AI Insider Threat Detection Specialist
An AI Insider Threat Detection Specialist combines behavioral analytics, machine learning, and cybersecurity expertise to identify…
Skill Guide
It is the structured process of mapping an insider's malicious or negligent actions to the stages of a cyber kill-chain and then correlating those actions to specific tactics, techniques, and procedures (TTPs) in the MITRE ATT&CK framework to detect, analyze, and disrupt threats.
Scenario
You are provided with a sanitized incident report detailing an employee who exfiltrated customer data. The report contains timeline entries: 'accessed CRM via VPN from home', 'used USB drive on office workstation', 'emailed zipped files to personal account'.
Scenario
Simulate a scenario where a frustrated system administrator attempts to escalate privileges and access sensitive R&D repositories before resigning.
Scenario
As the lead for Insider Threat Program (ITP) analytics, you must create a living document that codifies detection logic for the top 3 insider threat scenarios specific to your company (e.g., IP theft by engineers, financial fraud by accounting staff, sabotage by IT admins).
Use ATT&CK Navigator to create, annotate, and share custom matrices of insider TTPs. Operationalize the model in a SIEM by writing detection rules that look for the *sequence* of TTPs. UEBA platforms are critical for detecting the 'low-and-slow' deviations indicative of insider prep.
Use the Diamond Model to enrich your analysis beyond just TTPs, focusing on the adversary's persona and infrastructure (their home network, personal cloud storage). Strictly document chain of custody for every mapped log entry to ensure forensic integrity. Adversary emulation plans should be derived from your own insider threat models.
Answer Strategy
The candidate must demonstrate end-to-end thinking, connecting motivation to action to detection. Start with the kill-chain: focus on early stages like 'Weaponization' (staging code in a personal repo) and 'Delivery' (using approved code repository tools abnormally) rather than just the final exfiltration. Map to specific techniques: Prioritize T1074.001 - Data Staged: Local Data Staging, T1119 - Automated Collection, and T1567.002 - Exfiltration Over Web Service: Code Repository. Emphasize that early-phase detection (before data leaves the network) is critical for prevention.
Answer Strategy
This tests understanding of 'fileless' or 'LOLBins' abuse and behavioral detection. The strategy is to move beyond static indicators to behavioral analytics. The answer should state: 1) The need to baseline normal usage patterns of powerful tools (PowerShell, PsExec, WMI) by role. 2) The importance of detecting anomaly in *context*, not just the tool's presence: unusual parent process chains, execution at odd hours, targets outside of normal scope. 3) The specific ATT&CK sub-techniques to focus on, like T1059.001 - PowerShell or T1047 - WMI, but with a focus on the *behavioral* rules (e.g., 'PowerShell spawned by Office application followed by network connection to a personal cloud storage domain').
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