Data Classification Levels Explained: Enhance Data Security

Data classification levels have various use cases in businesses and governmental institutions. They define how data should be handled based on sensitivity and importance, allowing for more effective and secure data management.

What Are Data Classification Levels?

Data classification levels are categories used to organize data based on its sensitivity, confidentiality, and potential impact should it be accessed, altered, or destroyed without authorization.

These levels help determine what protective measures should be applied to the data. Here are the common data classification levels:

• Public: This level of data is intended for public use, and its access is not limited. This information can be openly shared and would cause no harm or risk if disclosed.
• Internal: This data is generally used within the organization, and its unauthorized disclosure could lead to minor damage or risk. This may include internal communications, certain HR records, and other non-sensitive operational data.
• Confidential: This level includes data whose unauthorized access could damage the organization. This often includes information like company intellectual property, financial records, strategic planning documents, etc.
• Restricted/Highly Confidential: This is the highest level and includes data that, if disclosed, could lead to severe damage. This typically includes sensitive personal data (like social security numbers, credit card information, etc.), classified information, and other forms of data protected by law or regulation.

How Do You Choose a Classification Level?

Choosing a classification level depends on the nature and sensitivity of the data. Here's how to go about it:

• Identify the nature of the data: The first step in choosing a data classification level is understanding the basic nature of the data you're classifying. Is it personal information? Financial records? Customer data? Public information?
• Assess sensitivity and confidentiality: How sensitive is the data? How severe would the impact be if it were to get into the wrong hands? Confidential data that could harm your organization or individuals - such as financial information, intellectual property, or personally identifiable information (PII) - usually requires higher levels of protection.
• Consider regulatory requirements: If your organization is subject to regulations like GDPR, HIPAA, or PCI DSS, you'll need to classify data in accordance with these rules. For instance, personal health information would require a high classification level under HIPAA.
• Evaluate business needs and value: Does the data have high business value? Is it critical to your operations? If so, you might want to classify it higher to protect it adequately.
• Understand accessibility needs: If the data needs to be widely accessible to employees or the general public, it may be classified at a lower level. However, a higher classification level might be necessary if it should only be accessed by specific individuals.
• Determine the impact of unauthorized access, alteration, or deletion: If the data is sensitive and unauthorized access, modification, or deletion can bring severe consequences, it should be classified higher.

Finally, once you've chosen different data classification levels, you must ensure they're clearly communicated and understood across your organization. Staff training and regular updates to your classification scheme should be an integral part of your data management strategy.

Examples of Data Classification Levels

Data classification levels vary depending on the organization and the sensitivity of the data. Here are some general examples of data classification levels:

• Public data—This information is openly available to everyone, and sharing, using, or redistributing it does not have any negative impacts. Examples include marketing materials and press releases.
• Internal data - This refers to information only available to individuals inside the organization. Internal memos, internal project reports, and employee directories are examples.
• Confidential data - This is sensitive information available only to selective individuals inside an organization. This can include financial information, customer lists, or strategic plans. Unauthorized access can lead to legal penalties or damage to the organization's reputation.
• Highly Confidential / Restricted Data - This classification level is for sensitive information. It often includes personal identification (PII) such as credit card information, social security numbers, medical records, etc. Unauthorized access can lead to severe legal consequences and significant reputation damage.
• Secret or Top Secret data - This classification is typically used in governmental or military settings. It refers to data whose unauthorized disclosure could cause serious or grave harm to national security. This category could include plans for military operations, details about nuclear weaponry, or surveillance data about foreign governments.

When Do You Need To Reclassify Data?

Data reclassification is necessary under several circumstances, such as:

• Changes in Legal and Regulatory Requirements: If new laws or regulations, such as GDPR or CCPA, affect the nature and sensitivity of the data, you may need to reclassify it.
• Changes in Data Use or Context: If data is used differently or its context changes, it might require reclassification. For instance, data previously classified as internal use may need to be reclassified as confidential if it begins to contain more sensitive details.
• Change in Data Value or Sensitivity: Over time, certain data may lose relevance or increase sensitivity. For instance, data about a forthcoming product release might be highly sensitive before the launch and downgraded afterward.
• Audit Results: Audits may highlight misclassifications or outdated classifications, prompting a reassessment and possible reclassification.
• New Data or Updated Information: Whenever new data is acquired, created, or existing data is significantly updated, it may need to be classified or reclassified.
• Changes in Business Operations or Strategy: When an organization makes significant changes to its operations or strategy, it may affect the sensitivity or relevance of certain data types.

