We thought that it might be helpful to post an Identity Analytics Glossary to explain some of the jargon and acronyms we use throughout this website and indeed in this area of business.

We trust you will find it helpful:

 

Artificial Intelligence

(acronym – AI)

The general field of computing whereby systems have the ability to perform tasks that would normally need the input of human intelligence. Now widely used in the fields of visual and voice recognition, language (translation) and some more advanced decision making (fuzzy logic).

Attribute Based Access Control

(acronym – ABAC)

This is an approach to Identity Access Management (IAM) that uses rules based on the attributes of the individual or system to allow access to (usually) IT resources. ABAC aids IAM by comparing a request with the environment in which that request is made, resulting in a logical recommendation or action which is often based on potential increase in risk and assumes least privilege.

Data Mining

The act of processing databases or other sources of information in order to produce new information and insight. Data Mining can incorporate advanced analytics to produce very precise and specific results from multiple sources of extremely large and unstructured datasets.

Identity and Access Management

(acronym – IAM)

This is the term for the raft of policies, systems and technologies whereby organisations monitor, control and ensure correct and proper access to their (usually) IT resources.

Identity Analytics

(Within the operational IT sphere) The process of analysing the access of individuals or systems to an organisation’s (usually) IT resources. Information from such a process can give rise to the creation of risk profiling and role management processes and controls. It is an essential process in Identity Access Management (IAM), governance and control.

Key Risk Indicator

(acronym – KRI)

(With the operational IT sphere) Criterion set to monitor and highlight areas of operational risk within an organisation with the purpose of mitigating potential future loss by spotlighting failing processes and inadequate systems.

Least Privilege

This is the principle whereby any individual (or system) is granted the least amount of access (privileges) to an organisation’s (usually) IT resources in order for them to do their job. This is similar in principle to “need to know” and assumes the default position of “no access” to all resources.

Machine Learning

This is an advanced area in the general field of Artificial Intelligence (AI) whereby a computer system has the ability to learn unaided. The computer system can adapt to new information and data without specific guidance or changes to its original parameters or programming.

Role Based Access Control

(acronym – RBAC)

This is an approach to IAM that uses Roles (often defined or refined through Role Mining) to regulate access to IT resources within an organization. Where the Role, rather than individual request, defines the access given to individuals, Least Privilege is more easily controlled and maintained.

Role Mining

Whereby a system or process maps user access and permissions to resources, usually in order to ensure correct and proper access, modify that access or create a template for appropriate future access to systems and resources for others in a specific role. Roles are usually closely linked to job definition or responsibility within an organisation. Role mining is often performed as a prelude to Role Based Access Control.

Unsupervised Machine Learning

This is highly advanced Machine Learning (cutting edge Artificial Intelligence or AI). It is the process whereby a computer can meaningfully describe and present results and structure from analysing (often very large) unstructured datasets without any predetermined parameters in which to either do this task or assess the values of its output.