AIDIRS: A conceptual Framework for AI-Driven Incident Response in Higher Education

As cyber threats increasingly target educational institutions, there is a growing need for intelligent and proactive cybersecurity measures in higher education. This paper proposes a conceptual framework for an Artificial Intelligence (AI)-Driven Incident Response System (AIDIRS) tailored to the unique needs of academic environments. The AIDIRS model integrates machine learning, natural language processing, and automation to detect threats, prioritize incidents, and initiate real-time responses with minimal human intervention. This framework is designed to address the unique needs of the academia landscape including fragmented infrastructure, limited cybersecurity resources and adherence to regulatory bodies such as Family Educational Rights and Privacy Act (FERPA) in the United States and General Data Protection Regulation (GDPR) in Europe. This framework offers a scalable, ethical and proactive approach to cybersecurity in higher education. This paper also highlights barriers such as data privacy, skill gaps, and integration issues, and concludes with strategic recommendations for future work to ensure the practical implementation as well as a robust, responsive, and ethical deployment of AIDIRS in academic institutions