Disruption of Business Operations

A subject deletes organizational files or data (manually or through tooling) outside authorized workflows, resulting in the loss, concealment, or unavailability of operational assets. This infringement encompasses both targeted deletion (e.g. selected records, logs, or documents) and bulk removal (e.g. recursive deletion of directories or volumes).

Unlike Destructive Malware Deployment, which uses self-propagating or malicious code to irreversibly damage systems, this behavior reflects direct user-driven actions or scripts that remove or purge data without employing destructive payloads. Deletions may be conducted via built-in utilities, custom scripts, scheduled tasks, or misuse of administrative tools such as backup managers or version control systems.

This activity frequently occurs to:

• Conceal evidence of other infringing actions (e.g. log deletion to frustrate investigation)
• Sabotage availability of critical information (e.g. deleting shared drives or project directories)
• Facilitate exfiltration or preparation (e.g. purging redundant files before copying sensitive data)

It may also involve secondary actions such as emptying recycle bins, purging shadow copies, disabling version histories, or wiping removable media to obscure the scope of deletion.

The subject deliberately interferes with operational systems in ways that degrade, interrupt, or misroute services relied upon by customers, without relying on file deletion or malware. This includes misconfigurations, service disabling, authentication interference, or intentional introduction of latency, instability, or incorrect outputs. The result is operational degradation that directly or indirectly affects service delivery, availability, or trust.

Unlike File or Data Deletion, this infringement does not depend on erasing data, and unlike Destructive Malware Deployment, it does not rely on malicious payloads or automated damage. The disruption instead stems from direct manipulation of infrastructure, configurations, service states, or user access.

Examples include:

• Intentionally disabling authentication or API endpoints
• Modifying DNS, firewall, or routing rules to block legitimate traffic
• Tampering with load balancers or HA/failover logic
• Altering service configurations to break dependency chains (e.g. pointing production systems to empty dev databases)
• Injecting false flags into monitoring or orchestration tools to trigger auto-scaling failures or mis-alerts
• Enabling excessive logging or computation to induce service latency or memory exhaustion
• Locking critical service accounts, API keys, or secrets in vault systems

These actions may be motivated by retaliation, concealment, sabotage, or insider coercion, and often occur in environments where the subject has legitimate system access but uses it to destabilize service delivery covertly.

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.

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.

Network Access Control (NAC) manages and regulates devices accessing a organization's network(s), including personal devices under a Bring Your Own Device (BYOD) policy. NAC systems ensure that only authorized and compliant devices can connect to the network, reducing security risks.
 

NAC performs the following functions:

• Device Authentication and Authorization: Checks whether the device meets the organization’s security policies before granting access.
• Compliance Checks: Verifies that devices have up-to-date security patches and configurations. Non-compliant devices may be denied access or placed in a quarantined network zone.
• Segmentation and Isolation: Restricts devices' access to sensitive areas, limiting potential impact from compromised devices.
• Continuous Monitoring: Tracks connected devices for ongoing compliance and can automatically quarantine or disconnect those that fall out of compliance.
• Policy Enforcement: Applies security policies to ensure devices can only access appropriate resources based on their security status.

NAC functionality can be provided by dedicated NAC appliances, next-generation firewalls, unified threat management devices, and some network switches and routers.

Access to specific areas of a site should be restricted to only authorized personnel, through the use of controls such as locked doors, mantraps, and gates requiring an ID badge.

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.

The Principle of Least Privilege should be enforced, and period reviews of permissions conducted to ensure that accounts have the minimum level of access required to complete duties as per their role.

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.

Static code analysis integrated into CI/CD pipelines provides a critical prevention mechanism against anti-forensic behaviors embedded in code, scripts, and infrastructure definitions. By enforcing automated review of logic patterns prior to deployment, organizations can detect concealed execution paths, scheduling abuse, and evasive constructs before they reach production.

This control is especially vital in mitigating deferred execution techniques, where the subject inserts code that activates long after submission—typically to evade scrutiny or delay attribution. Static analysis enables defenders to identify high-risk patterns at rest, before runtime, reducing reliance on reactive detection and shortening investigative timelines.