Continually reviewing and revising your data classification schema and categories is a best practice for maintaining data security and integrity.

Why Do You Need to Have Data Classification Levels?

Data classification levels are fundamental to data protection strategy. They add order, streamline operations, and enhance an organization's overall data governance framework.

Data classification levels are necessary for several reasons, including:

• Improved Data Security: When data is divided into sensitivity levels, organizations can easily identify and better protect confidential information, reducing the chance of data breaches.
• Regulatory Compliance: Various regulations, such as GDPR, HIPAA, and PCI-DSS, mandate businesses to protect certain data types. Data classification levels can ensure the right data is guarded in line with legal requirements, preventing potentially hefty fines for non-compliance.
• Efficient Data Management: Classification allows for better data organization, making it easier to locate and retrieve information when needed. This can streamline tasks and facilitate decision-making within a business.
• Resource Allocation: A business can effectively assign resources where they're most needed by classifying data. Rather than protecting all data equally, resources can be focused on safeguarding the most sensitive information.
• Risk Management: Classification helps identify the potential impact of a data breach. This can inform risk assessments and continuity planning, assisting organizations to prepare for worst-case scenarios.
• Access Control: Data classification levels can guide who can access specific information within a business, ensuring only authorized personnel can access sensitive data.
• Data Life Cycle Management: Classification can play a key role in data life cycle management, helping frame policies around data retention, archiving, and destruction based on its importance and sensitivity.

Data Classification Level Use Cases

Here are some examples of data classification use cases:

• Risk Management: By classifying data according to its sensitivity, businesses can identify and protect their most valuable data assets. This helps in risk assessment and the implementation of appropriate security measures.
• Compliance: Many industries have specific regulations about handling certain data types. For example, in healthcare, patient data falls under HIPAA regulations, which require particular protections. Classification levels can help ensure data is handled according to these regulations.
• Access Control: Highly confidential information should be accessible only by a select group of company employees. Different classification levels make access controls more easily and efficiently set.
• Data Breach Prevention and Response: If a breach were to occur, knowing the classification of the exposed data could help the security team prioritize their response. Sensitive data would require an immediate and forceful response, while less critical data could be addressed with less urgency.
• IT and Security Resource Allocation: Knowing data classification helps organizations allocate their IT and security resources more effectively. Resources can be focused on protecting the most vital or vulnerable data.
• Data Lifecycle Management: Classifying data can help manage it throughout its lifecycle—from collection, use, and storage to eventual disposal. For instance, data labeled 'obsolete' can be safely de-identified or deleted.
• Data Governance: Classification levels can be central to an organization's data governance strategy. Organizations can ensure that all data management activities align with their broader business objectives and regulatory requirements by classifying data.

What Are Data Classification Levels Best Practices?

• Set Clear Definitions: Establish clear definitions for different levels of data classification based on the sensitivity and importance of the data. Most organizations use three to five levels (Public, Internal, Confidential, and Restricted).
• Adopt a Standardized Approach: To avoid inconsistencies and confusion, use a standard data classification scheme across the organization.
• Training & Awareness: Regularly educate and train your employees about the importance of data classification, the different levels of classification, and policies related to handling each level of data.
• Automate the Process: Utilize automatic classification tools to reduce human error and improve efficiency. However, remember that while these tools can assist tremendously, they should not entirely replace human judgment.
• Data Ownership: Assign data ownership to specific individuals or teams within the organization. These data owners will be responsible for the classification, safe handling, and security of their own data.
• Integrate with Data Security Policies: Data classification should form the basis for your data security policies and controls. Security measures such as access controls, encryption, etc., should be applied based on the classification level of the data.
• Regulatory Compliance: Your data classification scheme should consider any regulatory requirements your organization needs to comply with, such as GDPR, HIPAA, or CCPA.
• Data Labeling and Metadata: Labels should be applied to data based on their classification. This can include visual labels and metadata tags to help ensure that data is handled correctly.
• Testing and Auditing: Regular audits should be carried out to ensure data is classified correctly and the data classification policy is being adhered to as intended.

Discover How Fortra’s Data Classification Can Help Your Organization

Data Classification enables organizations to safeguard sensitive information, comply with legal requirements, and improve data management.

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