The integration of Large Language Models (LLMs) and gamification presents a promising direction for enhancing cyber security education by enabling adaptive, engaging, and personalised learning experiences. LLMs can dynamically generate context-aware content and feedback, while gamification enhances motivation and retention through interactive, challenge-based learning. However, current approaches often treat these technologies in isolation, with gamification systems relying on static, one-size-fits-all designs, and LLM applications focusing narrowly on technical assistance without pedagogical alignment. Moreover, existing models lack the incorporation of structured curriculum frameworks, limiting both curriculum relevance and instructional coherence. Therefore, this paper proposes CyberGPL, a conceptual framework for Cyber Security Gamified Personalised Learning. Unlike traditional approaches, CyberGPL is built upon the Cyber Security Body of Knowledge (CyBOK) framework, ensuring structured curriculum alignment while delivering highly personalised and engaging learning experiences. The proposed CyberGPL framework integrates four key components: learner profiling and pathway mapping, AI-driven content generation using LLMs, a gamification and engagement engine, and an educator monitoring and analytics dashboard. These components work in synergy to deliver personalised, CyBOK-aligned content through narrative-driven quests, adaptive assessments, and real-time feedback. Educators are supported with actionable analytics and oversight tools to ensure instructional quality and data-informed decision-making. By bridging pedagogical structure with advanced AI and motivational design, CyberGPL offers a significant advancement in the design of intelligent, engaging, and effective cyber security education systems.