KQL mastery and automation — production-ready hunts and automated workflows
Key Questions
What are MITRE-mapped KQL libraries in Microsoft Sentinel?
MITRE-mapped KQL libraries are production-ready query collections derived from threat hunts, aligned with MITRE ATT&CK framework. They enable cross-log triage and focus on performance and false positive reduction. These libraries support automated workflows and historical XDR hunts.
How does Sentinel AI support analytics and playbooks?
Sentinel AI, including anomaly detection and Copilot, enhances analytics by automating triage and playbook adaptation based on context. It complements cloud log centralization and D3 AI for high-fidelity network evidence. This reduces uninvestigated alerts, with 67% typically going unchecked.
What is the significance of 12-year Data Lake retention in Microsoft Defender XDR?
Native 12-year log retention in the Data Lake enables long-term historical hunts for XDR investigations. It supports architecting robust SIEM labs on Azure and deep threat analysis. This retention aids in understanding extended attack paths over time.
What are custom graphs in Microsoft Sentinel?
Custom graphs, now in public preview as of April 1, visualize attack paths using GQL and AI across identities, devices, and resources. They help investigate connections in security attacks. This feature enhances threat response in Sentinel.
What is an Agentic SOC and its production tests?
An Agentic SOC uses multi-agent AI for autonomous triage and investigation, surpassing static playbooks. RSAC 2026 highlighted 5 production tests to validate effectiveness, echoed by Corelight AI. It dynamically adapts to threats without replacing SIEM.
How does AI triage address SIEM alert challenges?
AI triage investigates 67% of uninvestigated SIEM alerts by automating analysis without tuning rules or losing visibility. It integrates with Sentinel for cross-log workflows. This fixes alert fatigue in production environments.
What hacks accelerate agent development for Sentinel Data Lake?
Hacks for building agents with Microsoft Sentinel Data Lake include dev tools and best practices from senior product managers. They speed up custom AI agent creation for triage and SOAR. Resources cover Linux telemetry fixes and integration.
What learning resources exist for KQL hunts, rules, and SOAR?
Webinars and workshops cover KQL hunts, rules, and SOAR automation in Sentinel. YouTube videos like 'Investigating Cyber Threats with Microsoft Sentinel' (1:31:23) and 'Learn SIEM and Threat Detection' provide hands-on guidance. These focus on production-ready workflows.
MITRE-mapped KQL libraries from hunts; cross-log triage, Sentinel AI (anomaly/Copilot) for analytics/playbooks. Performance/FP focus; enhanced by cloud log centralization/D3 AI complements, 12-year Data Lake retention for XDR historical hunts, Linux telemetry fixes, MSP KQL libraries. New: Custom graphs public preview (April 1) for attack paths via GQL/AI; data lake agent dev hacks; AI-driven SOC high-fidelity network evidence (Corelight/D3 dynamic playbooks); webinars/workshop on KQL hunts, rules, SOAR. RSAC Agentic SOC: 5 production tests for multi-agent AI triage; Corelight agentic AI echoes.