defensive-threat-hunting

Hypothesis-driven threat hunting uses KQL queries to investigate threats across enterprise endpoints by validating assumptions, documenting findings, and mapping detections to MITRE ATT&CK techniques. Structured workflows guide the hunt from hypothesis creation to gap analysis.

LOLBAS detection identifies threats by monitoring living-off-the-land binaries and scripts abused by attackers. KQL hunt queries target this process execution abuse across enterprise endpoints to detect suspicious activity and produce structured, prioritized findings.

Yes, threat hunting findings map directly to MITRE Navigator coverage analysis to visualize technique detection gaps. This workflow aligns endpoint telemetry results with MITRE ATT&CK categories and generates documentation for coverage gap analysis.

LSASS memory monitoring requires endpoint telemetry to detect process access and memory indicators. KQL queries target LSASS access patterns to identify credential dumping activities, producing structured hunt findings with prioritized detections.

Documenting threat hunting findings uses provided documentation templates to record hypotheses, results, and MITRE mappings. This workflow produces structured hunt reports and coverage gap analysis to promote prioritized detections across enterprise endpoints.

Yes, KQL-based hunt queries are required to execute LOLBAS detection, LSASS monitoring, and exfiltration checks. The MITRE-aligned workflow depends on endpoint telemetry and KQL to produce structured hunt findings and gap analysis.