{
 "license": "CC-BY-4.0",
 "attribution": "Fide AI, Agentic Cyber Explorer",
 "url": "https://agentic-cyber-explorer.pages.dev/events/google-sec-gemini-v1-2025/",
 "asOf": "2026-09-26",
 "id": "google-sec-gemini-v1-2025",
 "date": "2025-04-04",
 "datePrecision": "day",
 "title": "Google announces Sec-Gemini v1, an experimental model for security operations workflows",
 "lane": "defense",
 "kind": "tool-release",
 "summary": "Google announced Sec-Gemini v1, an experimental model combining Gemini with Google Threat Intelligence, OSV and Mandiant data for tasks such as incident root-cause analysis and vulnerability impact assessment. Google reports it outperforms other models by at least 11% on CTI-MCQ and 10.5% on CTI-Root Cause Mapping, and offered free research access to selected organizations.",
 "whyItMatters": "It is an example of a defender-specialized model whose advantage comes from integrated threat-intelligence data rather than only model scale.",
 "actors": [
  "google"
 ],
 "topics": [
  "soc-automation",
  "threat-intelligence"
 ],
 "atlas": [
  "tools"
 ],
 "artifacts": [
  "sec-gemini",
  "gemini"
 ],
 "sources": [
  {
   "url": "https://security.googleblog.com/2025/04/google-launches-sec-gemini-v1-new.html",
   "publisher": "Google Security Blog",
   "title": "Google announces Sec-Gemini v1, a new experimental cybersecurity model",
   "date": "2025-04-04",
   "type": "primary",
   "accessed": "2026-09-25"
  }
 ],
 "keyFacts": [
  {
   "fact": "Sec-Gemini v1 outperforms other models on CTI-MCQ by at least 11% and on CTI-Root Cause Mapping by at least 10.5%.",
   "locator": "Benchmark paragraph, Figures 1-2"
  },
  {
   "fact": "Authors: Elie Burzstein and Marianna Tishchenko (Sec-Gemini team); access offered to select organizations for research.",
   "locator": "Byline and availability paragraph"
  }
 ],
 "significance": 2,
 "fideQuestions": [
  "FID-076"
 ],
 "methods": [],
 "review": "assistant-drafted",
 "addedOn": "2026-09-25"
}