The confluence of advances in machine learning, code translation, and automated security analysis presents transformative potential for modern software engineering. This article proposes a conceptual, integrated framework that leverages unsupervised code translation, large language models (LLMs) for code editing, and AI-enabled vulnerability detection, to enhance both code portability and security assurance. Drawing upon recent work in unsupervised programming language translation (Lachaux, Roziere, Chanussot & Lample, 2020), performance‑improving code edits via learning (Shypula et al., 2023), zero‑shot vulnerability repair with LLMs (Pearce et al., 2023), and broader surveys of AI‑assisted big code understanding (Wong et al., 2023), the framework emphasizes an iterative pipeline: translate → adapt → secure. Additionally, we integrate insights from research on AI-based vulnerability detection in enterprise contexts (Rajapaksha et al., 2023; Behfar, 2023), semantic deduplication of security findings (Gulraiz, 202x), and compliance automation (Amaral et al., 2021; Areo, 2021), to support compliance and operational deployment. Through detailed theoretical elaboration, we examine potential benefits, challenges, limitations, and future directions. We argue that such a unified pipeline can significantly reduce the human burden in cross-language migration and bolster resilience against security vulnerabilities, while acknowledging risks such as over-reliance on AI, calibration, and regulatory compliance.

AI-assisted programming, code translation, vulnerability detection, software security

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