user-lookup-entity-chronicle

name	user-lookup-entity-chronicle
description	Use when querying Chronicle user entity details, department, manager, and role context.
category	atomic
version	1.0.0
type	Skill
title	Skill: Atomic Runbook: Lookup User Entity Activity in Chronicle
generated	{"by":"process:google-labs-jules","at":"2026-08-20T02:00:00Z"}

Atomic Runbook: Lookup User Entity Activity in Chronicle

ID: RB-ATOM-USER-001 Version: 1.0 Last_Updated: 2025-05-30 Purpose: To retrieve a summary of a user's activity from Chronicle SIEM using the lookup_entity tool. This provides a quick overview of their logins, systems accessed, related alerts, and first/last seen times. Parent_Runbook(s)/Protocol(s): rules-bank/indicator_handling_protocols.md#user-indicator (Assuming a future section for Users) Trigger: When a username (e.g., from an alert, phishing report, or HR termination list) requires an initial check for activity within Chronicle.

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Inputs Required

• username: string - The username to look up (e.g., jdoe, [e-mail]).
  • Source Example: Alert field principal.user.userid, email sender/recipient.
• hours_back (optional): integer - How many hours of historical data to consider. Defaults to 72 (3 days).

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Execution Steps

1. Tool Selection:
   • Primary_Tool_MCP_Server: secops-mcp
   • Primary_Tool_Name: lookup_entity
2. Parameter Mapping:
   • Map username (Input) to MCP Tool parameter entity_value.
   • Map hours_back (Input or default) to MCP Tool parameter hours_back.
3. Execute Tool: Call lookup_entity.
   • AI Agent Note: Refer to rules-bank/mcp_tool_best_practices.md. Usernames might need to be in a specific format (e.g., UPN) depending on how they are logged and parsed into UDM.
4. Data Transformation/Extraction:
   • Output is textual. AI may parse for specific details (e.g., number of distinct hosts logged into, alert counts).

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Outputs Expected

• chronicle_entity_summary: string - Textual summary from lookup_entity.
• related_alerts_count (optional, if parsable): integer.
• accessed_hosts_count (optional, if parsable): integer - Number of unique hosts associated with the user in the summary.
• login_locations_summary (optional, if parsable): string/list - Summary of source IPs or geolocations for logins.
• output_status: string - ["Success", "NoInfoFound", "Failure"]
• output_message: string (if Failure).

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Decision Logic / Next Steps (If Applicable)

• IF output_status is "Success":
  • Log chronicle_entity_summary.
  • IF related_alerts_count > 0 OR summary indicates logins from unusual locations OR access to an unusually high number of hosts THEN
    • Flag User as "Suspicious Activity Observed - Needs Deeper Dive".
    • Proceed to rb_user_search_login_activity_chronicle.md and rb_user_search_process_activity_chronicle.md.
  • ELSE
    • Flag User as "Low/Normal Activity Observed via Lookup".
    • May still proceed to detailed searches if external context (e.g., user reported as compromised) warrants it.
• IF output_status is "NoInfoFound":
  • Log "No activity found for user {username} in Chronicle for the last {hours_back} hours."
• ELSE (output_status is "Failure"):
  • Log error: output_message.
  • Escalate: "Failed to lookup user {username} in Chronicle."

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AI Agent Execution Notes

• Be mindful of username formats (e.g., samaccountname vs. userprincipalname vs. short name). The data_normalization_map.md might be relevant if the input username needs translation to the format typically found in UDM principal.user.userid.
• AI parsing chronicle_entity_summary should look for "Logon Events:", "Distinct Hosts:", "Related Alerts:".

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Metrics Collection Points

• Log execution time.
• Log output_status.
• (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 (future User section)
• rules-bank/ai_performance_logging_requirements.md
• rules-bank/data_normalization_map.md