The FREE Cybersecurity Glossary by Thor Pedersen!

Data in Use: Refers to data that is currently being processed or manipulated by a computer application or user. Unlike data at rest or data in transit, it's in an active state, making it potentially more vulnerable to unauthorized access or attacks, like memory scraping. Security measures for data in use include encryption and access controls. Encrypting data in active use, also known as runtime encryption, involves protecting data being processed in a computer's memory. Techniques include Trusted Execution Environments (TEEs) that create secure areas in a processor, Homomorphic Encryption that allows computations on encrypted data, and Secure Enclaves like Intel SGX, which safeguard data even if the system is compromised.

Data Labeling: The process of categorizing or tagging data with labels that provide informative context or meaning, facilitating proper handling and protection according to the specified labels.

Data Lake: A centralized repository that allows you to store all your structured and unstructured data at any scale. You can store your data as-is without having to first structure the data and run different types of analytics—from dashboards and visualizations to big data processing, real-time analytics, and machine learning to guide better decisions. Data lakes are typically implemented using a flat architecture where data is tagged with metadata and unique identifiers and can be efficiently queried and analyzed.

Data Leak: An incident where sensitive, protected, or confidential data is unintentionally exposed, transmitted, or made accessible to unauthorized individuals. This could be the result of various actions, such as poor security configurations, vulnerabilities in software, or human error, and can lead to significant reputational and financial damage.

Data Leakage: An event where information, either intentionally or accidentally, is moved outside of an organization's security boundaries, possibly falling into the wrong hands. Data leakage could occur through various channels, such as email attachments, cloud storage, physical devices, or even through employee misconduct.

Data Lifecycle: The sequence of stages that a particular unit of data goes through from its initial generation or capture to its eventual archival and retirement or deletion. This lifecycle typically includes stages such as creation, processing, storage, usage, sharing, archiving, and disposal, with each stage necessitating specific handling and security measures.