Detection, Monitoring, and Incident Response Capabilities Questions
Understanding of detection and monitoring mechanisms (SIEM, EDR, IDS/IPS, log aggregation, behavioral analytics, threat intelligence integration), designing effective alerting and detection rules, assessing detection gaps, incident response procedures, and how penetration testing findings inform incident response planning. Understanding the importance of logging, centralized log management, and alert response.
MediumTechnical
72 practiced
Technical domain specific (medium): Design a SOAR playbook for phishing triage that ingests suspicious emails from mailbox detection tools, performs header analysis, extracts URLs and attachments, performs reputation checks, detonates attachments in a sandbox, enriches with threat intel, and then proposes automated remediation actions (quarantine, block URL). Include decision points where human approval is required and how evidence/artifacts are preserved.
HardTechnical
44 practiced
Technical-coding (hard): Given the following sample Sysmon-like JSON event snippets, implement a Python function detect_credential_dumping(events: List[dict]) -> List[dict] that identifies potential credential-dumping incidents. The function should detect sequences where a suspicious utility (e.g., procdump.exe, rundll32.exe loading suspicious modules, or lsass memory access) is invoked and within a 10-minute window there are process creations or module loads consistent with credential dumping. Sample events:[{"timestamp":"2026-02-01T10:12:00Z","host":"host1","event_id":1,"process":"C:\\Windows\\System32\\procdump.exe","command_line":"procdump -ma lsass.exe"}, {"timestamp":"2026-02-01T10:15:00Z","host":"host1","event_id":6,"image_loaded":"C:\\tools\\mimikatz.dll"}, {"timestamp":"2026-02-01T10:50:00Z","host":"host2","event_id":1,"process":"C:\\Windows\\explorer.exe","command_line":"..."}]Requirements: Python 3.8+, overall complexity O(n log n) or better, handle events out-of-order, and return incidents containing host, window start/end, and correlated evidence event IDs. Explain briefly how your algorithm scales to streaming input.
MediumTechnical
40 practiced
Technical-coding (medium): Write a Splunk SPL query that finds hosts where a scheduled task executed a process that matches suspicious names (e.g., procdump, mimikatz, rundll32 with suspicious params) and correlate this to Sysmon Process Create (EventID 1) within the previous 24 hours. The query should group results by host and show timestamped evidence paths for triage.
HardTechnical
56 practiced
Technical domain specific (hard): Evaluate vendor-managed EDR, open-source EDR, and managed detection providers for a global enterprise with ~200k endpoints. Produce a decision matrix comparing detection coverage, telemetry fidelity, deployment and maintenance effort, response SLAs, integration with existing SIEM/SOAR, total cost of ownership, and legal/data-sovereignty considerations. Recommend a hybrid approach and justify it.
EasyTechnical
82 practiced
Explain how you would use the MITRE ATT&CK framework to classify detections and prioritize coverage. Which mapping fields should be included in detection rules and playbooks (for example, ATT&CK technique ID, tactic, confidence, detection method, required telemetry), and how would you use ATT&CK mapping to drive gaps and sprint prioritization?
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