Detection of Time-Based Execution Logic:
Flag conditional statements that compare system time or date against hardcoded thresholds or calculated values.
Examples:

• if (datetime.now() > target_date)
• if time.time() > 1723468800 (UNIX timestamp obfuscation)

Abnormal Delay Functions and Sleep Calls:
Block or escalate the use of delay functions exceeding operational thresholds. Focus on calls intended to stall execution post-deployment.
Examples:

• sleep(3600)
• Start-Sleep -Seconds 1800
• Thread.sleep(900000) (in Java)

Embedded Scheduler References in Scripts:
Detect scripting logic that attempts to create or modify scheduled tasks, cron jobs, or background triggers.
Examples:

• echo '0 4 * * * /usr/bin/script.sh' >> /etc/crontab
• schtasks /create /tn "Update" /tr C:\temp\payload.exe /sc once /st 23:59
• at now + 1 minute /interactive "cmd.exe"

Identification of Obfuscation and Dynamic Constructs:
Scan for base64-encoded, concatenated, or dynamically constructed commands that attempt to evade static detection of time or scheduling logic.
Examples:

• eval(base64.b64decode(payload))
• task_command = "schtasks" + " /create /sc daily"
• exec("sleep " + str(delay_seconds))

CI/CD Blocking and Exception Escalation:
Treat the above patterns as rule violations within CI/CD pipelines. Enforce blocking behavior unless a security-reviewed exception is filed. Ensure exception cases are logged, tagged, and auditable.

Pre-Deployment Artifact Scanning:
Apply static analysis not only to source code but to bundled artifacts such as container images, compiled scripts, or deployment templates (e.g., Terraform, CloudFormation) to catch embedded logic in infrastructure as code (IaC).

Cross-Team Code Review and Signature Expansion:
Maintain shared detection signatures across DevSecOps, application security, and insider risk teams. Regularly review triggered matches to refine accuracy and discover new anti-forensic variants.

Attestation of Safe Logic by Departing Engineers:
Require final code audits for subjects flagged for departure or termination. Mandate re-review of any automation, CI/CD jobs, or privileged scripting authored by the subject.

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.

Audit Logs are records generated by systems and applications to document activities and changes within an environment. They provide an account of events, including user actions, system modifications, and access patterns.

In high-sensitivity physical environments such as data centers, battery banks, and server rooms, environmental consistency is a critical security signal. Unexplained physical changes—such as added devices, modified cable routing, or thermal anomalies—may indicate preparatory activity by a subject intending to exfiltrate data, introduce malicious hardware, or compromise critical infrastructure.

Automated visual and thermal baseline scanning provides a scalable method to detect such changes by comparing real-time camera feeds to historical baselines. This technique extends the concept of "known good state" into the physical realm, enabling early identification of unauthorized modifications before they result in policy violations or technical compromise.

Methods of implementation

Autonomous Environmental Scanning Systems: 

Deploy robotic or fixed-position platforms capable of capturing high-resolution visual and thermal imagery at defined intervals. These systems should be configured to scan static components (e.g., server racks, power units, fire suppression systems, cable bundles, and access panels) from consistent angles and distances.

Baseline Comparison Algorithms: 

Implement software that compares each new scan to a stored baseline image set. Visual deviation detection should include object placement, cable routing, connected device presence (e.g., USB or external drives), and enclosure status (open vs. closed). Thermal deviation detection should identify abnormal heat signatures on batteries, processors, fans, or power supplies—indicative of tampering, overload, or early-stage failure.

Alert Routing and Escalation: 

Flag deviations beyond a defined threshold for human validation. Route alerts to a live remote operator who can verify anomalies and determine whether an onsite response is required. Escalation should trigger access reviews, subject correlation (e.g., badge scans or door logs), and containment measures if sabotage or preparatory behavior is suspected.

Targeted Focus Zones

Prioritize static components that are unlikely to change under normal operational procedures. These include:

• Server rack front and rear panels
• Electrical panels and circuit breakers
• UPS units and cooling systems
• Cable trays and conduit paths
• High-value compute or storage nodes

Anomaly Logging and Cross-Referencing: 

Record each scan result, deviation instance, and operator decision for later forensic analysis. Integrate with physical access systems to correlate anomalies with subject presence.

Establish and monitor baseline system performance metrics for all critical endpoints, servers, and cloud workloads to detect deviations that may indicate unauthorized activities, such as crypto mining, data staging, or malware execution. Deviations from expected resource usage profiles can serve as an early indicator of operational misuse, compromise, or unauthorized software deployment.

