Anti-Trust or Anti-Competition

Routine reviews of user accounts and their associated privileges and permissions should be conducted to identify overly-permissive accounts, or accounts that are no longer required to be active.

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.

Offering mental health support and conflict resolution programs to
help employees identify and report manipulative behavior in the
workplace

A structured program, including a helpline or other reporting mechanism, designed to assist employees who feel vulnerable, whether due to personal issues, coercion, or extortion. This process allows employees to confidentially raise concerns with trusted teams, such as Human Resources or other qualified professionals. In some cases, it may be appropriate to discreetly share this information with trusted individuals within the Insider Risk Management Program to help prevent and detect insider threats while also providing necessary support to the employee.

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.

A social media policy is a set of rules that governs how employees should use social media platforms in connection with their work. It outlines acceptable and unacceptable behaviors, helps employees understand the consequences of misuse, and serves as a deterrent by promoting accountability, raising awareness of risks, and ensuring consistent enforcement.

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.

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.

Provide a process for all staff members to report concerning and/or suspicious behaviour to the organization's security team for review. An internal whistleblowing process should take into consideration the privacy of the reporter and the subject(s) of the report, with specific regard to safeguarding against reprisals against reporters.

Background checks should be conducted to ensure whether the information provided by the candidate during the interview process is truthful. This could include employment and educational reference checks, and a criminal background check. Background checks can highlight specific risks, such as a potential for extortion.

Various methods can be used within Exchange to prevent internal emails being auto-forwarded to remote domains. This can prevent exfiltration via email auto-forwarding rules.

Regulation Awareness Training equips staff with the knowledge and understanding required to comply with legal, regulatory, and policy obligations relevant to their roles. This includes, but is not limited to, export controls, international sanctions, anti-bribery laws, conflict-of-interest rules, antitrust regulations, and data protection requirements.

The training should be customized according to the specific risks of different roles within the organization, ensuring that employees in high-risk areas—such as legal, procurement, sales, finance, engineering, and senior management—receive in-depth education on how to recognize and avoid behaviors that could lead to regulatory violations. Scenarios that could result in inadvertent or intentional breaches should be addressed, alongside practical advice on how to report concerns and escalate issues.

To accommodate varying learning styles and operational needs, Regulation Awareness Training can be delivered through multiple formats:

• eLearning Modules: For general staff, to provide flexible, scalable training on compliance topics, which can be completed at the employee's convenience.
• Instructor-led Sessions: For higher-risk roles or senior management, where more interactive, in-depth training may be necessary to address complex regulatory requirements and nuanced decision-making.
• Scenario-based Workshops: To reinforce learning with real-world examples and role-playing exercises that help employees internalize regulatory concepts.

By fostering a culture of compliance and accountability, Regulation Awareness Training helps minimize the risk of breaches, whether intentional or accidental, and strengthens the organization’s ability to identify, prevent, and respond to regulatory infringements.

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.

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.

Social Media Monitoring refers to monitoring social media interactions to identify organizational risks, such as employees disclosing confidential information and making statements that could harm the organization (either directly or through an employment association).

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.