Data Classification Identifies Personal and Sensitive Data for Privacy
Data classification is the process of sorting and labeling data for purposes of data privacy and data management. The data classification process provides intelligence on data location, sensitivity, geography, or other characteristics. With this intelligence, organizations can better manage and secure data to meet privacy, governance, business, and security objectives.
Data can be classified across multiple variables. For example, a piece of data could be classified as company confidential, high business value and medium-term retention; the data classifications providing value to the organization’s utilization, management, and governance of that data.
How do companies implement data classification?
- Via policies– the organization specifies the use and restrictions of data by policy.
- Through automation–data privacy automation software will discover and analyze data sets to determine the appropriate classification based on multiple variables and rules. AI and ML capabilities can enhance discovery, enabling the detection of potentially unknown personal and sensitive data that organizations have not officially defined or cataloged.
- User driven– where data owners help define data classification directly and/or verify the results from automated solutions.
How is data classification used within the context of data privacy?
- Determine what regulations apply to which data.
- Determine the sensitivity of the data for the purpose of being precise in the application of data and cybersecurity controls.
- Identify elements of personal data needed to support data subject rights and consent requests.
- Support internal and external audits of personal information.
- Multi-label classification allows organizations to pinpoint data sets where several data elements could be combined to allow identification of anonymized data. This is referred to as toxic combinations of data.
Here are additional resources:
- SecurityIntelligence, “You Don’t Know What You Don’t Know: 5 Best Practices for Data Discovery and Classification”
- Carnegie Mellon Institute, “Guidelines for Data Classification”
You just learned about Data Classification, now explore related terms like Personal Data, Data Privacy, Data Mapping, Data Flow Diagrams, and Data Lineage.