PII Leakage (Personally Identifiable Information)

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 subject may be required to undergo a criminal background check prior to joining the organization, particularly when the role involves access to sensitive systems, data, or physical spaces. This preventative measure is designed to identify any prior criminal conduct that may present a risk to the organization, indicate a potential for malicious behavior, or conflict with legal, regulatory, or internal policy requirements.

Criminal background checks help assess whether a subject's history includes offenses related to fraud, theft, cybercrime, or breaches of trust—each of which may elevate the insider threat risk. Roles with elevated privileges, access to customer data, financial systems, or classified information are often subject to stricter screening protocols to ensure individuals do not pose undue risk to organizational security or compliance obligations.

This control is especially critical in regulated industries or environments handling national security assets, intellectual property, or financial infrastructure. In such settings, background checks may be embedded within broader personnel vetting procedures, such as security clearances or workforce integrity programs.

Where appropriate, periodic re-screening or risk-based follow-up checks—triggered by role changes or concerning behavior—can strengthen an organization’s ability to detect emerging threats over time. When implemented consistently, background checks can serve as both a deterrent and a proactive defense against insider threat activity.

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.

Mandatory security awareness training for employees can help them to recognize a range of cyber attacks that they can play a part in preventing or detecting. This can include topics such as phishing, social engineering, and data classification, amongst others.

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 individual may be required to present and verify valid government-issued identification prior to their association with the organization. This process serves as a foundational identity assurance mechanism, ensuring that the subject is who they claim to be and enabling further vetting procedures to be accurately applied.

Verification of official identification—such as passports, national ID cards, or driver’s licenses—supports compliance with legal, regulatory, and internal requirements related to employment eligibility, right-to-work verification, security clearance eligibility, and access provisioning. It also helps establish a verifiable link between the individual and other background screening measures, including criminal record checks, reference verification, and credential validation.

In the context of insider threat prevention, government-issued ID verification helps prevent identity fraud and the onboarding of individuals using false or stolen identities to gain unauthorized access to sensitive roles, environments, or data. This is particularly critical in sectors handling classified information, critical infrastructure, or financial assets, where subjects may otherwise attempt to obscure prior conduct or affiliations.

Organizations may perform this verification in-house using secure document validation systems or biometric identity matching, or they may rely on trusted third-party identity verification providers offering digital identity assurance services. As part of a multi-layered personnel screening framework, this control helps reduce the risk of malicious insiders gaining a foothold under false pretenses.

Implement a process whereby HR data and observations, including those from managers and colleagues, can be securely communicated in a timely manner to investigators, triggering proactive monitoring of potential insider threats early in their lifecycle. Collaboration between HR teams, managers, colleagues, and investigators is essential for detecting concerning behaviors or changes in an employee's personal circumstances that could indicate an increased risk of insider threat.

Mental Health and Personal Struggles

• Trigger Event: HR receives reports or observes a significant change in an employee's behavior or performance, which may indicate mental health issues or personal struggles that could elevate the likelihood of an insider threat. This information may come from managers, colleagues, or direct observations within HR.
• Indicator: Multiple reports from managers, direct supervisors, or colleagues highlighting behavior changes such as stress, depression, or erratic actions.
• Response: HR teams should notify investigators of high-risk employees with visible signs of distress or any reported instances that might indicate susceptibility to manipulation or exploitation.

Negative Statements or Discontent with the Company

• Trigger Event: HR identifies instances of employees making negative statements about the company, its leadership, or its operations, potentially through personal social media channels, internal communications, or third-party reports. Additionally, such concerns might be raised by managers or colleagues.
• Indicator: Recorded incidents where employees voice dissatisfaction in forums or interactions that may expose vulnerabilities within the company, which may come from colleagues, managers, or HR’s internal channels.
• Response: Immediate referral to investigators for further investigation, including tracking if such sentiments are coupled with any increase in risky behaviors (e.g., accessing sensitive data or systems without authorization).

Excessive Financial Purchases (Potential Embezzlement or Third-Party Influence)

• Trigger Event: HR or finance teams notice discrepancies in an employee's personal financial behavior—particularly excessive spending patterns that appear inconsistent with their known salary or financial profile. This could indicate embezzlement, financial mismanagement, or payments from third parties. Such concerns may also be raised by managers or colleagues.
• Indicator: Transactions that show a high degree of personal spending or financial behavior inconsistent with the employee’s compensation, possibly flagged by HR, finance, or colleagues who notice unusual behaviors.
• Response: Referral to investigators for correlation with employee access to financial or sensitive company systems, along with further scrutiny of potential illicit financial transactions. Third-party or whistleblower reports, including from colleagues or managers, may also be investigated as part of a broader risk assessment.

Hearsay and Indirect Reports

• Trigger Event: Anonymous or informal reports—such as rumors or gossip circulating in the workplace—that hint at potential insider threat behaviors. These reports, often from colleagues or managers, may be unsubstantiated, but they still warrant an alert if the volume or credibility of the information increases.
• Indicator: Reports or concerns raised by employees, colleagues, or external parties suggesting that an employee may be engaging in unusual behaviors, such as excessive contact with external vendors, financial irregularities, or internal dissatisfaction.
• Response: Investigators work with HR to assess the situation by cross-referencing any concerns, including those from colleagues or managers, with the employee's activity patterns, communication, and access to sensitive systems.

Implementation Considerations

• Collaboration Framework: A clear and secure protocol for HR, managers, colleagues, and investigators to share critical information regarding employees at risk. This should maintain employee privacy and legal protections, while still enabling timely alerts.
• Confidentiality and Privacy: All information related to personal behavior, health, or financial matters must be handled with sensitivity and in accordance with legal and regulatory frameworks, such as GDPR or local privacy laws.
• Continuous Monitoring: Once flagged, employees should be monitored for any other risk indicators, including changes in data access patterns, unapproved system access, or behavior that correlates with identified 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.

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.

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.