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Secure Code Generation and Software Engineering With Generative AI
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Author(s): Hanh Le (University of the Cumberlands, USA)
Copyright: 2027
Pages: 40
Source title:
Generative AI for Cyber Risk Management
Source Author(s)/Editor(s): Yassine Maleh (Sultan Moulay Slimane University, Morocco), Lahby Mohamed (Hassan II University, Casablanca, Morocco)and Ahmed A. Abd El-Latif (Prince Sultan University, Saudi Arabia)
DOI: 10.4018/979-8-3693-8397-1.ch007
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Abstract
The integration of generative AI into software engineering is transforming how developers write and secure code. Tools like GitHub Copilot and ChatGPT enhance productivity but also introduce risks to code quality, security, and accountability. This study explores how generative AI intersects with secure software engineering by examining its role within the Secure Software Development Life Cycle (SSDLC). It investigates how AI-generated code can propagate vulnerabilities and how prompt engineering may mitigate these risks. Emphasis is placed on the importance of explainability, code auditing, and human oversight in AI-augmented development environments. Through case studies in DevSecOps (Prates & Pereira, 2025), the paper illustrates both the promise and the pitfalls of AI-assisted coding. Findings underscore the need to evolve traditional practices to ensure safe, transparent, and ethical AI integration, offering actionable strategies for aligning intelligent automation with secure development standards.
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