The FREE Cybersecurity Glossary by Thor Pedersen!

Database Replication: A technique that involves maintaining copies of the same database on multiple computer systems. This is done not only to ensure data availability and durability but also as a measure of resilience in the face of technical failures or data breaches. If one server or database is compromised, operations can continue unaffected using the replicated databases, minimizing disruption and data loss.

Database System Vulnerabilities: The weaknesses in a database system that can be exploited by malicious actors to gain unauthorized access, disrupt operations, or manipulate data. Such vulnerabilities could stem from a variety of factors, including but not limited to software bugs, improper configurations, weak security controls, or lack of timely updates and patches. Identifying and mitigating these vulnerabilities is crucial in protecting the data held within the database system.

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.

DeCSS Program: A computer program designed to circumvent the Content Scramble System (CSS) used to encrypt and protect DVD content from unauthorized duplication and viewing. DeCSS program enabled users to bypass CSS and access the DVD content, leading to significant legal and ethical discussions about digital rights management and copyright infringement.

Diffie-Hellman (DH or D-H): A cryptographic protocol enabling secure key exchange over a public channel, allowing two parties to establish a shared secret key without directly sharing private information. It is foundational for secure communications, underpinning many encryption and secure communication technologies.

DSaaS (Data Science as a Service): A cloud-based service that offers data science capabilities, including analytics, machine learning, and big data processing, without the need for organizations to maintain their own data science infrastructure. DSaaS enables access to advanced tools, algorithms, and expertise, facilitating data-driven decision-making and insights.