{
  "abstract": "Artificial intelligence (AI) is increasingly used to support cybersecurity tasks such as network\n\nintrusion detection, malware analysis, threat intelligence, and security-operations workflows. At the\n\nsame time, AI-enabled systems introduce risks related to model opacity, adversarial manipulation,\n\nprivacy, distribution shift, and misuse of generative models. This narrative review organizes the topic\n\ninto four connected areas: AI-based detection, explainable AI (XAI), adaptive threat intelligence, and\n\ngenerative AI and large language models (LLMs). Across these areas, the central engineering\n\nchallenge is not only to detect malicious activity, but to produce reliable, interpretable, and operationally\n\nuseful outputs under changing conditions. The review highlights recurring limitations in evaluation\n\nrealism, explainability, adaptation to evolving threats, and human oversight. It proposes a practical\n\nresearch agenda focused on reproducible benchmarking, robustness testing, human-in-the-loop\n\nresponse, privacy-aware data practices, and transparent reporting. Because this manuscript is a\n\nnarrative review rather than a systematic review, it does not claim exhaustive coverage of the literature.\n\nA future version should document a reproducible search and screening protocol and verify all\n\nbibliographic records before journal submission",
  "abstract_shown": true,
  "affiliations": [
    "DHA Suffa University"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 1,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Syed Tabish Shah",
      "orcid": "0009-0000-6367-4123"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/6rk6wwf9nt6ej.bib",
  "cited_by": 0,
  "doi": "10.5281/zenodo.23450799",
  "fulltext_url": null,
  "id": "6rk6wwf9nt6ej",
  "issns": [],
  "landing_url": "https://zenodo.org/records/23450799",
  "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/23450799/files/Syed_Tabish_Shah_research.pdf/content",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-18",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/6rk6wwf9nt6ej.ris",
  "sources": [
    {
      "first_seen": "2026-11-01T12:21:03Z",
      "native_id": "23450799",
      "source": "zenodo",
      "url": "https://zenodo.org/records/23450799"
    }
  ],
  "subjects": [],
  "title": "Artificial Intelligence in Cybersecurity: Techniques, Limitations, and Research Directions",
  "title_text": "Artificial Intelligence in Cybersecurity: Techniques, Limitations, and Research Directions",
  "type": "preprint",
  "updated_at": "2026-11-01T12:21:03Z",
  "url": "https://research.draconitic.com/paper/6rk6wwf9nt6ej",
  "venue": "Zenodo",
  "versions": []
}