Detection Methods

• Collect and baseline key performance metrics (e.g., CPU utilization, GPU load, memory consumption, disk I/O, and network throughput) for each system class based on normal operational workloads.
• Continuously monitor and analyze live system telemetry against established baselines using security information and event management (SIEM), endpoint detection and response (EDR), or cloud-native monitoring tools.
• Set threshold alerts for resource utilization that significantly exceeds normal variance ranges over sustained periods without corresponding change tickets, scheduled tasks, or workload justifications.
• Correlate performance anomalies with process monitoring to identify unauthorized or unexpected processes consuming system resources.
• Integrate anomalous performance detections into insider threat investigation workflows, focusing on unexplained deviations, especially on systems not expected to experience significant workload fluctuations (e.g., office endpoints, file servers, idle cloud instances).

Indicators

• Sustained CPU or GPU utilization significantly above baseline norms, particularly during non-peak operational hours.
• Persistent high memory usage, disk I/O, or network traffic inconsistent with documented business activities.
• Systems exhibiting performance profiles typical of known unauthorized activities (e.g., high sustained CPU with low disk I/O suggestive of mining workloads).
• Lack of approved change requests or business justification corresponding with the onset of anomalous resource usage.
• Anomalies clustered around users, departments, or system groups known for prior boundary-testing or policy violations.

CCTV can be used to observe activity within or around a site. This control can help to detect preparation or infringement activities and record it to a video file.

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.

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:

• Active Directory login events
• VPN or RDP session logs
• Privileged session management tools

Construct correlation logic:

• Alert when login event occurs but no badge entry for that subject within ±30 minutes.
• Extend correlation window for multiple facility entry points if needed.

Filter for:

• Logins to high-sensitivity systems
• After-hours activity
• Accounts with elevated privileges

Alert on:

• Logins during badge absence
• Logins post facility closure
• Badge present, but login occurs from unassigned workstation

Example Scenario

In 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.

File Integrity Monitoring (FIM) is a technical prevention mechanism designed to detect unauthorized modification, deletion, or creation of files and configurations on monitored systems. The most basic implementation method is cryptographic hash comparison, where a known-good baseline (typically SHA256 or SHA1) is calculated and stored for monitored files. At regular intervals (or in real time) current file states are re-hashed and compared to the baseline. Any discrepancy in hash value, size, permissions, or timestamp is flagged as an integrity violation.

While hash comparison is foundational, mature File Integrity Monitoring (FIM) solutions incorporate additional telemetry and instrumentation to increase forensic depth, reduce false positives, and support attribution:

• ACL and Permission Monitoring: Captures unauthorized changes to file ownership, execution flags (e.g. chmod +x), NTFS permissions, or group inheritance, critical for detecting silent privilege escalation.
• Timestamp Integrity Checks: Monitors for retroactive or unnatural changes to creation, modification, and access timestamps, commonly associated with anti-forensic behaviors such as timestomping.
• Event-based Hooks: Leverages OS-native event subsystems (e.g. Windows ETW, USN Journal; Linux inotify, auditd, fanotify) to trigger high-fidelity alerts on file system activity without waiting for interval-based scans.
• Process Attribution: Enriches FIM events with the user identity, process name, PID, and command line responsible for the change, enabling precise correlation with session logs, drift indicators, and subject behavior.
• Snapshot or Versioned Comparisons: Enables file state diffing across time, including rollback of modified artifacts or analysis of change sequences (common in forensic suites and some EDR platforms).

To be effective in insider threat contexts, File Integrity Monitoring should be explicitly tuned to monitor (at minimum):

• Executable and script directories (%ProgramFiles%, %APPDATA%, /usr/local/bin/, /opt/)
• Configuration and runtime paths (/etc/, C:\Windows\System32\Config, container volumes)
• Security logs, audit trails, and telemetry agents (.evtx, /var/log/, SIEM client logs)
• Credential storage and secrets locations (browser credential stores, password vaults, keyrings, .env files)
• Backup and recovery tooling (scripts, snapshot schedulers, and volume metadata)

In ransomware or destruction scenarios, File Integrity Monitoring can detect the early stages of detonation by identifying rapid, high-volume file modifications and hash changes, particularly in mapped drives, document repositories, and shared storage. This can serve as a trigger for containment actions and/or investigation before full encryption completes, especially when correlated with process telemetry and known ransomware behaviors (e.g. deletion of shadow copies, entropy spikes).

When tuned and deployed appropriately, File Integrity Monitoring provides a high-fidelity signal of tampering, staging, or covert access attempts, even when other telemetry (e.g. signature-based detection or anomaly modeling) fails to trigger. This makes it particularly valuable in environments where subjects have elevated access, control over telemetry agents, or knowledge of investigative blind spots.

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.

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.