{
  "id": 16097,
  "url": "https://arxiv.org/abs/2608.02422v1",
  "title": "Agentic Incident Response through Digital Twin-Enhanced Multiscale Planning",
  "summary": "Incident response is currently managed by security operators using predefined playbooks, resulting in slow, labor-intensive security decision-making processes. Consequently, there is a growing need for automated incident response planning. Decision-theoretic approaches based on control, optimization, and reinforcement learning have been proposed to automate such planning tasks with well-grounded approaches, yet most of which, while guaranteeing strong performance, are limited to abstract models",
  "authors": "Yiran Gao, Tao Li, Kim Hammar",
  "category": "research",
  "topics": "jobs-economy,agents-autonomy",
  "orgs": null,
  "regions": null,
  "published_at": "2026-08-03T16:03:16.000Z",
  "fetched_at": "2026-08-04T05:10:21.797Z",
  "source_slug": "x-arxiv-cs-ai",
  "source_name": "arXiv cs.AI",
  "source_homepage": "https://arxiv.org/list/cs.AI/recent",
  "ethics_ai_record_url": "https://ethics.ai/record/16097",
  "original_url": "https://arxiv.org/abs/2608.02422v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}