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Data Classification

What is Data Classification?

Data classification is the process of categorizing and labeling data based on its level of importance and sensitivity. It’s like putting your valuable information into different lockers, each with a specialized level of security. The idea is to categorize and label data based on how sensitive it is so that you can keep your most important information safe and secure.

Data classification helps to identify and protect sensitive information, such as personally identifiable information (PII), payment card information (PCI), protected health information (PHI) financial data, confidential business information, and other types of sensitive information. Imagine having all of an organization’s employees' personal data and company financial information in one place and needing to know which information is sensitive and needs extra protection. Data classification solves this problem by helping identify and protect sensitive information.

By classifying data, organizations can take control of their information assets and make sure they’re protected. They can understand what data is sensitive, who should have access to it, and how it should be protected. With the proper security measures in place, such as encryption, access controls, and data loss prevention policies, they can reduce the risk of data breaches and protect against cyber threats.

Data Classification Levels

Data classification can be done manually or automatically, using a combination of human judgment and advanced algorithms. The data classification levels can vary, ranging from simple labels such as “public,” “confidential,” and “sensitive” to more detailed categories based on specific regulations and industry standards.

Example of data classification levels:

  1. Confidential Data: This is the most sensitive category and includes data that must be protected at all costs, such as trade secrets, financial information, personally identifiable information (PII), and confidential business information.
  2. Internal Use Only: This category includes sensitive data but is not as critical as confidential data, such as employee payroll information, internal memos, and project plans.
  3. Restricted Data: This category includes sensitive data but is not as critical as confidential data, such as customer information, marketing plans, and pricing information.
  4. Public Data: This category includes data that is not sensitive and can be freely shared with the public, such as company press releases and marketing materials.
  5. Archived Data: This category includes data that is no longer actively used but still needs to be retained for legal, regulatory, or historical reasons, such as old financial reports and personnel records.
Reasons to Implement a Data Classification Process

Why is Data Classification Important?

Data classification is a crucial aspect of information security because it helps organizations understand the level of sensitivity of different types of data and identify the appropriate measures to protect it. This is important because not all data is created equal; some are more sensitive or valuable than others.

Data categorization helps organizations determine which data needs to be protected and the required protection level. For example, highly sensitive data such as financial information, personally identifiable information (PII), or confidential business information may need to be encrypted and stored in secure locations. In contrast, less sensitive data may be stored on less secure systems.

Data classification also helps organizations align with compliance regulations such as the General Data Protection Regulation (GDPR), the Health Insurance Portability and Accountability Act (HIPAA), or the Payment Card Industry Data Security Standard (PCI DSS). These regulations often require organizations to implement specific security measures for protecting sensitive data, and data classification is the first step in determining which data falls into this category.

What are Some Data Classification Examples?

There are several types of data that must be classified for better data security, as they are considered sensitive and require protection from unauthorized access, theft, or loss. 

Here are some data classification examples essential in many organizations:

  1. Personal Identifiable Information (PII): This includes data that can be used to identify an individual, such as full name, Social Security number, driver's license number, or passport number.
  2. Financial Information: This includes data related to financial transactions and accounts, such as credit card numbers, bank account numbers, and investment information.
  3. Confidential Business Information: This includes data that is proprietary to a company and gives it a competitive advantage, such as trade secrets, business plans, and market research.
  4. Health Information: This includes data related to a person's health status and medical history, such as diagnoses, treatment plans, and prescription information.
  5. Intellectual Property: This includes data related to patents, trademarks, copyrights, and trade secrets.
  6. Government Information: This includes data that is classified or restricted by government agencies, such as national security information, law enforcement records, and classified military information.
  7. Employee Information: This includes data related to employees, such as payroll information, job performance evaluations, and disciplinary records.

These are just a few examples of the classification data that is vital for better data security. The specific types of data that must be classified will vary based on the needs and security requirements of each organization. However, the goal of data classification is always to help organizations better understand the level of sensitivity of their data and determine the appropriate security measures needed to protect it.

Common Compliance Standards

How does Data Classification Improve Data Security?

Data classification is vital to data security as a means of organizing and categorizing data based on sensitivity, value, and criticality to the organization. This information is then used to prioritize and determine the appropriate security measures that need to be applied to protect the data from unauthorized access, theft, or loss.

There are many key ways data classification is used in data security, including:

  1. Risk Assessment: Data classification is used to identify the most critical assets and prioritize protecting sensitive data. This helps organizations to focus their cybersecurity efforts on the areas that require the most attention.
  2. Access Control: Data classification helps organizations to determine who should have access to sensitive data and what level of access they should have. For example, highly sensitive data may only be accessible by a small group of authorized personnel, while less sensitive data may be accessible by a wider group of employees.
  3. Data Encryption: Data classification helps organizations determine which data requires encryption and the necessary level of encryption. For example, some highly sensitive data might require encryption both at rest and in transit, while less sensitive data may only need to be encrypted at rest.
  4. Data Backup and Recovery: Data classification helps organizations determine which data needs to be backed up and how often. For example, highly sensitive data may need to be backed up daily and stored in secure off-site locations, while less sensitive data may only need to be backed up weekly.
  5. Compliance: Data classification is also used to ensure compliance with data protection regulations such as the General Data Protection Regulation (GDPR), the Health Insurance Portability and Accountability Act (HIPAA), or the Payment Card Industry Data Security Standard (PCI DSS). These regulations often require organizations to implement specific security measures for protecting sensitive data, and data classification is the first step in determining which data falls into this category.

How does Dig use Data classification?

Dig Security helps organizations discover, classify, protect, and govern their cloud data. Dig performs automatic data classification to categorize and label sensitive information based on its level of importance and confidentiality. This helps organizations to identify and secure sensitive data and to ensure that it has the proper controls and is only accessible by authorized personnel.

Data classification is typically performed using a combination of manual and automated methods. Dig Security uses advanced algorithms and machine learning techniques to classify data automatically based on its content and context. This helps to quickly and accurately identify sensitive information, such as personally identifiable information (PII), financial data, and confidential business information.

Once the data is classified, organizations can apply appropriate security measures to protect it, such as encryption, access controls, and data loss prevention (DLP) policies. Dig also provides monitoring and threat detection services to ensure that sensitive information remains secure.

The use of data classification is critical for organizations that handle sensitive information, as it helps to mitigate the risk of data breaches and protect against cyber threats. Using advanced technologies and expert knowledge, Dig Security helps organizations secure their data and meet regulatory compliance requirements.