Certificate of Cloud Security Knowledge (CCSK) Practice Test 2025 - Free CCSK Practice Questions and Study Guide

The concept of implementing automated data discovery and labeling to control data management across virtual machines is fundamentally aligned with Data Loss Prevention (DLP). DLP refers to a strategy that aims to ensure sensitive data is not lost, misused, or accessed by unauthorized users. By conducting automated data discovery, organizations can identify where their sensitive information resides within virtual environments, and applying labeling helps in tracking and applying appropriate protection measures to that data.

DLP encompasses finding and managing critical information, ensuring compliance with regulations, and preventing data breaches. Automated processes allow for timely responses to data management needs, reinforcing security postures effectively.

Data integrity verification, data encryption management, and data access control serve different purposes and do not primarily focus on the automated discovery and labeling aspect that is core to DLP. While data integrity verification is about ensuring data remains accurate and trustworthy, and data encryption management focuses on protecting data through encryption techniques, data access control centers on managing who has the permissions to access data. None of these other options directly address the automated processes associated with discovering and labeling data for protective measures in a virtual machine context.