GenAI-Based Cybersecurity Adoption in Higher Education: A SWOT-Driven Responsible Implementation Framework
Year: 2026
Type: preprint
Abstract: The rapid digital transformation of higher education has increased dependence on cloud platforms, learning management systems, online assessment systems, research repositories, enterprise applications, and interconnected digital services. At the same time, this transformation has also considerably increased cyber risks facing higher education institutions (HEIs). Generative Artificial Intelligence (GenAI) enables threat-intelligence analysis, security-log interpretation, anomaly investigation, incident-response assistance, and cybersecurity knowledge management. However, adoption brings challenges involving hallucination, privacy, organizational readiness, human oversight, accountability, regulatory compliance, and adversarial misuse. Existing cybersecurity and AI governance frameworks serve as foundational guidance, although they primarily concern cybersecurity risk management or AI governance independently and do not provide a holistic adoption framework explicitly focused on GenAI-based cybersecurity for HEI infrastructure. This paper presents a SWOT-based responsible adoption model composed of five aspects: GenAI cybersecurity capabilities, organizational readiness, ethical governance, regulatory compliance, and cybersecurity outcomes. A single comparative document analysis assesses the proposed framework with the NIST Cybersecurity Framework (CSF) 2.0, NIST Artificial Intelligence Risk Management Framework (AI RMF) with its Generative AI Profile, ISO/IEC 42001, and higher-education cybersecurity guidance. This decision model utilizes nine evaluation criteria to review cybersecurity capability, GenAI specificity, organizational readiness, ethical governance, regulatory alignment, human oversight, higher-education contextualization, measurable cybersecurity outcomes, and implementation decision support. Results suggest that established frameworks offer robust but varying emphases of coverage, while the proposed model consolidates these concerns into an HEI-specific adoption decision layer. The framework aims to guide HEIs not only in understanding how GenAI can assist with cybersecurity, but also in determining whether the institution is prepared to apply it responsibly, what governance mechanisms should be established, and how its value can be evaluated.
Source: Research Square
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7225805974