<![CDATA[Digital, web-based portfolios have become essential tools for students and job seekers to showcase technical skills. However, most current portfolios are static HTML/CSS sites, vulnerable to cyber attacks and unable to personalize content. This study introduces SecurePortfolio, a conceptual framework combining a machine learning security layer to block malicious traffic and a causal reinforcement learning module to adaptively recommend portfolio content. Simulation results show that the security component mitigates most threat vectors, while the personalization engine improves content relevance compared to static layouts. The framework offers a blueprint for intelligent, secure, and context-aware digital portfolios for graduates entering the workforce.]]>
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