Leaver

When an employee leaves the organization, a formal process should be followed to ensure all equipment is returned, and any associated accounts or access is revoked.

Training should equip employees to recognize manipulation tactics, such as social engineering and extortion, that are used to coerce actions and behaviors harmful to the individual and/or the organization. The training should also encourage and guide participants on how to safely report any instances of coercion.

An agent capable of User Activity Monitoring (UAM) is a software agent installed on organization endpoints (such as laptops); typically, User Activity Monitoring agents are only deployed on endpoints where a human user Is expected to conduct the activity.

The User Activity Monitoring agent will typically record Operating System, application, and network activity occurring on an endpoint, with a focus on activity that is or can be conducted by a human user. The purpose of this monitoring is to identify undesirable and/or malicious activity being conducted by a human user (in this context, an Insider Threat).

Typical User Activity Monitoring platforms operate in an agent/server model where activity logs are sent to a server for automatic correlation against a rule set. This rule set is used to surface activity that may represent Insider Threat related activity such as capturing screenshots, copying data, compressing files or installing risky software.

Other platforms providing related functionality are frequently referred to as User Behaviour Analytics (UBA) platforms.

An agent capable of User Behaviour Analytics (UBA) is a software agent installed on organizational endpoints (such as laptops). Typically, User Activity Monitoring agents are only deployed on endpoints where a human user is expected to conduct the activity.

The User Behaviour Analytics agent will typically record Operating System, application, and network activity occurring on an endpoint, focusing on activity that is or can be conducted by a human user. Typically, User Behaviour Analytics platforms operate in an agent/server model where activity logs are sent to a server for automatic analysis. In the case of User Behaviour Analytics, this analysis will typically be conducted against a baseline that has previously been established.

A User Behaviour Analytic platform will typically conduct a period of ‘baselining’ when the platform is first installed. This baselining period establishes the normal behavior parameters for an organization’s users, which are used to train a Machine Learning (ML) model. This ML model can then be later used to automatically identify activity that is predicted to be an anomaly, which is hoped to surface user behavior that is undesirable, risky, or malicious.

Other platforms providing related functionality are frequently referred to as User Activity Monitoring (UAM) platforms.

In relevant security tooling (such as a SIEM or EDR), a watchlist (also known as a reference set) should be used to monitor for any activity generated by accounts belonging to employees who have left the organization, as this is unexpected. This can help to ensure that the security team readily detects any unrevoked access or account usage.

This process must be in partnership with the Human Resources team, which should inform the security team when an individual leaves the organization (during an Employee Off-Boarding Process, see PV024), including their full and user account names. Ideally, this process should be automated to prevent any gaps in monitoring between the information being sent and the security team adding the name(s) to the watchlist. All format variations should be considered as individual entries in the watchlist to ensure accounts using different naming conventions will generate alerts, such as john.smith, john smith, john.smith@company.com, and jsmith.

False positives could occur if there is a legitimate reason for interaction with the account(s), such as actions conducted by IT staff.