Service Desk Impersonation for Credential Manipulation

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.

Randomized, routine verification of physical identity credentials is a necessary preventive control in environments where access is gated by visual or badge-based authentication. Unverified presence within secured areas increases organizational tolerance for impersonation, tailgating, and badge misuse—especially where behavioral drift has eroded expectations of enforcement.

Identity challenge programs mitigate this drift by reinforcing that possession of an ID badge is not proof of authorization. When implemented effectively, they also surface expired, misused, or cloned credentials before they enable preparatory actions such as unauthorized access, lateral movement, or physical data collection.

Human-led or Automated challenge mechanisms

Credential Verification Points (CVPs): 

Assign roving or fixed-position security personnel equipped with access control readers capable of validating badge status and presenting the registered photo of the assigned individual. Personnel should challenge any subject whose badge fails to scan or whose appearance does not match the system photo.

Automated Robotic Challenge Systems: 

Deploy robotic guard platforms with integrated badge readers, cameras, and two-way audio connected to a live remote security agent. These systems can autonomously perform credential challenges without requiring direct physical confrontation. They are especially valuable in high-risk or high-traffic areas where human intervention may be inconsistent or prone to social engineering.

Implementation considerations

Separation of Challenge and Enforcement: 

Where feasible, separate the individual performing the challenge from the individual initiating an enforcement action. This reduces risks associated with escalation—such as confrontation with hostile subjects—or familiarity bias from onsite personnel.

Policy Integration: 

Embed the challenge expectation within the Acceptable Use Policy and physical security policy. Clarify that possession of a badge does not exempt any individual from verification.

Audit and Alerting: 

Log all challenge events (successful, failed, bypassed) to a centralized system. Include metadata such as badge ID, photo match result, time, location, and outcome. Flag repeat failures or unverified entries for investigative review.

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.

Privileged Access Management (PAM) is a critical security practice designed to control and monitor access to sensitive systems and data. By managing and securing accounts with elevated privileges, PAM helps reduce the risk of insider threats and unauthorized access to critical infrastructure.

Key Prevention Measures:

Least Privilege Access: PAM enforces the principle of least privilege by ensuring users only have access to the systems and data necessary for their role, limiting opportunities for misuse.

• Just-in-Time (JIT) Access: PAM solutions provide temporary, on-demand access to privileged accounts, ensuring users can only access sensitive environments for a defined period, minimizing exposure.
• Centralized Credential Management: PAM centralizes the management of privileged accounts and credentials, automatically rotating passwords and securely storing sensitive information to prevent unauthorized access.
• Monitoring and Auditing: PAM solutions continuously monitor and log privileged user activities, providing a detailed audit trail for detecting suspicious behavior and ensuring accountability.
• Approval Workflows: PAM incorporates approval processes for accessing privileged accounts, ensuring that elevated access is granted only when justified and authorized by relevant stakeholders.

Benefits:

PAM enhances security by reducing the attack surface, improving compliance with regulatory standards, and enabling greater control over privileged access. It provides robust protection for critical systems by limiting unnecessary exposure to high-level access, facilitating auditing and accountability, and minimizing opportunities for both insider and external threats.

This prevention mandates a standardized, enforceable identity verification process for all service desk interactions involving password resets, account unlocks, or access changes. It requires the use of multi-factor authentication (MFV), out-of-band confirmation (OOB), and structured workflow enforcement to ensure caller legitimacy. The process reduces susceptibility to impersonation, prevents policy bypass under pressure, and ensures auditability for investigative review.

Prevention Measures

All verification must use at least two distinct factors:

• A knowledge factor (e.g., pre-set PIN or a dynamic question).
• A possession factor (e.g., one-time passcode sent to a pre-registered corporate channel such as email or SMS).

For high-risk accounts, apply out-of-band confirmation:

• Perform a callback to a pre-registered phone number.
• Or request confirmation via an internal messaging platform (e.g., Microsoft Teams or Slack).

Verification steps must be enforced within the IT Service Management (ITSM) platform:

• Each verification checkpoint is prompted and must be completed before proceeding.
• Agents cannot override or skip steps manually.
• All actions must be logged automatically with time, actor, and outcome.

Escalation protocols must be followed when:

• Verification fails.
• A request seems inconsistent or suspicious.
• The subject is flagged in HR systems as inactive, offboarded, or under restriction.

Logs must include:

• Requestor’s claimed identity.
• Verifier’s identity.
• Verification methods used.
• Outcome of each step.

Service desk staff must undergo regular training and social engineering simulations:

• Focus on red flags such as urgency, executive name-dropping, or vague justifications.
• Reinforce the principle: verification is mandatory, regardless of pressure.

Establish and maintain formal, well-communicated pathways for personnel to request resources, report deficiencies, or propose operational improvements. By providing structured mechanisms to meet legitimate needs, organizations reduce the likelihood that subjects will bypass policy controls through opportunistic or unauthorized actions.

Implementation Approaches

• Create clear, accessible request processes for technology needs, system enhancements, and operational support requirements.
• Ensure personnel understand how to escalate unmet needs when standard processes are insufficient, including rapid escalation pathways for operational environments.
• Maintain service-level agreements (SLAs) or expected response times to requests, ensuring perceived barriers or delays do not incentivize unofficial action.
• Integrate feedback mechanisms that allow users to suggest improvements or report resource shortfalls anonymously or through designated representatives.
• Publicize successful examples where formal channels resulted in legitimate needs being met, reinforcing the effectiveness and trustworthiness of the system.

Operational Principles

• Responsiveness: Requests must be acknowledged and processed promptly to prevent frustration and informal workarounds.
• Transparency: Personnel should be informed about request status and outcomes to maintain trust in the process.
• Accountability: Ownership for handling requests must be clearly assigned to responsible teams or individuals.
• Cultural Integration: Leaders and supervisors should reinforce the use of formal channels and discourage unsanctioned self-remediation efforts.

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.

Custom or pre-built detection logic can be used to determine if a user account has authenticated from two geographic locations in a period of time that is not feasible for legitimate travel between the locations.

In relevant security tooling (such as a SIEM or EDR), a watchlist (also known as a reference set) should be used to monitor for any activity generated by accounts belonging to employees who have left the organization, as this is unexpected. This can help to ensure that the security team readily detects any unrevoked access or account usage.

This process must be in partnership with the Human Resources team, which should inform the security team when an individual leaves the organization (during an Employee Off-Boarding Process, see PV024), including their full and user account names. Ideally, this process should be automated to prevent any gaps in monitoring between the information being sent and the security team adding the name(s) to the watchlist. All format variations should be considered as individual entries in the watchlist to ensure accounts using different naming conventions will generate alerts, such as john.smith, john smith, john.smith@company.com, and jsmith.

False positives could occur if there is a legitimate reason for interaction with the account(s), such as actions conducted by IT staff.

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.