Generative AI Governance & Secure Content Automation in Higher Education
DOI:
https://doi.org/10.63282/3050-9262.IJAIDSML-V5I4P116Keywords:
Generative Artificial Intelligence, AI Governance, Secure Content Automation, Higher Education, Academic Integrity, Data Privacy, Responsible AI, Institutional Knowledge ManagementAbstract
The accelerated adoption of Generative Artificial Intelligence (GenAI) in higher education is reshaping academic content creation, assessment, research support, and institutional operations. While GenAI offers significant gains in scalability, personalization, and operational efficiency, its integration raises critical concerns related to data privacy, academic integrity, intellectual property protection, bias, transparency, and regulatory compliance. This paper presents a governance-driven approach to secure content automation that enables higher education institutions to harness GenAI responsibly and at scale. The proposed framework integrates policy-based governance, lifecycle-aware controls, and security-by-design principles across data ingestion, content generation, review, storage, and distribution. Core elements include governance-aware prompting, risk scoring, human-in-the-loop oversight, access control, and auditability, ensuring accountability and trust in AI-generated outputs. Drawing on recent empirical evidence from 2024 studies, the paper demonstrates measurable improvements in policy compliance, risk reduction, automation efficiency, and ethical trustworthiness when structured governance frameworks are applied. By aligning institutional governance, technical controls, and academic oversight, the study provides a practical reference architecture and implementation guidance for universities. The findings highlight that secure content automation, when coupled with robust AI governance, enables sustainable digital transformation while preserving educational values, regulatory alignment, and stakeholder trust
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