The Hidden Risks of AI-Generated Code in Modern Software Development
The integration of Large Language Models (LLMs) into the software development life cycle (SDLC) has catalyzed a paradigm shift in engineering productivity. However, the transition toward “machine-augmented generation” has outpaced traditional security validation frameworks. This article explores the emerging “Velocity–Security Gap,” focusing on the systemic risks posed by AI-generated code, particularly within Small and Medium Enterprises (SMEs). We argue that the lack of contextual reasoning in LLMs, coupled with reduced human-in-the-loop oversight, necessitates a fundamental shift toward automated, real-time DevSecOps interventions.
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