Data classification is the process of organizing data into tiers of information for data organizational purposes.
Data classification is essential to make data easy to find and retrieve so that your organization can optimize risk management, compliance, and legal requirements. Written guidelines are essential in order to define the categories and criteria to classify your organization’s data. It is also important to define the roles and responsibilities of employees in the data organization structure.
When data classification procedures are established, security standards should also be established to address data life-cycle requirements. Classification should be simple so employees can easily comply with the standard.
Examples of data classifications are:
- 1st Classification: Data that is free to share with the public
- 2nd Classification: Internal data not intended for the public
- 3rd Classification: Sensitive internal data that would negatively impact the organization if disclosed
- 4th Classification: Highly sensitive data that could put an organization at risk
Data classification is a complex process, but automated systems can help streamline this process. The enterprise must create the criteria for classification, outline the roles and responsibilities of employees to maintain the protocols, and implement proper security standards. Properly executed, data classification will provide a framework for the storage, transmission and retrieval of data.
Automation simplifies data classification by enabling you to dynamically set different filters and classification criteria when viewing data across your storage. For instance, if you wanted to classify all data belonging to users who are no longer at the company as “zombie data,” the Komprise solution will aggregate files that fit into the zombie data criterion to help you quickly classify your data.