Exfiltration via Screen Sharing Software

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.

Domain Name System (DNS) filtering allows the blocking of domain resolution for specific domains or automatically categorized classes of domains (depending on the functionality of the software or appliance being used). DNS filtering prevents users from accessing blocked domains, regardless of the IP address the domains resolve to.

Examples of automatically categorized classes of domains are ‘gambling’ or ‘social networking’ domains. Automatic categorizations of domains are typically conducted by the software or appliance being used, whereas specific domains can be blocked manually. Most DNS filtering software or appliances will provide the ability to use Regular Expressions (RegEx) to (for example) also filter all subdomains on a specified domain.

DNS filtering can be applied on an individual host, such as with the hosts file, or for multiple hosts via a DNS server or firewall.

A Data Classification Policy establishes a standard for handling data by setting out criteria for how data should be classified and subsequently managed and secured. A classification can be applied to data in such a way that the classification is recorded in the body of the data (such as a footer in a text document) and/or within the metadata of a file.

An Acceptable Use Policy (AUP) is a set of rules outlining acceptable and unacceptable uses of an organization's computer systems and network resources. It acts as a deterrent to prevent employees from conducting illegitimate activities by clearly defining expectations, reinforcing legal and ethical standards, establishing accountability, specifying consequences for violations, and promoting education and awareness about security risks.

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 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.

In modern versions of the Windows operating system, the prefetch feature serves an important function in speeding up the run time of applications. It does this by creating a cache of information on an application on its first run that is is stored for later reference in c:\windows\prefetch, these files are created with the extension .pf and have the following format <EXECUTABLE>-<HASH>.pf.

These created files contain the created and modified timestamps of the respective file, the file size, process path, how many times it has been run, the last time it was run, and resources it references in the first 10 seconds of execution.

Since every executable that is run will have a prefetch file created when the feature is enabled, the prefetch directory and the contents within it can offer new and valuable insights during an investigation, particularly when the original executable no longer exists.