Security Monitoring and Threat Detection Questions
Covers the principles and practical design of security monitoring, logging, and threat detection across environments including cloud scale infrastructure. Topics include data collection strategies, centralized logging and storage, security information and event management architecture, pipeline and ingestion design for high volume and high velocity data, retention and indexing tradeoffs, observability and telemetry sources, and alerting and tuning to reduce noise. Detection techniques include signature based detection, anomaly detection, indicators of compromise, behavioral detection, correlation rules, and threat intelligence integration. Also covers evaluation metrics such as false positives and false negatives, detection coverage and lead time, incident escalation, playbook integration with incident response, automation and orchestration for investigation and remediation, and operational concerns such as scalability, cost, reliability, and privacy or compliance constraints.
MediumTechnical
0 practiced
Scenario: You observe a potential lateral movement campaign in logs: multiple authentication successes across hosts by the same user, subsequent unusual process creations, and new service installations. Describe how you would correlate authentication, process, and network logs to detect this campaign. Provide an example correlation rule or graph query (pseudocode or SQL-like) and discuss heuristics for time windows, identity mapping, and enrichment data you would use to increase confidence.
MediumTechnical
0 practiced
Scenario: Your organization must collect security logs while complying with GDPR. For each of the following, describe concrete steps you would take: (1) logging web requests that may contain PII in headers or body, (2) storing logs that contain user identifiers, (3) supporting a right-to-be-forgotten request that affects analytics and model training. Explain techniques for pseudonymization, hashing/salting, data minimization, access controls, and how to maintain ML utility.
EasyTechnical
0 practiced
Scenario: You are responsible for instrumenting a new microservice so it emits security-relevant telemetry useful for ML detection. Describe what to log (authentication events, authorization failures, incoming request metadata, response codes, correlation IDs, container/runtime metadata), how to structure logs (structured JSON, consistent schema, fields to always include), sampling strategies to limit volume without losing signal, and how to avoid logging PII while preserving detection effectiveness. Explain the implications of these choices for downstream ML models and alerting.
HardTechnical
0 practiced
How would you defend ML-based detectors against adversarial evasion and poisoning attacks in a production security monitoring environment? Discuss practical defenses spanning data validation, robust training, input sanitization, ensemble methods, detection of label drift/poisoning, provenance tracking, rate-limiting suspicious data sources, and runbook responses when poisoning is suspected. Give specific actions for both training-time and runtime hardening.
HardTechnical
0 practiced
Design privacy-preserving ML approaches for security detection on logs that contain PII. Compare differential privacy, federated learning, synthetic data generation, local anonymization (k-anonymity/pseudonymization), and use of secure enclaves or MPC. For each approach discuss feasibility, expected impact on detection accuracy, operational complexity, and compliance considerations. Recommend a hybrid approach for a cloud + on-prem deployment with reasoning.
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