A Definition of Data Classification
Data classification is broadly defined as the process of organizing data by relevant categories so that it may be used and protected more efficiently. The classification process not only makes data easier to locate and retrieve – data classification is of particular importance when it comes to risk management, compliance, and data security.
Data classification involves tagging data, which makes it easily searchable and trackable. It also eliminates multiple duplications of data, which can reduce storage and backup costs, as well as speed up the search process.
Reasons for Data Classification
Data classification is carried out for a variety of purposes, one of the most common being a process that supports data security initiatives. But data may be classified for a number of reasons, including ease of access, to comply with regulatory requirements, and to meet various other business or personal objectives. In some cases, data classification is a regulatory requirement, as data must be searchable and retrievable within specified timeframes. For the purposes of data security, data classification is a useful tactic that facilitates proper security responses based on the type of data being retrieved, transmitted, or copied.
Types of Data Classification
Data classification often involves a multitude of tags and labels, defining the type of data, confidentiality, and its integrity. Availability is also sometimes considered in data classification processes.
Data’s level of sensitivity is often classified based on varying levels of importance or confidentiality, which correlates to the security measures in place to protect each classification level. For example, an organization may classify data as:
In this instance, public data represents the least-sensitive data with the lowest security requirements, while restricted data is in the highest security classification and represents the most sensitive data. This type of data classification is often the starting point for many enterprises, followed by additional identification and tagging procedures that label data based on its relevance to the enterprise, quality, and other classifications.
The Data Classification Process
Data classification can be a complex and cumbersome process, unless automated systems are used to streamline the process. Still, an enterprise must determine the categories and criteria that will be used to classify data, understand and define its objectives, outline the roles and responsibilities of employees in maintaining proper data classification protocols, and implement security standards that correspond with data categories and tags. When done correctly, this process will provide employees and third parties involved in the storage, transmission, or retrieval of data with a framework within which to operate.
Policies and procedures should be well-defined, considerate of the security requirements (or confidentiality) of data types, and straightforward enough that policies are easily interpreted by employees to promote compliance. For instance, each category should include information about the types of data classified as such, security considerations with rules for retrieving, transmitting, and storing data, clear examples, and potential risks associated with a breach of security policies.
The data classification process goes far beyond making information easy to find. Data classification is necessary to enable modern enterprises to make sense of the vast amounts of data available at any given moment. Data classification provides a clear picture of the data within the organization’s control and an understanding of where data is stored, how it’s most easily accessed, and how data is best protected from potential security risks. Data classification, once implemented, provides an organized information framework that facilitates more adequate data protection measures and promotes employee compliance with security policies.
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