GenAI-Powered Analytics in Software Development: Redefining Data Engineering and Security Practices
Keywords:
GenAI-powered analytics, data engineering, security practices, software development, vulnerability detection, compliance, resource optimization.Abstract
The integration of generative artificial intelligence (GenAI)-powered analytics into software development is revolutionizing data engineering and security practices. This study explores the transformative impact of GenAI on these domains, leveraging a mixed-methods approach to analyze data from industry case studies, academic literature, and expert interviews. The results reveal significant improvements in data processing efficiency, with a 30% reduction in pipeline execution time, and enhanced data quality, as evidenced by a 10.6% increase in accuracy and a 60% reduction in error rates. In security practices, GenAI-powered analytics demonstrated a 40% increase in vulnerability detection rates and a 75% reduction in mean time to detect (MTTD) threats. Compliance with security standards such as ISO 27001 and GDPR improved by 25%, while resource utilization metrics, including CPU and memory consumption, saw reductions of 35% and 28%, respectively. These findings highlight the ability of GenAI to automate tasks, optimize workflows, and enhance system resilience. However, challenges such as ethical concerns, data privacy, and the need for human oversight remain critical considerations. This study underscores the potential of GenAI to redefine software development, offering actionable insights for organizations seeking to leverage this technology for innovation and efficiency.
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