How do you classify data in your organization? Conducting a data risk assessment and keeping compliance regulations top of mind are some of the first steps to protecting an organization’s data.
What Is Data Classification?
Not every piece of organizational data requires top-level security, and to assign the right kind of security to the most confidential data, it’s important to classify it. Data classification is also vital for several compliance standards.
What Are Data Classification Levels?
Data classification levels involve sorting data into categories determined by its sensitivity, value, and importance to the organization.
Each classification level corresponds to a certain degree of access, security, and protection. While the specific names and number of data classification levels vary across organizations, they typically include the following:
- Public Data: This data is intended for public consumption and can be freely distributed. There is no harm done to organizations if this data is disclosed.
- Internal Data: This is normally information used within the enterprise and could cause minimal harm if disclosed, although it is not intended for public consumption. Examples might include company memos, internal procedures, and training materials.
- Confidential Data: This is sensitive data that could cause substantial harm to the organization if inappropriately disclosed. Examples include intellectual property, financial data, strategic plans, and certain personal information.
- Restricted/Sensitive Data: This is the most sensitive data that the organization handles. Unauthorized disclosure could seriously harm the organization and could violate laws and regulations. Examples include credit card information, medical records, and personally identifiable information (PII).
In some systems, there's another classification, known as some variant of "Secret" or "Top Secret." These classifications typically apply to highly sensitive government or military information.
Why Are Data Classification Labels Important?
Data classification labels are important for several reasons:
- Data Protection: Labels help identify the data's sensitivity, which aids in applying proper security controls to protect it. For example, data labeled "confidential" may require higher security measures such as encryption or limited access.
- Compliance with Regulations: Many regulations, such as GDPR or HIPAA, require businesses to know where their sensitive data is and how it's being protected. Classification labels help ensure compliance by easily identifying this data.
- Enhanced Data Management: Labels help manage data better because they offer a clear way to categorize and understand data, making it easier for data to be stored, retrieved, and utilized effectively.
- Risk Management: Classification labels help us to understand the potential risk associated with different data types. For example, a breach of highly confidential data could have severe consequences compared to a breach of public information.
- Cost Savings: By classifying data, organizations can prioritize their resources and invest heavily in protecting extremely sensitive data rather than wasting resources on lower-value data.
- Data Breach Response: If a data breach occurs, classification labels can help organizations identify what type of data was compromised and respond accordingly.
In summary, labels are a core element of a data security strategy. Data classification solutions enforce these labels consistently within all systems and workflows.
What Are the Types of Data Classification Labels?
Data classification labels typically align with how sensitive or confidential the data is and how it should be handled. Here are some common types of data classification labels:
- Public: Data under this label is intended for public access and does not contain any sensitive information. Examples include marketing materials, press releases, and published research.
- Internal: This label is for data that isn't meant for public access but isn't necessarily confidential. It's typically used for non-sensitive company data that are used daily, such as corporate policies, internal newsletters, and project collaboration documents.
- Confidential: This label is given to data that, if disclosed, could potentially harm an individual or the organization, such as business strategies, trade secrets, protected customer data, and research and development information.
- Top Secret/Highly Confidential: This is the highest label assigned to data whose unauthorized disclosure could cause severe damage to the organization. This typically includes highly sensitive information such as financial data, legal documents, personally identifiable information (PII), or protected health information (PHI).
- Restricted: These labels are reserved for the most sensitive data that it could cause severe harm or damage to the organization or individuals if disclosed — including credit card numbers, Social Security numbers, and medical records.
Please note that the specific labels used can vary depending on the organization and industry. For example, a government agency might use labels like "classified" and "top secret," whereas a corporation might use labels like "internal" and "confidential."
The Advantages of Data Classification Labels
- Enhanced Data Security: Data classification labels improve security by clarifying which information requires more security measures.
- Compliance: Many industries and laws require organizations to maintain certain security levels for different types of information. Classification labels help organizations maintain compliance by clearly identifying what type of data is under what classification.
- Better Data Management: Classification labels allow organizations to manage their data better. For instance, organizations can efficiently locate and access critical information when it is correctly labeled.
- Efficient Resource Allocation: By knowing which data is most critical or sensitive, organizations can allocate their resources more efficiently, focusing on protecting the most important assets.
- Informed Decision Making: When data is properly labeled, it becomes easier for decision-makers in the organization to understand the types of data they are working with, leading to more informed decisions.
- Minimize Data Breaches: Classification labels can reduce the risk of data breaches as they provide a system whereby sensitive data is easily identified and therefore can be better protected.
- Streamline Data Retrieval: Classification labels can help streamline data retrieval. Employees can easily identify and access the needed data without wasting time sorting through irrelevant information.
- Effective Response to Data Breach: In case of a security breach, labeled data simplifies the response process, making it easier to identify and report what type of data was compromised.
- Risk Management: It allows organizations to better manage their data-related risks by identifying the sensitivity of their data.
- Cost Reduction: By classifying data, companies can prioritize their protection efforts on the most sensitive data, reducing the costs related to over-protecting less sensitive data.
Data Classification Labels Best Practices
- Define and Standardize Categories: Establish clear and distinct categories for data classification. These categories need to be understood universally within the organization. Typically, categories may include public, internal, confidential, and highly confidential.
- Use Clear Labeling: The labels for data classification should be clear and easy to understand. Using a color-coding system (red for sensitive, yellow for moderate, and green for low risk) can make the labels quickly identifiable.
- Train Employees: Ensure that employees are familiar with the data classification labels and understand their meanings. They should know how to properly handle and protect each category of data based on its classification label.
- Automate Where Possible: Consider using automated tools to apply classification labels to data. This can make the process more efficient and ensure that data is consistently labeled according to established criteria.
- Regularly Update Labels: Periodically review and update your data classification labels. As the organization changes and evolves, the data classification labels might need adjustment.
- Implement Label-Based Policies: Establish security policies based on the classification labels. For instance, 'confidential' labeled data might require encryption or additional access controls.
- Enforce Compliance: After labels have been applied, ensure that data handling practices align with the assigned classifications. This could include proper storage, secure sharing protocols, and timely disposal.
- Monitor and Correct Misclassifications: Regularly audit your data classification system to ensure accuracy. Swiftly correct any misclassifications to prevent potential data breaches or non-compliance issues.
- Tailor Labels to Suit Your Business: Customize classification labels to reflect the data's value and sensitivity relative to your specific business needs and regulatory environment.
- Include Metadata: Metadata can provide context to the data, ensuring the label and classification are more accurate. Attributes such as creator, date created, last accessed, and data location can be part of metadata tagging.
How Fortra Assists With Data Classification and Sensitivity Labeling
Data classification labels are critical in ensuring that the right level of security is applied to protect the data based on its sensitivity and importance to the organization.
Fortra understands the intricacies of data management, regulatory compliance, and data classification required to protect your resources. Contact us today to learn how our robust data classification and sensitivity labeling capabilities can help you secure your vital information.
