labelings, like public, confidential, etc. It is best to use AI/NLP algorithms based on data context to deliver better accuracy. The NLP tools classify data precisely using different contexts, such as data relationship, meaning, or intent.

3. Metadata Enrichment

Metadata refers to the data about data. After data labeling, the data classification tool should automatically generate business, privacy, and security metadata. This type of metadata may include attributes such as data system name, location, residency, retention period, quality, applicable laws, and security and privacy violations.

4. Data Cataloging

Data cataloging isn’t necessarily a part of data classification. However, once all the data is classified, organizing it in a catalog is recommended, making it easier for data users to search, discover, and understand data.

Once all the data is discovered and classified with appropriate metadata, tags, and labels, organizations can use these insights to establish proper controls to meet data security, governance, and compliance obligations.

How Securiti Can Help

Securiti Sensitive Data IntelligenceTM (SDI) goes beyond basic data discovery to help organizations accurately classify data and get rich data context, including security and privacy metadata. For e.g., with Securiti, the privacy team can leverage metadata context to identify people owners of a PII data element quickly. SDI delivers the shared data intelligence context for data security, privacy, governance, and compliance teams, enabling them to automate all controls while reducing the cost and complexity of not operating multiple data classification tools across teams and cloud siloes.

How Securiti’s SDI helps:

  • Broadest Coverage of clouds and data systems
  • Designed for Hyperscale
  • Higher data classification efficacy
  • Common taxonomy across hybrid multi-cloud and SaaS
  • Data classification at rest and in motion
  • Integrated data security, governance, and privacy management
  • Flexible deployment models

Sign up for a demo to learn more about Sensitive Data Intelligence.


Structured data is usually in a pre-defined format, such as the data available in tables or relational databases. It is easy to analyze structured data as the relationship between datasets is defined. Unstructured data, on the other hand, doesn’t have a predefined format or scheme, which is why it is challenging to analyze unstructured data. Unstructured data examples include CSV files, docs, image files, emails, etc.

There are numerous benefits of automated classification of data. For instance, it reduces human errors to a great extent and increases accuracy. With automation, the process can be scaled to include the classification of large volumes of datasets. More importantly, with proper data handling, organizations can better ensure they are on the right track to compliance.

There’s no data classification regulation, only data protection regulations. These regulations require users to classify data to determine the applicable controls.

There are several steps that organizations could take to organize and classify their data in the cloud. First, it is important to ensure that the classification tools must support the cloud. Secondly, identify all the systems, data stores, or applications containing personal and sensitive data. Develop a clear yet comprehensive classification policy that considers different attributes, labels, and tags to discover and classify data accordingly. Leverage an automated classification tool for speed, accuracy, and scalability.

Various aspects must be considered while reviewing and updating data classification policies. For instance, new data privacy regulations are being enacted worldwide, and the existing laws receive amendments due to the evolving technologies and industry best practices. It is recommended to perform periodic risk assessments to identify emerging risk trends and optimize the classification policies accordingly.

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