Means
Ability to Modify Cloud Resources
Access
Aiding and Abetting
Asset Control
Bluetooth
Bring Your Own Device (BYOD)
Clipboard
Delegated Access via Managed Service Providers
FTP Servers
Installed Software
Media Capture
Network Attached Storage
Physical Disk Access
Placement
Printing
Privileged Access
Removable Media
Screenshots and Screen Recording
Sensitivity Label Leakage
SMB File Sharing
SSH Servers
System Startup Firmware Access
Unmanaged Credential Storage
Unrestricted Software Installation
Unrevoked Access
Web Access
- ID: ME027.001
- Created: 01st August 2025
- Updated: 01st August 2025
- Contributor: The ITM Team
Credentials in Ticketing Systems
Passwords, API keys, and privileged credentials are communicated, stored, or embedded in service desk tickets, including incident responses, change management notes, and administrative work orders. These credentials are often entered by IT or support personnel as part of access restoration, environment configuration, or user provisioning workflows.
Because many service desk platforms (such as ServiceNow, Jira Service Management, Freshservice & Zendesk) are broadly accessible across IT, engineering, and sometimes third-party vendor teams, the storage of credentials in ticketing systems significantly expands the number of individuals who can retrieve operationally sensitive access. In many cases, ticket logs are not considered part of the formal audit surface for access control, and standard retention, encryption, or obfuscation policies are inconsistently applied.
When credentials are available through searchable tickets, any subject with sufficient access to the service desk platform may bypass formal access provisioning and review processes. This creates an unmonitored path to privilege, especially when ticket histories are long-lived and tied to high-value systems. Investigators should treat such platforms as latent access repositories, especially during retrospective analysis of system access or in cases where no formal credential use appears in logs.
Prevention
ID | Name | Description |
---|---|---|
PV023 | Access Reviews | 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. |
PV058 | Consistent Enforcement of Minor Violations | Establish and maintain processes where all policy violations, including those perceived as minor or low-impact, are addressed consistently, proportionately, and promptly. By reinforcing that even small infractions matter, organizations deter boundary testing behaviors and reduce the risk of escalation into more serious incidents.
Implementation Approaches
Operational Principles
|
PV012 | End-User Security Awareness Training | 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. |
PV003 | Enforce an Acceptable Use Policy | 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. |
PV055 | Enforce Multi-Factor Authentication (MFA) | Multi-Factor Authentication (MFA) is a critical component of a comprehensive security strategy, providing an additional layer of defense by requiring more than just a password for system access. This multi-layered approach significantly reduces the risk of unauthorized access, especially in cases where an attacker has obtained or guessed a user’s credentials. MFA is particularly valuable in environments where attackers may have gained access to user credentials via phishing, data breaches, or social engineering.
For organizations, enabling MFA across all critical systems is essential. This includes systems such as Active Directory, VPNs, cloud platforms (e.g., AWS, Azure, Google Cloud), internal applications, and any resources that store sensitive data. MFA ensures that access control is not solely dependent on passwords, which are vulnerable to compromise. Systems that are protected by MFA require users to authenticate via at least two separate factors: something they know (e.g., a password), and something they have (e.g., a hardware token or a mobile device running an authenticator app).
The strength of MFA depends heavily on the factors chosen. Hardware-based authentication devices, such as FIDO2 or U2F security keys (e.g., YubiKey), offer a higher level of security because they are immune to phishing attacks. These keys use public-key cryptography, meaning that authentication tokens are never transmitted over the network, reducing the risk of interception. In contrast, software-based MFA solutions, like Google Authenticator or Microsoft Authenticator, generate one-time passcodes (OTPs) that are time-based and typically expire after a short window (e.g., 30 seconds). While software-based tokens offer a strong level of security, they can be vulnerable to device theft or compromise if not properly secured.
To maximize the effectiveness of MFA, organizations should integrate it with their Identity and Access Management (IAM) system. This ensures that MFA is uniformly enforced across all access points, including local and remote access, as well as access for third-party vendors or contractors. Through integration, organizations can enforce policies such as requiring MFA for privileged accounts (e.g., administrators), as these accounts represent high-value targets for attackers seeking to escalate privileges within the network.
It is equally important to implement adaptive authentication or risk-based MFA, where the system dynamically adjusts its security requirements based on factors such as user behavior, device trustworthiness, or geographic location. For example, if a subject logs in from an unusual location or device, the system can automatically prompt for an additional factor, further reducing the likelihood of unauthorized access.
Regular monitoring and auditing of MFA usage are also critical. Organizations should actively monitor for suspicious activity, such as failed authentication attempts or anomalous login patterns. Logs generated by the Authentication Service Providers (ASPs), such as those from Azure AD or Active Directory, should be reviewed regularly to identify signs of attempted MFA bypass, such as frequent failures or the use of backup codes. In addition, setting up alerts for any irregular MFA activity can provide immediate visibility into potential incidents.
Finally, when a subject no longer requires access, it is critical that MFA access is promptly revoked. This includes deactivating hardware security keys, unlinking software tokens, and ensuring that any backup codes or recovery methods are invalidated. Integration with the organization’s Lifecycle Management system is essential to automate the deactivation of MFA credentials during role changes or when an employee departs. |
PV038 | Insider Threat Awareness Training | 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. |
PV048 | Privileged Access Management (PAM) | 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:
Benefits:
|
Detection
ID | Name | Description |
---|---|---|
DT046 | Agent Capable of Endpoint Detection and Response | 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). |
DT045 | Agent Capable of User Activity Monitoring | 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. |
DT047 | Agent Capable of User Behaviour Analytics | 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. |
DT137 | Discrepancies Between Physical Access Logs and System Authentication | In tightly controlled environments where system access is expected to be physically co-located (e.g., secure enclaves, badge-restricted zones, no-VPN networks), login events occurring without corresponding physical entry records indicate potential misuse of credentials or anti-forensic access. Subjects may provide or leak credentials to others, or operate under shared or impersonated accounts. This discrepancy can also signal badge cloning, tailgating, or failure to enforce physical-to-logical access binding.
Detection Methods
Compare physical access logs (e.g., Lenel, Genetec, CCure badge systems) with:
Construct correlation logic:
Filter for:
Alert on:
Example ScenarioIn a secure IT operations center, access to administrative consoles is restricted to physically present engineers. On a holiday, an engineer’s domain account logs into the configuration server — but badge access records show they never entered the facility that day. Investigation reveals the password was shared with a colleague under informal backup practices, violating policy and creating audit ambiguity. |
DT102 | User and Entity Behavior Analytics (UEBA) | 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. |
DT101 | User Behavior Analytics (UBA) | 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. |