As machine learning (ML) continues to shape modern cybersecurity tools, computing education must evolve to help students critically engage with these technologies. One underrepresented topic in current curricula is daemon security, which involves protecting the long-running background processes common in Linux systems. These background processes (i.e., daemons) pose significant security risks in enterprise and cloud environments, yet receive little attention in operating systems or security courses. This paper reports on a semi-structured interview study with 22 academic and industry stakeholders. Using a Business Model Canvas-informed framework, we explored perspectives on ML-based approaches to daemon security and their educational implications. Participants conveyed both enthusiasm and apprehension. Although many acknowledged the potential of machine learning for real-time threat detection, concerns were also raised regarding automation, trust, and the adequacy of educational preparedness. Thematic analysis highlighted persistent gaps in instruction related to daemon awareness, system-level monitoring, and intelligent defense strategies. We propose practice-informed curriculum changes that directly address these instructional gaps, positioning this work as a concrete step toward aligning cybersecurity education with emerging system-level defense challenges.
Generative AI (GenAI) has introduced 'vibe coding', a transformative paradigm where developers use high-level natural language to iteratively generate software. While this approach offers significant gains in development efficiency (with some studies reporting productivity increase of up to 55%), it introduces critical concerns regarding reliability, security and long-term maintainability. This paper analyzes the dual impact of AI coding tools on the software development life cycle (SDLC), identifying recurring vulnerabilities such as insecure code patterns and supply-chain risks. Synthesizing findings from recent studies, this paper assesses how the conversational nature of vibe coding can lead to over-trust and technical debt. Finally, drawing on the NIST AI Risk Management Framework (AI RMF), we propose a governance framework that emphasizes human accountability and phase-dependent controls to ensure the trustworthy adoption of AI-assisted programming. The findings are intended to provide practitioners with a clear framework for governing the use of AI coding tools in their workflows.
