Data Classification Methods: Ensuring Security and Compliance

Data classification methods are techniques used to organize and categorize data into various distinct classes or groups. These methods are essential for numerous applications, such as data analysis and machine learning, as well as data security, data management, and regulatory compliance.

What Is Data Classification?

Data classification is the process of organizing and categorizing data into different groups and types based on its content and structure. This helps in managing and protecting data more effectively, adhering to policies and regulations, enhancing security, and supporting data analysis and other business operations.

Why Data Classification Matters

The goal of data classification is to ensure that data is appropriately secured, data compliance is maintained, and data management is effective. The exact process may vary based on each organization's specific needs and considerations.

Data classification is critical for several reasons:

• Facilitates Data Security: By identifying which data is sensitive or confidential, organizations can apply appropriate security measures to safeguard that data.
• Regulatory Compliance: Many industries are required by law to uphold particular standards regarding the storage, processing, and transmission of certain types of data. Misclassifying data can result in non-compliance and hefty fines.
• Risk Management: Knowing what data you have and how it's classified can help manage and decrease the potential risk associated with a data breach.
• Facilitates Data Privacy: By properly classifying data, organizations can better prevent unauthorized access to sensitive information and protect individuals' privacy.
• Informed Decision Making: When data is correctly classified, organizations can use it more effectively for decision-making, analytics, and creating business strategies.
• Incident Response: Proper data classification can aid in incident response by identifying potential data compromise in the event of a breach.
• Cost Management: Effective data classification can also lead to significant cost savings, such as storing less sensitive data on cheaper, low-security storage.

How does Data Classification Work?

Data classification organizes data into categories based on different attributes, such as sensitivity, regulatory requirements, values, and more. Here's a detailed explanation of how data classification works.

• Define Classification Criteria: The first step is to define what data will be classified and the criteria for its classification. This could include the type of data, its sensitivity, or the department it originates from, among other things.
• Generate Labels or Tags: After the criteria are defined, labels or tags need to be generated corresponding to the classification. Labels could be "Confidential," "Public", "Internal," and so on.
• Classification of Data: The data is then classified based on the defined criteria and labels. This could be done manually, where a data owner assigns labels to the data sets, or automatically, where software or algorithms are used to classify data.
• Implement security measures: Depending on the classification, the appropriate security measures are then applied to the data. For instance, data classified as confidential might be encrypted, with access limited to only certain individuals.
• Audit and Adjust: Regular auditing should be conducted to ensure the classifications are accurate and security controls are working as intended. Adjustments should be made as necessary, especially when new types of data are introduced or when the sensitivity of data changes.
• Train and Educate Staff: Raising staff awareness about the importance of data classification and training them to handle different classes of data is also vital to implementing data classification.
• Monitor and Maintain: The classification scheme needs ongoing monitoring and maintenance to ensure its effectiveness, particularly as the organization's needs and the data landscape evolve.

Types of Data Classification

Data classification can be grouped into three main types: Content-Based, Context-Based, and User-Based.

• Content-Based Classification: This method involves reviewing the content within files or documents and tagging it with a classification level based on the data elements it contains. For example, a document may be classified as "Confidential" if it contains sensitive data like credit card numbers or personally identifiable information (PII).
• Context-Based Classification: This approach classifies data based on the elements surrounding the data, also known as metadata. This may include the application that created the data, where the data resides, when the data was created, who owns the data, etc. For example, classifying emails based on subject lines, sender, and recipient addresses.
• User-Based Classification: As the name suggests, this type of classification relies on the user who is creating or handling the data to classify the data manually. This classification is dependent on the user's knowledge and discretion. For example, a document creator classifies a file as "Internal" based on the information it contains.

What are the Data Classification Levels?

Data classification levels are typically divided into four categories: Public, Internal, Confidential, and Restricted.

• Public: This is data intended for public use. It includes marketing materials, company websites, and other public documents that pose no risk to a business or individuals if accessed, mishandled, or disclosed.
• Internal: This category involves data that is meant to stay within the organization but is not confidential. Examples would be internal newsletters, email messages, and internal company policies. Loss of internal data may cause minor inconveniences, but it's not harmful in most cases.
• Confidential: This is sensitive data that, if disclosed, could have serious implications for the organization. It includes trade secrets, intellectual property, or certain types of employee or customer data. Access to confidential data is usually restricted to specific individuals.
• Restricted: This category includes the most sensitive data that has legal obligations for its protection. For example, credit card numbers, Personally Identifiable Information (PII), and Protected Health Information (PHI) are all classified as Restricted. Unauthorized disclosure of this data could lead to severe legal and financial repercussions.

