Perquisites of Good Classification of Data

Data classification has improved significantly over time. Today, the technology is used for a variety of purposes, often in support of data security initiatives. But data may be classified for a number of reasons, including ease of access, maintaining regulatory compliance, 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.

Sensitive data is a general term representing data restricted to use by specific people or groups. Sensitive and confidential data are often used interchangeably. Examples of sensitive data include intellectual property and trade secrets.

Data reclassification is re-categorization of data to apply appropriate updates, for example, based on changes to legal or contractual obligations, data usage or value, or new or revised regulatory mandates.

Data tagging or labeling adds metadata to files indicating the classification results.

There are three main types of data classification that are considered industry standards:

• Content-based classification inspects and interprets files looking for sensitive information
• Context-based classification looks at application, location, or creator among other variables as indirect indicators of sensitive information
• User-based classification depends on a manual, end-user selection of each document. User-based classification relies on user knowledge and discretion at creation, edit, review, or dissemination to flag sensitive documents.

Purpose of Data Classification

• Informs risk management, legal discovery and regulatory compliance processes
• Helps prioritize security measures
• Improves user productivity and decision-making by streamlining search and e-discovery
• Reduces data maintenance and storage costs by identifying duplicate and stale data
• Helps IT teams justify requests for investments in data security