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Insider Threat Matrix™

  • ID: AF022.004
  • Created: 20th May 2025
  • Updated: 20th May 2025
  • Platforms: MacOS, Windows, Linux,
  • Contributor: The ITM Team

Snapshots and Rollbacks to Remove Evidence

The subject uses virtual machine snapshots, checkpoints, or revert-to-save-state features to erase forensic evidence of activity within a virtualized environment. By taking a snapshot before conducting malicious or high-risk operations, the subject ensures they can later roll the system back—removing all traces of files, commands, logs, and process history created during the session.

 

This technique allows the subject to:

 

  • Create disposable execution environments for malware, exfiltration staging, or credential harvesting.
  • Test or refine malicious payloads without contaminating the final operating state.
  • Erase system logs, shell history, temp files, or volatile indicators without needing individual cleanup.
  • Avoid triggering file integrity monitoring or host-based change detection on the base image.
  • Delay detection by performing actions in a timeline that no longer exists once the rollback is complete.

 

Example Scenarios:

 

  • A subject launches a virtual machine, takes a snapshot, and performs a simulated ransomware attack using internal files. After testing, they roll back to the original snapshot, deleting all evidence of tool execution, encryption activity, and lateral movement.
  • During a data staging operation, the subject collects documents within a VM and compresses them. After extraction, they revert the VM to a pre-staging snapshot, eliminating any trace of the aggregation.
  • An insider uses nested virtualization to test payload delivery across OS versions. Each test is followed by a rollback, leaving no visible trace of the toolsets used or the compromised states created.

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.

PV001No Ready System-Level Mitigation

This section cannot be readily mitigated at a system level with preventive controls since it is based on the abuse of fundamental features of the system.

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.

DT048Data Loss Prevention Solution

A Data Loss Prevention (DLP) solution refers to policies, technologies, and controls that prevent the accidental and/or deliberate loss, misuse, or theft of data by members of an organization. Typically, DLP technology would take the form of a software agent installed on organization endpoints (such as laptops and servers).

 

Typical DLP technology will alert on the potential loss of data, or activity which might indicate the potential for data loss. A DLP technology may also provide automated responses to prevent data loss on a device.

DT101User Behavior Analytics (UBA)

Implement User Behavior Analytics (UBA) tools to continuously monitor and analyze user (human) activities, detecting anomalies that may signal security risks. UBA can track and flag unusual behavior, such as excessive data downloads, accessing a higher-than-usual number of resources, or large-scale transfers inconsistent with a user’s typical patterns. UBA can also provide real-time alerts when users engage in behavior that deviates from established baselines, such as accessing sensitive data during off-hours or from unfamiliar locations. By identifying such anomalies, UBA enhances the detection of insider events.