hunt-threat

name	hunt-threat
description	Conduct proactive, hypothesis-driven threat hunting. Use when performing advanced hunting based on threat intelligence, TTPs, or anomalies. For Tier 3 analysts or dedicated threat hunters. Supports iterative search, pivoting, and comprehensive documentation.
required_roles	{"chronicle":"roles/chronicle.editor","gti":"GTI Enterprise"}
personas	["threat-hunter","tier3-analyst"]

Advanced Threat Hunting Skill

Conduct proactive, hypothesis-driven threat hunts based on threat intelligence, observed anomalies, or specific TTPs.

Inputs

• HUNT_HYPOTHESIS - Clear statement of the hunt objective (required)
  • Example: "Suspected DNS tunneling for C2 based on recent actor TTPs"
  • Example: "Anomalous PowerShell execution on critical servers"
  • Example: "Living-off-the-land techniques bypassing EDR"
• (Optional) RELEVANT_GTI_REPORTS - GTI Collection IDs or report names
• (Optional) TARGET_SCOPE_QUERY - UDM query to narrow initial scope
• TIME_FRAME_HOURS - Lookback period (default: 168 = 7 days)
• (Optional) HUNT_CASE_ID - case for tracking the hunt

Workflow

Step 1: Define Hypothesis & Scope

Clearly articulate:

• What threat behavior are we looking for?
• What would evidence of this look like in logs?
• What systems/users are in scope?
• What time period is relevant?

Create or identify HUNT_CASE_ID for documentation.

Step 2: Deep Intelligence Analysis

For each relevant GTI report:

gti-mcp.get_collection_report(id=REPORT_ID)
gti-mcp.get_entities_related_to_a_collection(id=REPORT_ID, relationship_name="attack_techniques")
gti-mcp.get_collection_timeline_events(id=REPORT_ID)
gti-mcp.get_collection_mitre_tree(id=REPORT_ID)

Also:

gti-mcp.get_threat_intel(query="Details on specific TTPs")

Step 3: Develop Initial Hunt Queries

Based on hypothesis and intelligence, formulate advanced queries:

SIEM queries:

secops-mcp.search_security_events(
    text="Advanced UDM query targeting specific behaviors",
    hours_back=TIME_FRAME_HOURS
)

BigQuery (for large-scale analysis):

bigquery.execute-query(query="Complex analytical query")

Step 4: Iterative Search & Analysis

Hunt Loop:

1. Execute queries
2. Analyze results for outliers, suspicious patterns
3. Identify leads (suspicious hosts, users, processes, connections)
4. Refine hypothesis based on findings
5. Develop new, more targeted queries
6. Repeat until exhausted or time limit reached

Key questions at each iteration:

• Does this match our hypothesis?
• What's the baseline/normal behavior?
• Are these true anomalies or noise?
• What should we pivot on next?

Step 5: Advanced Enrichment

For each promising lead:

secops-mcp.lookup_entity(entity_value=LEAD)

GTI enrichment and pivoting:

gti-mcp.get_..._report(identifier=LEAD)
gti-mcp.get_entities_related_to_...(identifier=LEAD)

Check IOC matches:

secops-mcp.get_ioc_matches()

Step 6: Continuous Documentation

Document throughout in HUNT_CASE_ID:

• Queries used (with results summary)
• Analysis reasoning
• Positive and negative findings
• Pivots and why they were taken

Use /document-in-case for each significant finding.

Step 7: Hunt Report

Use /generate-report with REPORT_TYPE="hunt_summary":

• Hypothesis and scope
• Intelligence sources used
• Queries executed
• Findings (positive and negative)
• Recommendations

Step 8: Action Based on Findings

Confirmed Threat Found: → Escalate to Incident Response immediately → Create incident case, hand over evidence

Suspicious Activity (not confirmed): → Recommend enhanced monitoring → Propose new detection rules to Security Engineering

Valuable Insights (no active threat): → Document for future reference → Propose detection improvements

Inconclusive: → Document process and limitations → Note areas for future investigation

Required Outputs

After completing this skill, you MUST report these outputs:

Output	Description
HUNT_QUERIES	UDM queries executed during the hunt
INITIAL_FINDINGS	Raw findings from SIEM searches
FINDINGS_TYPE	Category: lateral_movement, credential_access, data_exfil, or generic
DISCOVERED_IOCS	IOCs extracted from findings (IPs, domains, hashes)
HIGH_CONFIDENCE_IOCS	IOCs confirmed malicious via GTI enrichment
THREAT_CONFIRMED	Boolean: true if active threat confirmed, false otherwise

Hunt Hypothesis Templates

TTP-Based:

"Hunt for [MITRE Technique] activity, specifically [observable behavior], targeting [scope] over [timeframe]."

Actor-Based:

"Hunt for [Threat Actor] TTPs including [specific techniques], focusing on [likely targets] based on [intelligence source]."

Anomaly-Based:

"Investigate anomalous [behavior type] observed in [data source], specifically [anomaly description], to determine if malicious."

Example Hunt Queries

DNS Tunneling:

metadata.event_type = "NETWORK_DNS" AND
network.dns.questions.name MATCHES ".*[a-z0-9]{30,}.*" AND
target.hostname NOT IN @known_cdn_domains

Suspicious PowerShell:

metadata.event_type = "PROCESS_LAUNCH" AND
target.process.file.full_path MATCHES ".*powershell.*" AND
target.process.command_line MATCHES ".*(encodedcommand|bypass|hidden).*"

Living-off-the-Land:

metadata.event_type = "PROCESS_LAUNCH" AND
target.process.file.full_path IN @lolbins_list AND
principal.user.userid NOT IN @authorized_admins