Data Classification Models

Key Concepts

• Data Sensitivity Levels
• Data Classification Categories
• Data Handling Protocols

Data Sensitivity Levels

Data sensitivity levels categorize data based on its importance and the potential impact of its disclosure, alteration, or destruction. Common levels include:

• Public: Data that can be freely shared without any risk. Examples include marketing materials and public announcements.
• Internal: Data that is not intended for public release but is accessible within the organization. Examples include internal memos and employee directories.
• Confidential: Data that requires restricted access to prevent unauthorized disclosure. Examples include customer records and financial reports.
• Restricted: Highly sensitive data that requires the highest level of protection. Examples include intellectual property and classified government information.

Data Classification Categories

Data classification categories help in organizing data based on its type and sensitivity. Common categories include:

• Personal Data: Information that can identify an individual. Examples include names, addresses, and social security numbers.
• Financial Data: Information related to financial transactions and accounts. Examples include bank statements and credit card numbers.
• Health Data: Information related to an individual's health status. Examples include medical records and insurance claims.
• Intellectual Property: Information that is the result of creativity and innovation. Examples include patents, trade secrets, and copyrighted material.

Data Handling Protocols

Data handling protocols define the procedures for managing data based on its classification. These protocols ensure that data is protected throughout its lifecycle. Key protocols include:

• Access Control: Restricting access to data based on user roles and responsibilities. For example, only authorized personnel should have access to confidential financial data.
• Encryption: Converting data into a secure format to prevent unauthorized access. For example, sensitive emails can be encrypted to ensure they are only readable by intended recipients.
• Data Backup: Regularly creating copies of data to prevent loss in case of a breach or system failure. For example, critical business data should be backed up daily.
• Data Retention: Defining how long data should be kept before it is archived or destroyed. For example, financial records may need to be retained for several years for auditing purposes.

Examples and Analogies

Consider a hospital's data management system. Patient health data is classified as highly sensitive (Restricted) and must be protected with strict access controls and encryption. Financial data, such as billing information, is classified as Confidential and requires regular backups and secure storage. Internal policies, like staff schedules, are classified as Internal and are accessible to hospital employees but not the public. Public information, such as hospital announcements, is classified as Public and can be shared freely.

By understanding and implementing these data classification models, organizations can effectively manage and protect their data, ensuring compliance and minimizing risks.