Webapp Building Threat Hunt Hypothesis Framework

---
name: webapp-building-threat-hunt-hypothesis-framework
description: Build a systematic threat hunt hypothesis framework that transforms threat intelligence, attack patterns, and environmental data into testable hunting hypotheses.
domain: webapp
category: threat-intelligence
hide: true
license: Apache-2.0
metadata:
  manifest_entry: src/corpus/webapp-building-threat-hunt-hypothesis-framework/SKILL.md
---
# 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

## Common Misconfigurations & Verification

- **Unfalsifiable hypotheses.** "Find evil" or "detect APTs" can't be tested. Scope every hypothesis to a specific ATT&CK technique + a named data source + an observable (e.g., "T1059.001: Empire stagers appear as 4104 events containing `FromBase64String` + `WebClient`").
- **Assuming coverage that doesn't exist.** Hunting T1059.001 with Script Block Logging (4104) disabled, or relying on Sysmon EID 1/3 that was never deployed, yields false negatives that look like clean results. Verify ingestion first with `| tstats count where index=* by sourcetype` and check each source's last-seen timestamp.
- **No baseline.** Without a frequency baseline every result looks anomalous. Build the normal-activity baseline before judging outliers.
- **Timezone/clock skew across DCs** breaks multi-source correlation — normalize everything to UTC and confirm event time vs index time gaps.
- **Verify the hunt can actually catch the thing:** emit a benign atomic test matching the hypothesis (e.g., request a Kerberos SPN ticket, or run an `-enc` command) and confirm the query returns your test event end-to-end.
- **Document negatives correctly:** record what coverage existed at hunt time — absence of evidence is only meaningful where logging was confirmed present.

## 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]
```