Product Innovation and Security: Data Science-Driven Approaches to Secure Software Engineering
Keywords:
Product Innovation, Secure Software Engineering, Data Science, Machine Learning, Software Security, SSEMM, Vulnerability Detection, DevSecOps.Abstract
In the era of rapid digital transformation, balancing product innovation with robust softwaresecurity has become a critical challenge for development teams. This study explores how datascience-driven approaches can be effectively integrated into secure software engineering
References
Ahmed, I., Mia, R., & Shakil, N. A. F. (2023). Mapping blockchain and data science to the cyber threat intelligence lifecycle: Collection, processing, analysis, and dissemination. Journal of Applied Cybersecurity Analytics, Intelligence, and DecisionMaking Systems, 13(3), 1-37.
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Published
2025-08-20
How to Cite
Li Hong Wong – Charles, Bhavna Hirani, Meenakshi Alagesan. (2025). Product Innovation and Security: Data Science-Driven Approaches to Secure Software Engineering. Journal of Computational Analysis and Applications (JoCAAA), 34(6), 265–277. Retrieved from https://eudoxuspress.com/index.php/pub/article/view/3318
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