Glossary

Tokenization entails the substitution of sensitive data with a non-sensitive equivalent, known as a token. This token then maps back to the original sensitive data through a tokenization system that makes tokens practically impossible to reverse without them. Many such systems leverage random numbers to produce secure tokens. Tokenization is often used to secure financial records, bank accounts, medical records and many other forms of personally identifiable information (PII).

Unmanaged data stores are deployments that must be completely supported by development or infrastructure teams, without the assistance of the cloud service provider. This additonal logistical burden may be undertaken by teams aiming to comply with data sovereignty requirements, abide by private network or firewall requirements for security purposes, or resource requirements beyond the provider's (database as a service) DBaaS size or IOPS

Data lacking a pre-defined model of organization or that does not follow one. Such data is often text-heavy, but can also include facts, figures and time and date information. The resulting irregularities and ambiguities make unstructured data much harder for programs to understand than data stored in databases with fields or documents with annotations. Many estimates claim unstructured data comprises the vast majority of global data, and that this category of data is growing rapidly.

A vulnerability is a weakness that could be exploited or triggered by a threat source in internal controls, procedures for systems security, an information system, or implementation. A weakness is synonymous with deficiency and may result in security or privacy risks or both.