ip-get-gti-report

name	ip-get-gti-report
description	Use when fetching GTI reputation, ASN, and geolocation details for an IP address.
category	atomic
version	1.0.0
type	Skill
title	Skill: Atomic Runbook: Get IP Address Reputation from GTI
generated	{"by":"process:google-labs-jules","at":"2026-08-20T02:00:00Z"}

Atomic Runbook: Get IP Address Reputation from GTI

ID: RB-ATOM-IP-001 Version: 1.0 Last_Updated: 2025-05-30 Purpose: To retrieve a comprehensive IP address analysis report from Google Threat Intelligence (GTI) to assess its reputation. Parent_Runbook(s)/Protocol(s): rules-bank/indicator_handling_protocols.md#1-atomic-indicator-ip-address Trigger: When an IP address requires an external reputation check as part of an investigation or triage process.

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Inputs Required

• ip_address: string - The IP address to query.
  • Source Example: Alert field source.ip, destination.ip, principal.ip, target.ip; Output from another runbook.

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Execution Steps

1. Tool Selection:
   • Primary_Tool_MCP_Server: Google Threat Intelligence MCP
   • Primary_Tool_Name: get_ip_address_report
2. Parameter Mapping:
   • Map ip_address (Input) to MCP Tool parameter ip_address.
3. Execute Tool: Call the get_ip_address_report tool with the mapped ip_address.
   • AI Agent Note: Refer to rules-bank/mcp_tool_best_practices.md for specific guidance on GTI tools.
4. Data Transformation/Extraction:
   • The primary output is a JSON object. Key fields for initial assessment are typically found under data.attributes.last_analysis_stats (especially malicious), data.attributes.categories, data.attributes.as_owner, and data.attributes.country.

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Outputs Expected

• gti_ip_report: JSON - The full JSON report from the GTI get_ip_address_report tool.
• malicious_score: integer - The number of engines reporting the IP as malicious (extracted from gti_ip_report.data.attributes.last_analysis_stats.malicious).
• harmless_score: integer - The number of engines reporting the IP as harmless (extracted from gti_ip_report.data.attributes.last_analysis_stats.harmless).
• suspicious_score: integer - The number of engines reporting the IP as suspicious (extracted from gti_ip_report.data.attributes.last_analysis_stats.suspicious).
• categories: list - List of categories assigned to the IP by GTI (e.g., "malware", "phishing").
• as_owner: string - The AS owner of the IP.
• country: string - The country associated with the IP.
• output_status: string - ["Success", "Failure"]
• output_message: string (if Failure) - Details of the issue (e.g., "API error", "Invalid IP format").

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Decision Logic / Next Steps (If Applicable)

• IF output_status is "Success":
  • IF malicious_score > 5 OR "malware" IN categories OR "phishing" IN categories THEN
    • Flag IP as "High_Risk_GTI".
    • Consider proceeding to containment-related runbooks or escalate immediately.
  • ELSE IF malicious_score > 0 OR suspicious_score > 0 THEN
    • Flag IP as "Medium_Risk_GTI_Needs_Correlation".
    • Proceed to further internal investigation runbooks (e.g., rb_ip_lookup_entity_chronicle.md).
  • ELSE (low malicious/suspicious score)
    • Flag IP as "Low_Risk_GTI".
    • Proceed to further internal investigation runbooks.
• ELSE (output_status is "Failure"):
  • Log error: output_message.
  • Consider executing rb_ip_get_secops_threat_intel.md as an alternative.
  • IF alternative also fails, escalate to human analyst: "Failed to retrieve external reputation for IP {ip_address}."

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AI Agent Execution Notes

• Ensure the input ip_address is a valid IPv4 or IPv6 address.
• If the GTI tool returns an error related to API quotas, log this and consider a retry strategy with backoff, or escalate if retries fail.
• The AI should parse the gti_ip_report to extract the specific output parameters listed above.

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Metrics Collection Points

• Log execution time for this runbook.
• Log output_status, malicious_score, harmless_score, suspicious_score.
• (Reference rules-bank/ai_performance_logging_requirements.md)

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References

• rules-bank/mcp_tool_best_practices.md
• rules-bank/indicator_handling_protocols.md
• rules-bank/ai_performance_logging_requirements.md
• "Blueprint for AI Agents in Cybersecurity" (for general AI agent interaction principles)
• "Measuring ROI of AI agents in security operations" (for context on metric importance)