What Are the Data Sensitivity Levels?

Data sensitivity levels refer to the degree of risk that could result if the data were compromised. There are typically three levels: high, medium, and low.

• High Sensitivity Data: This includes data whose unauthorized disclosure could have serious adverse effects on an organization or individuals. Examples may include classified information, trade secrets, personally identifiable information (PII), credit card data, and health records.
• Medium Sensitivity Data: This data is less sensitive but could still have significant impact if disclosed or altered. Examples include internal communication, non-confidential business documents, and personal emails.
• Low-Sensitivity Data: This includes information that can be made public without any risk to the organization or individuals. It might consist of already publicly available data or non-sensitive and non-confidential data such as a company's publicly available reports, public website content, and press releases.

Data Classification Methods

Data can be classified using various methods, primarily:

• Manual Classification: Here, the data owner or an authorized user manually assigns the data a label indicating its classification. This method can be accurate, but it is labor-intensive and time-consuming.
• Automated Classification: With automated classification, software technologies analyze data and assign classification based on predetermined rules and policies. This method is faster and less labor-intensive but may not be as precise with complex data sets.
• User-driven or User-assisted Classification: This is a combination of manual and automated classification. Users assign labels to a subset of data, and machine learning algorithms extrapolate from this to classify the rest of the data. This method strikes a balance between precision and efficiency.
• Content-Based Classification: This method classifies data based on the content within the data object. For example, a document containing sensitive credit card information would be classified as confidential.
• Context-Based Classification: This method classifies data based on factors surrounding the data, such as the creator, time of creation, the application used, or the location of the data.
• Machine Learning Classification: In this method, algorithms are trained to recognize patterns and characteristics, which are used to classify new or existing data. This method can handle large volumes of data and improve accuracy over time.

Common Data Classification Standards and Requirements

There are several common data classification standards and requirements that organizations typically follow, which often depend on the industry they're in and the type of data they handle:

• General Data Protection Regulation (GDPR): This is the European Union's regulation for data protection and privacy. It establishes stringent guidelines for data collection, storage, and processing for EU residents and requires categorizing personal data based on sensitivity and associated risk.
• Payment Card Industry Data Security Standard (PCI-DSS): This standard applies to any organization that processes credit card transactions. It stipulates ways to secure cardholder data during and after a financial transaction. It requires organizations to protect and monitor cardholder data.
• Health Insurance Portability and Accountability Act (HIPAA): This act applies to health care providers, health insurance companies, and businesses that process and store medical records. It requires the protection of personally identifiable information and other medical-related data.
• California Consumer Privacy Act (CCPA) and Virginia Consumer Data Protection Act (VCDPA): These are state-level laws and regulations that enforce consumers' data privacy rights, requiring businesses that handle personal data of state residents to follow privacy procedures and protections.
• ISO/IEC 27001: This international standard specifies the requirements for an information security management system (ISMS) and includes stipulations for dealing with sensitive data.
• Sarbanes-Oxley Act (SOX): This law imposes strict requirements for companies listed in the U.S. to ensure they manage financial and accounting data accurately.

The Benefits of Data Classification

Data classification offers a myriad of benefits, including:

• Improved Data Security: By identifying and classifying sensitive data, organizations can implement appropriate security controls, such as encryption and access control, to prevent unauthorized access and protect against data breaches.
• Regulatory Compliance: Data classification helps organizations comply with various data protection regulations and laws such as GDPR, HIPAA, and CCPA by ensuring the right protections are in place for sensitive and personal data.
• Enhanced Search and Retrieval: Organized data can be retrieved more efficiently, making it easier to locate specific information and enabling faster decision-making.
• Increased Employee Awareness: It promotes an understanding of the value and sensitivity of data amongst employees, fostering good data handling practices.
• Cost Savings: Deleting unneeded data can help avoid unnecessary costs from storage, data breaches, and non-compliance penalties.
• Facilitates Data Lifecycle Management: It improves understanding of how data should be handled throughout its lifecycle - from creation and storage to distribution and deletion.
• Enhanced Data Quality: Classification ensures that data is updated, relevant, and accurate, leading to improved data quality over time.

Adopt Fortra’s Data Classification Methods

One of the primary objectives of data classification is to ensure that sensitive data is given the highest level of protection and that less important data does not consume too many resources.

Fortra understands how data classification is crucial to devising an effective data security strategy and better-managing cybersecurity risks.

Request a demo today to see Fortra’s data classification acumen in action.