{
  "abstract": "This paper presents a methodology for evaluating Retrieval-Augmented Generation (RAG) in industrial cybersecurity compliance assessment. The study focuses on whether AI-generated compliance judgments are supported by authoritative cybersecurity guidance and evidence of control implementation.\n\nThe proposed evaluation uses NIST Special Publication 800-82 Revision 3 as its primary reference source and defines three assessment outcomes: compliant, non-compliant, and insufficient evidence. The planned benchmark examines five evidence conditions: full supporting evidence, partial supporting evidence, irrelevant evidence, absent evidence, and contradictory evidence.\n\nThe methodology describes a human annotation and adjudication process and proposes metrics for classification performance, retrieval relevance, evidence grounding, citation correctness, and uncertainty handling.\n\nThis manuscript is a methodology-focused research preprint. The full benchmark experiments and quantitative evaluation have not been completed, and no empirical performance claims are made. Further dataset construction, experimental execution, and independent evaluation are required.",
  "abstract_shown": true,
  "affiliations": [
    "Axix Technologies LLC USA"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 1,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Muhammad Ali Arshad",
      "orcid": "0009-0003-0377-5178"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/080343xqxmcny.bib",
  "cited_by": 0,
  "doi": "10.5281/zenodo.23498011",
  "fulltext_url": null,
  "id": "080343xqxmcny",
  "issns": [],
  "landing_url": "https://zenodo.org/records/23498011",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [],
  "openalex": null,
  "pdf_url": "https://zenodo.org/api/records/23498011/files/RAG_Industrial_Cybersecurity_Compliance_Research_Paper.pdf/content",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-20",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/080343xqxmcny.ris",
  "sources": [
    {
      "first_seen": "2026-11-03T12:20:47Z",
      "native_id": "23498011",
      "source": "zenodo",
      "url": "https://zenodo.org/records/23498011"
    }
  ],
  "subjects": [],
  "title": "Evaluating Retrieval-Augmented Generation for Industrial Cybersecurity Compliance Assessment",
  "title_text": "Evaluating Retrieval-Augmented Generation for Industrial Cybersecurity Compliance Assessment",
  "type": "preprint",
  "updated_at": "2026-11-03T12:20:47Z",
  "url": "https://research.draconitic.com/paper/080343xqxmcny",
  "venue": "Zenodo",
  "versions": []
}