performing-threat-hunting-with-elastic-siem

name	performing-threat-hunting-with-elastic-siem
description	Performs proactive threat hunting in Elastic Security SIEM using KQL/EQL queries, detection rules, and Timeline investigation to identify threats that evade automated detection. Use when SOC teams need to hunt for specific ATT&CK techniques, investigate anomalous behaviors, or validate detection coverage gaps using Elasticsearch and Kibana Security.
domain	cybersecurity
subdomain	soc-operations
tags	["soc","elastic","siem","threat-hunting","kql","eql","mitre-attack","kibana"]
version	1.0
author	mahipal
license	Apache-2.0
nist_ai_rmf	["MEASURE-2.7","MAP-5.1","MANAGE-2.4"]
atlas_techniques	["AML.T0070","AML.T0066","AML.T0082"]
d3fend_techniques	["Application Protocol Command Analysis","Network Isolation","Network Traffic Analysis","Client-server Payload Profiling","Network Traffic Community Deviation"]
nist_csf	["DE.CM-01","DE.AE-02","RS.MA-01","DE.AE-06"]
mitre_attack	["T1078","T1685.002","T1685.005","T1566","T1027"]
source	https://github.com/mukul975/Anthropic-Cybersecurity-Skills
source_commit	04450304b12645cb2b974ab96d28c0664758a88d

Performing Threat Hunting with Elastic SIEM

When to Use

Use this skill when:

• SOC teams need to proactively search for threats not caught by existing detection rules
• Threat intelligence reports describe new TTPs requiring validation against historical data
• Red team exercises reveal detection gaps that need hunting query development
• Periodic hunting cadence requires structured hypothesis-driven investigations

Do not use for real-time alert triage — that belongs in the Elastic Security Alerts queue with automated detection rules.

Prerequisites

• Elastic Security 8.x+ with Security app enabled in Kibana
• Data ingestion via Elastic Agent (Endpoint Security integration) or Beats (Winlogbeat, Filebeat, Packetbeat)
• Data normalized to Elastic Common Schema (ECS) field mappings
• User role with kibana_security_solution and read access to relevant indices
• MITRE ATT&CK framework knowledge for hypothesis generation

Workflow

Step 1: Develop Hunting Hypothesis

Start with a hypothesis based on threat intelligence, ATT&CK technique, or anomaly:

Example Hypothesis: "Attackers are using living-off-the-land binaries (LOLBins) for execution, specifically certutil.exe for file downloads (T1105 — Ingress Tool Transfer)."

Define scope:

• Data sources: logs-endpoint.events.process-*, logs-windows.sysmon_operational-*
• Time range: Last 30 days
• Expected indicators: certutil.exe with -urlcache, -split, or -decode flags

Step 2: Hunt Using KQL in Discover

Open Kibana Discover and query with KQL (Kibana Query Language):

process.name: "certutil.exe" and process.args: ("-urlcache" or "-split" or "-decode" or "-encode" or "-verifyctl")

Refine to exclude known legitimate use:

process.name: "certutil.exe"
  and process.args: ("-urlcache" or "-split" or "-decode")
  and not process.parent.name: ("sccm*.exe" or "ccmexec.exe")
  and not user.name: "SYSTEM"

For PowerShell-based hunting with encoded commands (T1059.001):

process.name: "powershell.exe"
  and process.args: ("-enc" or "-encodedcommand" or "-e " or "frombase64string" or "iex" or "invoke-expression")
  and not process.parent.executable: "C:\\Windows\\System32\\svchost.exe"