{
  "id": 277,
  "url": "https://arxiv.org/abs/2607.03233v1",
  "title": "Agentic and Generative AI for Open-Source Intelligence and Cyber Investigations: Taxonomy, Evaluation, Challenges, and Future Directions",
  "summary": "The rapid growth of publicly available digital information has rendered manual open-source intelligence (OSINT) analysis insufficient for modern intelligence, cybersecurity, and cyber investigation. Large language models (LLMs) and agentic AI systems, capable of tool use, multi-step reasoning, and iterative intelligence generation, have emerged as promising solutions, yet evaluation frameworks have not kept pace with reported capabilities. This survey systematically reviews 74 studies and makes ",
  "authors": "Eduardo Almeida Palmieri, Mohamed Chahine Ghanem, Dipo Dunsin, Zubair Baig, Ed de Quincey, Kim-Kwang Raymond Choo",
  "category": "research",
  "topics": "military-security,agents-autonomy,finance-investment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-07-03T11:42:29.000Z",
  "fetched_at": "2026-07-14T14:14:24.248Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/277",
  "original_url": "https://arxiv.org/abs/2607.03233v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}