Integrating Generative AI in Cybersecurity Curricula

Artificial intelligence technologies with generative capabilities have accelerated fundamental transformations in security architectures, necessitating reconceptualization of security frameworks and threat assessment protocols. This study presents a pedagogical framework for integrating generative AI (GenAI) into university-level cybersecurity curricula. The methodology establishes foundational knowledge in generative models and language processing architectures, followed by applications across defensive security measures. The framework includes automated cyber threat intelligence, malicious code and malware detection, log anomaly detection, digital image forensics, and AI-assisted penetration testing. The framework acknowledges the dual-use nature of GenAI in security domains, incorporating prompt injection attacks that manipulate model behaviors and compromise system integrity. Laboratory modules presented will provide students with hands-on experience on advanced tools including large language models, diffusion models, and cognitive architectures for automated security assessment. This study seeks to prepare cybersecurity professionals with critical competencies necessary for effective operation within an environment increasingly shaped by artificial intelligence systems.