Building Threat Hunt Hypothesis Framework

SkillDocs & knowledge

Guides your agent through planning a threat hunt: forming hypotheses from threat intel and running EDR/SIEM queries to validate them.

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Details

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Building Threat Hunt Hypothesis FrameworkStart free
About this skill

Build a systematic threat-hunt workflow that turns threat intelligence and ATT&CK gap analysis into testable hypotheses, then executes and validates them via EDR/SIEM queries (CrowdStrike, Defender, Splunk, Elastic, Sysmon, Velociraptor, Sigma) and documents findings in a standardized hunt report. U

What this skill tells your AI

The instructions your AI receives, as published by costrict-plugins-repo/mukul975-anthropic-cybersecurity-skills-cybersecurity-skills in skills/building-threat-hunt-hypothesis-framework/SKILL.md and read by ahel’s review.

When to Use

  • When proactively hunting for indicators of building threat hunt hypothesis framework in the environment
  • After threat intelligence indicates active campaigns using these techniques
  • During incident response to scope compromise related to these techniques
  • When EDR or SIEM alerts trigger on related indicators
  • During periodic security assessments and purple team exercises

Prerequisites

  • EDR platform with process and network telemetry (CrowdStrike, MDE, SentinelOne)
  • SIEM with relevant log data ingested (Splunk, Elastic, Sentinel)
  • Sysmon deployed with comprehensive configuration
  • Windows Security Event Log forwarding enabled
  • Threat intelligence feeds for IOC correlation

Workflow

  1. Formulate Hypothesis: Define a testable hypothesis based on threat intelligence or ATT&CK gap analysis.
  2. Identify Data Sources: Determine which logs and telemetry are needed to validate or refute the hypothesis.
  3. Execute Queries: Run detection queries against SIEM and EDR platforms to collect relevant events.
  4. Analyze Results: Examine query results for anomalies, correlating across multiple data sources.
  5. Validate Findings: Distinguish true positives from false positives through contextual analysis.
  6. Correlate Activity: Link findings to broader attack chains and threat actor TTPs.
  7. Document and Report: Record findings, update detection rules, and recommend response actions.

Key Concepts

ConceptDescription
TA0001Initial Access
TA0003Persistence
TA0008Lateral Movement
TA0010Exfiltration

Tools & Systems

ToolPurpose
CrowdStrike FalconEDR telemetry and threat detection
Microsoft Defender for EndpointAdvanced hunting with KQL
Splunk EnterpriseSIEM log analysis with SPL queries
Elastic SecurityDetection rules and investigation timeline
SysmonDetailed Windows event monitoring
VelociraptorEndpoint artifact collection and hunting
Sigma RulesCross-platform detection rule format

Common Scenarios

  1. Scenario 1: Intelligence-driven hunt based on APT campaign report
  2. Scenario 2: ATT&CK coverage gap analysis driving hypothesis creation
  3. Scenario 3: Anomaly-driven hypothesis from UEBA alert investigation
  4. Scenario 4: Situational awareness hunt based on industry sector threats

Output Format

Hunt ID: TH-BUILDI-[DATE]-[SEQ]
Technique: TA0001
Host: [Hostname]
User: [Account context]
Evidence: [Log entries, process trees, network data]
Risk Level: [Critical/High/Medium/Low]
Confidence: [High/Medium/Low]
Recommended Action: [Containment, investigation, monitoring]

Signals

GitHub stars
73
Forks
12
Last commit
Sep 2026
Advanced
Item type
skill
Key
building-threat-hunt-hypothesis-framework-costrict-p-09ldzsq
Source
github.com/costrict-plugins-repo/mukul975-anthropic-cybersecurity-skills-cybersecurity-skills