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Corresponding Author ORCID Identifer

Sainag Nethalahttps://orcid.org/0009-0002-1180-4058

Section

Regular Issue

Abstract

Generative Artificial Intelligence (Generative AI) models are now broadly used for academic writing and software development for the sake of productivity and efficiency. Concerns on the impact of Artificial Intelligence (AI) tools on academic integrity and cybersecurity grow bigger with time. Generative AI is being used for code generation, editing, and review, raising ethical and security challenges. A big concern is the involuntary introduction of vulnerabilities into codebases. They can reproduce known bugs or malicious code that compromise software integrity because of the way models are trained: on large datasets. The tools may also pose additional security threats often encountered during software development. AI models trained on public data will generate code that resembles copyrighted content, creating ownership and legal grey areas. Use of AI to delegate coding increases potential adversarial attacks and model poisoning. Addressing these challenges would therefore call for a balanced approach towards AI integrating into software development. Secure coding practices, thorough testing, continuous monitoring, and collaboration between developers, security professionals, and AI researchers should be balanced. Strong governance, regular audits, transparency in AI development, and the embedding of ethical standards in AI usage will help in ensuring it is safe and effective. Generative AI should be seen as a tool to enhance, not replace, human expertise in software development. While automation can streamline workflows, developers must remain vigilant to detect and mitigate AI-induced vulnerabilities. A proactive approach that combines human oversight with AI-driven efficiency will be key to securing the future of software development.

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