Use of a Virtual Machine

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

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.

The process for having software installed on a corporate endpoint by IT should require approval from the employee's line manager to ensure the request is legitimate and appropriate.

The Principle of Least Privilege should be enforced, and period reviews of permissions conducted to ensure that accounts have the minimum level of access required to complete duties as per their role.

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).

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.

This artifact is only generated where both “Clipboard History” and “Clipboard history across your devices” is enabled within the Windows system settings for clipboard.

ActivitiesCache.db is associated with the Windows Timeline feature, which was introduced in Windows 10, allowing users to keep track of their activities across different devices and sessions.

This artifact is located in:

C:\Users\Username\%AppData%\Local\ConnectedDevicesPlatform\<UserProfile>\
 

This .db file can be opened using appropriate software, such as DB Browser for SQLite. The ActivityOperations table is of interest, with the following notable fields:

• StartTime (epoch time) – When the data was first copied to the clipboard 
• ExpirationTime (epoch time) – When the data will be deleted from the ActivitiesCache.db (roughly 12 hours) 
• ClipboardPayload – Base64 encoded string of the clipboard contents  
• Payload – This field tells you where the clipboard data was copied from
• ActivityType – Type 10 means data resides in clipboard, Type 16 shows if data was copied or pasted

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.

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.

Deploy User and Entity Behavior Analytics (UEBA) solutions designed for cloud environments to monitor and analyze the behavior of users, applications, network devices, servers, and other non-human resources. UEBA systems track normal behavior patterns and detect anomalies that could indicate potential insider events. For instance, they can identify when a user or entity is downloading unusually large volumes of data, accessing an excessive number of resources, or engaging in data transfers that deviate from their usual behavior.

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.

LNK files or Shortcut files are stored in the location C:\Users\<user>\AppData\Roaming\Microsoft\Windows\Recent Items and have the “.lnk” file extension.

These files are automatically created when a user account accesses a file through Windows Explorer.

This artifact can provide information as to when a file was accessed, modified, and created, the file path and name, and the file size. .LNK files persist even if the actual file has been deleted, helping to uncover if a file has been accessed then subsequently deleted or moved as it is no longer present in the recorded full file path.