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What is Data Classification? Definition, Levels & Examples

Philip Robinson | 11 min read| Updated On - February 29, 2024

What is Data Classification? Definition, Levels & Examples

Data Classification is simply the process of organizing data based on a set of pre-defined categories. Since organizations have limited resources, it is important for them to know exactly where their most sensitive data is located, in order to be able to allocate those resources in the most effective manner.

Data Classification Definition

Data classification is the process of categorizing data based on its confidentiality in order to determine the level of access that should be granted to it and the level of protection it requires against unauthorized access or disclosure. The classification of data can be based on factors such as the type of data, its value, the level of risk of its exposure, and any applicable regulatory requirements. The purpose of data classification is to provide a framework for data management and security that enables organizations to identify and protect their most valuable and sensitive data assets.

Data Classification Reasons and Benefits

There are many reasons/benefits why organizations choose to classify their data, which are as follows;

  • Data classification helps ensure sensitive information is properly protected
  • It allows organizations to prioritize resources based on the value of the data
  • Data classification can help with regulatory compliance by making it easier to respond to subject access request (SARs)
  • It enables more effective data sharing and collaboration within an organization
  • Proper data classification can reduce the risk of data breaches or leaks
  • It can aid in disaster recovery and business continuity planning
  • Classification can help organizations determine appropriate levels of access and control for different types of data
  • Classification allows for better data management and organization
  • It can support more accurate reporting and analysis of data
  • Data classification can help organizations save time and resources by focusing efforts on the most important data.

Types of Data Classification

One common classification is based on sensitivity or confidentiality. In this approach, data is classified as public, internal, confidential, or highly confidential. Public data is non-sensitive information that can be openly shared. Internal data is restricted to an organization and accessible only to authorized personnel. Confidential data requires a higher level of protection due to its sensitive nature, such as customer details or financial records. Highly confidential data includes trade secrets or classified information, which demands the highest level of security.

Another classification type is based on data content. It involves categorizing data according to its characteristics or attributes. For instance, data can be classified as text, images, audio, video, or numerical data. This classification helps in understanding the nature of the data and determining appropriate storage and processing techniques.

Temporal data classification is used to organize data based on time-related properties. Time-based classifications include historical data, current data, or forecasted data. Historical data refers to past records, while current data represents real-time information. Forecasted data, on the other hand, involves predicting future trends based on historical or current data.

Data classification can also be based on the purpose or usage of the data. Examples include reference data, transactional data, or analytical data. Reference data provides a framework for other data and includes things like country codes or product catalogs. Transactional data captures the details of specific business transactions. Analytical data, on the other hand, is used for analysis and decision-making, often derived from multiple sources. Learn more about Data Classification types.

Data Classification Levels

Data classification involves assigning levels of classification to data based on its sensitivity and confidentiality. These levels help determine the appropriate handling, storage, and access controls for the data. Here are the different levels of data classification commonly used:

  1. Unclassified: This is the lowest level of data classification. Unclassified data contains information that is non-sensitive and can be freely shared or accessed without any restrictions. It does not pose any risk if disclosed or accessed by unauthorized individuals.
  2. Confidential: The confidential level is used for data that requires protection due to its sensitive nature. It includes information that, if disclosed or accessed without authorization, could harm individuals or organizations. Access to confidential data is restricted to authorized personnel who have a legitimate need to know.
  3. Secret: Secret data classification is used for highly sensitive information that, if compromised, could cause significant damage to national security or an organization’s operations. Access to secret data is strictly controlled, and only individuals with appropriate security clearance and a need-to-know basis can access it.
  4. Top Secret: This is the highest level of data classification. Top secret data contains information that, if disclosed, could cause severe damage to national security or critical infrastructure. It is heavily protected and access is limited to a select few individuals with the highest security clearances.
  5. Special Categories: In some cases, additional special categories may be defined to address specific types of sensitive data. These categories could include sensitive personal information, financial data, health records, or legal information. Each special category may have its own set of access controls and protection requirements.

Data classification levels ensure that data is handled and protected according to its sensitivity. Organizations and governments define their specific classification levels and associated security protocols based on their unique requirements and the nature of the data they handle. Implementing appropriate data classification helps safeguard sensitive information and maintain data integrity and confidentiality.

Data Classification Examples

Here are some examples of data classification:

Personal Identifiable Information (PII): This classification includes data that can identify an individual, such as names, addresses, social security numbers, or phone numbers. It is classified as sensitive and requires strict protection to prevent identity theft or privacy breaches.

Financial Data: Financial data classification encompasses information related to financial transactions, banking details, credit card information, or income records. It requires a high level of confidentiality and security to prevent financial fraud or unauthorized access.

Medical Records: Medical data classification involves healthcare-related information, including patient medical history, diagnoses, treatment plans, or test results. It falls under strict privacy regulations, such as the Health Insurance Portability and Accountability Act (HIPAA), and requires strong safeguards to protect patient privacy.

Intellectual Property: This classification includes trade secrets, patents, copyrights, or proprietary information that belongs to a company or individual. Intellectual property data requires stringent protection to maintain its competitive advantage and prevent unauthorized use or theft.

Government Classified Information: Government data classification involves sensitive information related to national security, defense, or intelligence. It includes classified documents, plans, or strategic information that must be protected from unauthorized disclosure to maintain the integrity and security of the nation.

These are just a few examples of data classification categories. Organizations and industries may have their own specific classifications based on their unique needs and compliance requirements. Data classification ensures that appropriate security measures and access controls are implemented based on the sensitivity and confidentiality of the data.

