Course Objective & Scope
Security Operations Centres (SOCs) struggle with thousands of daily SIEM alerts, complex query languages (KQL, SPL, ESQL) and repetitive enrichment steps that inflate analyst burnout and mean time to respond (MTTR). This course shows how to safely and measurably integrate Microsoft Sentinel / Security Copilot, Elastic AI Assistant, Splunk AI and custom LLM/agent workflows into SOC operations.
Participants build schema-grounded natural-language-to-SIEM query generators, autonomous Tier-1 alert triage and enrichment agents on SOAR/n8n, automated incident timelines and executive summaries, and defences against log-borne prompt injection targeting SOC assistants.
Who Should Attend?
- SOC managers, Tier-1/2/3 SOC analysts and incident response (DFIR) specialists
- SIEM and SOAR engineers (Microsoft Sentinel, Splunk, Elastic, QRadar)
- Security and AI engineers building SecOps automations
- Prerequisite: familiarity with SIEM/SOC workflows and at least one query language (KQL, SPL or Lucene/ESQL)
Key Takeaways
- Identify high-ROI AI use cases across Tier-1 triage, threat hunting and incident reporting
- Build schema-grounded assistants that translate natural language into validated KQL, SPL and Sigma rules
- Develop SOAR/agent playbooks that enrich SIEM alerts with CTI, asset inventory and historical case context
- Enforce human-in-the-loop approval gates before destructive containment actions such as host isolation or account lockouts
- Defend SOC AI assistants against log-borne indirect prompt injection and measure reductions in MTTD/MTTR
Curriculum
AI and Agentic Workflows in the Modern SOC Architecture
- Evolving from static SIEM rules and rigid SOAR playbooks to context-aware AI SOC architectures
- Comparing Microsoft Security Copilot, Elastic AI Assistant, Splunk AI and on-prem open-weight LLM deployments
Natural-Language-to-SIEM Queries (NL2KQL / NL2SPL) and Sigma Generation
- Building a schema- and example-grounded (RAG) assistant for generating KQL, SPL and ESQL queries
- Syntax validation, query cost guardrails and accelerating hypothesis-driven threat hunting
Autonomous Tier-1 Alert Triage and Contextual Enrichment
- Agent workflows that gather CTI/VirusTotal data, CMDB asset criticality, user context and similar past tickets
- Producing structured triage verdicts (true positive, benign, escalate) with cited log evidence
Incident Response (DFIR), Timeline Reconstruction and Reporting
- Synthesizing multi-source logs into a chronological incident timeline mapped to MITRE ATT&CK
- Automating shift-handover notes, technical post-mortems and executive/regulatory notification drafts
Securing the SOC AI, Human-in-the-Loop Gates and SOC Metrics
- Defending SOC agents against log-borne indirect prompt injection planted in User-Agent strings or email headers
- HITL approval gates for containment actions, hallucination checks and measuring MTTD, MTTR and triage accuracy
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