# Embracing the Generative AI Revolution: Advancing Tertiary Education in Cybersecurity with GPT

arXiv: https://arxiv.org/abs/2403.11402 · HTML: https://mlsanity.com/paper/2403.11402/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 95 references that are in the map.
- First posted on arXiv 18 Mar 2024.

## Builds on (most cited first, 11 of 11)

- [Sparks of Artificial General Intelligence: Early experiments with GPT-4](https://mlsanity.com/paper/2303.12712/) (2023)
- [Capabilities of GPT-4 on Medical Challenge Problems](https://mlsanity.com/paper/2303.13375/) (2023)
- [GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Models](https://mlsanity.com/paper/2303.10130/) (2023)
- [Inadequacies of Large Language Model Benchmarks in the Era of Generative Artificial Intelligence](https://mlsanity.com/paper/2402.09880/) (2024)
- [Generating Fake Cyber Threat Intelligence Using Transformer-Based Models](https://mlsanity.com/paper/2102.04351/) (2021)
- [Targeted Phishing Campaigns using Large Scale Language Models](https://mlsanity.com/paper/2301.00665/) (2022)
- [From Google Gemini to OpenAI Q* (Q-Star): A Survey of Reshaping the Generative Artificial Intelligence (AI) Research Landscape](https://mlsanity.com/paper/2312.10868/) (2023)
- [From COBIT to ISO 42001: Evaluating Cybersecurity Frameworks for Opportunities, Risks, and Regulatory Compliance in Commercializing Large Language Models](https://mlsanity.com/paper/2402.15770/) (2024)
- [Generative Adversarial Learning for Intelligent Trust Management in 6G Wireless Networks](https://mlsanity.com/paper/2208.01221/) (2022)
- [Deception for Cyber Defence: Challenges and Opportunities](https://mlsanity.com/paper/2208.07127/) (2022)
- [GPT, Ontology, and CAABAC: A Tripartite Personalized Access Control Model Anchored by Compliance, Context and Attribute](https://mlsanity.com/paper/2403.08264/) (2024)

## Abstract (arXiv)

The rapid advancement of generative Artificial Intelligence (AI) technologies, particularly Generative Pre-trained Transformer (GPT) models such as ChatGPT, has the potential to significantly impact cybersecurity. In this study, we investigated the impact of GPTs, specifically ChatGPT, on tertiary education in cybersecurity, and provided recommendations for universities to adapt their curricula to meet the evolving needs of the industry. Our research highlighted the importance of understanding the alignment between GPT's ``mental model'' and human cognition, as well as the enhancement of GPT capabilities to human skills based on Bloom's taxonomy. By analyzing current educational practices and the alignment of curricula with industry requirements, we concluded that universities providing practical degrees like cybersecurity should align closely with industry demand and embrace the inevitable generative AI revolution, while applying stringent ethics oversight to safeguard responsible GPT usage. We proposed a set of recommendations focused on updating university curricula, promoting agility within universities, fostering collaboration between academia, industry, and policymakers, and evaluating and assessing educational outcomes.