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Insider Threat Matrix™

  • ID: AF017
  • Created: 30th August 2024
  • Updated: 30th August 2024
  • Platforms: Windows, Linux, MacOS
  • Contributor: The ITM Team

Use of a Virtual Machine

The subject uses a virtual machine (VM) to contain artifacts of forensic value within the virtualized environment, preventing them from being written to the host file system. This strategy helps to obscure evidence and complicate forensic investigations.
 

By running a guest operating system within a VM, the subject can potentially evade detection by security agents installed on the host operating system, as these agents may not have visibility into activities occurring within the VM. This adds an additional layer of complexity to forensic analysis, making it more challenging to detect and attribute malicious activities.

Prevention

ID Name Description
PV015Application Whitelisting

By only allowing pre-approved software to be installed and run on corporate devices, the subject is unable to install software themselves.

Detection

ID Name Description
DT046Agent Capable of Endpoint Detection and Response

An agent capable of Endpoint Detection and Response (EDR) is a software agent installed on organization endpoints (such as laptops and servers) that (at a minimum) records the Operating System, application, and network activity on an endpoint.

 

Typically EDR operates in an agent/server model, where agents automatically send logs to a server, where the server correlates those logs based on a rule set. This rule set is then used to surface potential security-related events, that can then be analyzed.

 

An EDR agent typically also has some form of remote shell capability, where a user of the EDR platform can gain a remote shell session on a target endpoint, for incident response purposes. An EDR agent will typically have the ability to remotely isolate an endpoint, where all network activity is blocked on the target endpoint (other than the network activity required for the EDR platform to operate).

DT045Agent Capable of User Activity Monitoring

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.

DT047Agent Capable of User Behaviour Analytics

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.

DT043Sysmon Process Create Event

This detection is not enabled by default and requires additional configuration.

System Monitor (Sysmon) Event ID 1 is used to record process execution. Reviewing these logs can determine what software has been run on a system.