Anthropic-Cybersecurity-Skills/skills/building-threat-hunt-hypothesis-framework/SKILL.md

name, description, domain, subdomain, tags, version, author, license, nist_csf, mitre_attack

name	description	domain	subdomain	tags	version	author	license	nist_csf	mitre_attack
building-threat-hunt-hypothesis-framework	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. Use when planning or running a proactive threat hunt or scoping compromise from an intel- or anomaly-driven lead.	cybersecurity	threat-hunting	threat-hunting methodology hypothesis threat-intelligence hunting-framework proactive-detection	1.0	mahipal	Apache-2.0	DE.CM-01 DE.AE-02 DE.AE-07 ID.RA-05	T1071 T1059.001 T1055 T1547

Building Threat Hunt Hypothesis Framework

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

Concept	Description
TA0001	Initial Access
TA0003	Persistence
TA0008	Lateral Movement
TA0010	Exfiltration

Tools & Systems

Tool	Purpose
CrowdStrike Falcon	EDR telemetry and threat detection
Microsoft Defender for Endpoint	Advanced hunting with KQL
Splunk Enterprise	SIEM log analysis with SPL queries
Elastic Security	Detection rules and investigation timeline
Sysmon	Detailed Windows event monitoring
Velociraptor	Endpoint artifact collection and hunting
Sigma Rules	Cross-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]