Data Classification Process

The process of data classification will vary based on the organization’s objectives, but there are certain common practices that can lead to successful outcomes. Below are some best practices to consider:

1. Define the objectives of the data classification process

  • Identify the in-scope systems for the initial classification phase
  • Determine the applicable compliance regulations
  • Consider other business objectives such as risk mitigation, storage optimization, and analytics.

2. Categorize data types

  • Identify data created/collected by your organization
  • Distinguish proprietary data from public data
  • Identify all regulated data, such as that covered by GDPR, HIPAA or CCPA.

3. Establish classification levels

  • Determine the number of classification levels needed
  • Document each level and provide examples (use a classification matrix)
  • Train users to classify data if manual classification is required.

4. Define the automated classification process

  • Define the prioritization criteria for discovering sensitive data
  • Establish the frequency of classification, and resources required to automate the process.

5. Define categories and classification criteria

  • Establish high-level categories and provide examples
  • Define or enable applicable classification patterns and labels
  • Establish a process for validating both user classified and automated results.

6. Define outcomes and usage of classified data

  • Document risk mitigation steps and automated policies
  • Determine analysis processes for classification results
  • Establish expected outcomes from analytics.

7. Monitor and maintain your classification process

  • Develop a workflow to classify new or updated data
  • Review and update the classification process if necessary due to changes in business or regulatory requirements.

Data Classification Best Practices

Here are some best practices to consider:

  1. Define Data Classification Policies: Develop clear and comprehensive data classification policies that outline the criteria, levels, and procedures for classifying data. These policies should align with industry best practices and regulatory requirements.
  2. Involve Stakeholders: Engage key stakeholders, such as data owners, IT personnel, legal teams, and security professionals, in the data classification process. Collaborative input helps ensure a holistic and accurate classification of data.
  3. Educate Employees: Conduct regular training and awareness programs to educate employees about data classification principles, their roles and responsibilities, and the importance of protecting classified data. This helps promote a culture of data security within the organization.
  4. Automate Classification: Leverage technology and data classification tools to automate the classification process. These tools use various techniques, such as pattern matching, keyword analysis, or machine learning algorithms, to classify data accurately and efficiently.
  5. Assign Data Owners: Assign data owners or custodians responsible for classifying, managing, and protecting data within their respective domains. Data owners should have a clear understanding of the classification policies and should regularly review and update data classifications as needed.
  6. Implement Access Controls: Apply access controls based on the data classification levels. Limit access to classified data to authorized personnel with a need-to-know basis. Use strong authentication mechanisms, role-based access controls, and encryption to protect data.
  7. Regularly Review and Update Classifications: Conduct periodic reviews to ensure data classifications are accurate and up to date. Data classification should be a dynamic process that adapts to changes in data sensitivity, regulatory requirements, or organizational needs.
  8. Monitor and Audit Data Access: Implement robust monitoring and auditing mechanisms to track data access, usage, and modifications. Regularly review audit logs to identify any unauthorized access attempts or policy violations.
  9. Data Retention and Disposal: Establish clear policies for data retention and disposal. Determine the appropriate retention periods for each classification level and ensure secure data destruction when data is no longer needed.
  10. Continuously Improve: Continuously evaluate and improve data classification practices based on feedback, industry trends, and emerging technologies. Stay updated with evolving data privacy and security regulations to ensure compliance.

By following these best practices, organizations can enhance their data protection efforts, reduce risks, and ensure that data is properly classified and secured throughout its lifecycle.

How Lepide Helps with Data Classification

As data breaches continue to make the headlines, and Governments across the globe implement their own data privacy laws, the importance of data classification cannot be overstated. The Lepide Data Security Platform plays a crucial role in this process. It facilitates the discovery and classification of various types of data across a wide range of platforms, including both cloud-based and on-premise servers. Below are some of the main features/benefits that our Data Classification software provides.

Sensitive Data Discovery – Pre-defined schemas can be used to locate unstructured sensitive data across all data repositories, on-premise or cloud-based, which can be aligned it with compliance mandates like HIPAA, SOX, PCI, GDPR, CCPA, and more.

Incremental Scanning – Our solution scans various file formats like word and text documents, PDF files, and Excel spreadsheets to discover sensitive data. Data can be classified incrementally during creation and modification, ensuring a fast, scalable, and reliable process.

More context to classified data – Our software provides information about sensitive data location, access, and usage, enabling organizations to apply appropriate access controls.

Real-time threat detection – Our software can automatically identify and respond to hazardous user behavior in real-time, and provide reports and alerts on how users interact with sensitive/regulated data.

Reduction in False Positives – The Lepide software leverages proximity scanning to discover patterns that add context, ensuring accurate predictions of sensitive data and avoiding false positives.

Better Access Governance – Our data classification solution enables companies to manage access to sensitive information, restrict excessive permissions, for better data access governance (DAG).

Prioritization Based on Risk – Our solution assesses the level of risk associated with content, categorizes it, and assigns scores. Identifying important data enables organizations to concentrate on it and implement effective access control and activity monitoring.

If you’d like to see how the Lepide Data Security Platform can help you discover and classify your sensitive data, schedule a demo with one of our engineers or start your free trial today.

Philip Robinson
Philip Robinson

Phil joined Lepide in 2016 after spending most of his career in B2B marketing roles for global organizations. Over the years, Phil has strived to create a brand that is consistent, fun and in keeping with what it’s like to do business with Lepide. Phil leads a large team of marketing professionals that share a common goal; to make Lepide a dominant force in the industry.

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