Operationalizing Generative AI for Cybersecurity Incident Reporting: A Conceptual Operational AI and MLOps Framework for Regulated Healthcare Systems

Author First name, Last name, Institution

• Salama Alkhoori, Zayed University, Abu Dhabi Campus
• Mubarak Alfalahi, Zayed University, Abu Dhabi Campus
• Adhari Aldahmani, Zayed University, Abu Dhabi Campus
• Ashraf Khalil, Zayed University, Abu Dhabi CampusFollow
• Heba Ismail, Zayed University, Abu Dhabi CampusFollow

Document Type

Conference Proceeding

Source of Publication

2026 International Conference on Integrated Intelligence and Cognitive Engineering Iciice 2026

Publication Date

4-18-2026

Abstract

The increasing adoption of artificial intelligence in critical infrastructures has shifted research attention from model development toward the operationalization, governance, and lifecycle management of AI systems. In healthcare environments, cybersecurity incident reporting remains largely manual despite advances in security monitoring technologies, leading to delays, inconsistencies, and compliance challenges. Generative artificial intelligence (GenAI) offers opportunities to automate incident report drafting; however, its use in regulated domains raises concerns related to data privacy, auditability, accountability, and operational reliability. This paper presents a conceptual Operational AI and MLOps framework for AI-assisted cybersecurity incident reporting in healthcare systems. Using the Abu Dhabi Health Information Exchange (Malaffi) as a motivating context, the paper first describes the current AI-assisted incident reporting workflow and identifies its limitations. It then introduces a modular framework that formalizes this workflow through structured data management, generative reporting, MLOps lifecycle governance, and human oversight. An illustrative operational scenario is provided to demonstrate how the framework would function in practice. Rather than reporting empirical results, the paper offers a scientifically grounded design rationale and discusses anticipated operational benefits supported by existing literature. The framework aims to guide future implementations of trustworthy and compliant generative AI systems in healthcare cybersecurity operations.

DOI Link

10.1109/iciice69672.2026.11565490

ISBN

[9798331545314]

Publisher

IEEE

Disciplines

Computer Sciences

Keywords

AI Governance, Cybersecurity, Generative AI, Healthcare Systems, MLOps, Operational AI

Scopus ID

105043742390

Recommended Citation

Alkhoori, Salama; Alfalahi, Mubarak; Aldahmani, Adhari; Khalil, Ashraf; and Ismail, Heba, "Operationalizing Generative AI for Cybersecurity Incident Reporting: A Conceptual Operational AI and MLOps Framework for Regulated Healthcare Systems" (2026). All Works. 8193.
https://zuscholars.zu.ac.ae/works/8193

Indexed in Scopus

yes

Open Access

no