- 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.
- Data Link Layer The second layer of the OSI model handles the transfer of data between adjacent network nodes within a wide area network (WAN) or between nodes on the same local area network (LAN) segment. It encompasses the protocols and methods used to ensure an error-free transmission through physical addressing, data framing, network topology, error detection, and handling retransmissions. The Data Link Layer is subdivided into two sublayers - the Logical Link Control (LLC) layer and the Media Access Control (MAC) layer.
- Data Localization Laws or regulations requiring data about a nation's citizens or residents to be collected, processed, and/or stored inside the country before being transferred internationally, often as a condition for conducting business in a country. These measures are often enforced to protect data privacy, maintain data security, or enable law enforcement access to data.
- Data Loss Prevention (DLP) A set of tools or processes used to identify, monitor, and protect data in use, data at rest, and data in motion through deep content inspection and with a focus on sensitive data. The goal is to prevent sensitive data from being lost, misused, or accessed by unauthorized users, thereby maintaining the data's confidentiality and integrity.
- Data Manipulation Language (DML)A subset of SQL used to retrieve, insert, modify, and delete data in databases. DML includes commands such as SELECT, INSERT, UPDATE, and DELETE, enabling users to manage and manipulate data in relational database management systems.
- Data Mapping The process of creating data element mappings between two distinct data models. Data mapping is used as a first step for a wide variety of data integration tasks, including data transformation or data mediation between a data source and a destination. It helps to define how individual data fields are matched from one database to another, ensuring that information is transferred accurately and correctly during system migrations, data consolidation, or ETL (Extract, Transform, Load) processes. It is fundamental in data management and integration activities.
- Data Mining The analytical process designed to explore large amounts of data in search of consistent patterns and systematic relationships between variables and then validate the findings by applying the detected patterns to new subsets of data. It is a multidisciplinary approach to discovering useful information from large data sets and is used for various purposes, including market analysis, fraud detection, customer retention, and more.
- Data Modeling The process of developing data structures and specifications. It involves creating a conceptual representation of data objects, the associations between different data objects, and the rules governing these associations. This is instrumental in managing data as a resource, promoting data sharing, increasing data consistency, and designing databases.
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