Secure Software Engineering: A Synthesis of SSDLC, Devsecops, and Ai-Driven Threat Mitigation
DOI:
https://doi.org/10.62054/ijdm/0203.13Abstract
This study explores the critical convergence of software development and cybersecurity through a mixed-method approach combining systematic literature review, quantitative analysis, and case study evaluation to investigate the effectiveness of secure software development practices. As cyber threats grow in sophistication, traditional software engineering models prioritizing functionality and efficiency have proven inadequate in addressing evolving security challenges. The incorporation of Secure Software Development Lifecycle (SSDLC) and DevSecOps methodologies has emerged as a pivotal strategy, embedding security considerations from the earliest phases of development. This paper provides a comprehensive review of secure software development practices, core cybersecurity principles, regulatory compliance requirements, and technological innovations driving this integration. Drawing on insights from academic research, industry reports, and real-world applications, the findings emphasize the effectiveness of security-centric development paradigms, such as SSDLC and DevSecOps, in mitigating risks and strengthening software resilience. Furthermore, emerging technologies like artificial intelligence, blockchain, and automated security testing are reshaping secure development strategies by enabling predictive analytics, dynamic access control, and proactive vulnerability detection. Empirical results indicate that organizations adopting security-first frameworks report measurable reductions in system vulnerabilities and enhanced response times to incidents. This study underscores the imperative of embedding security within software engineering processes to ensure robust application design, safeguard user data, maintain system integrity, and reinforce the broader digital ecosystem. The findings offer valuable guidance for software developers, cybersecurity professionals, and organizational leaders seeking to enhance the security posture and long-term resilience of their software systems
Riferimenti bibliografici
Cyber.gov.au. (2025). Guidelines for software development. Australian Cyber Security Centre. Retrieved from https://www.cyber.gov.au
Fortune Business Insights. (2025). Cybersecurity market size, share & analysis—Global report 2032. Retrieved from https://www.fortunebusinessinsights.com/industry-reports/cyber-security-market-101165
Gutzmer, K. (2021). Blockchain technology and software security. Cybersecurity Insights Journal, 14(3), 55–68.
Howard, M., & Lipner, S. (2006). The security development lifecycle: A process for developing demonstrably more secure software. Microsoft Press.
Kudriavtseva, N., & Gadyatskaya, O. (2022). Machine learning for secure software development: A survey. Journal of Cybersecurity Research, 9(1), 1–17.
Kudriavtseva, N., & Gadyatskaya, O. (2022). Machine learning for secure software development: A survey. Journal of Cybersecurity Research, 9(1), 1–17.
McGraw, G. (2006). Software security: Building security in. Addison-Wesley.
Mordor Intelligence. (2025). Cybersecurity software market—Share & growth projections. Retrieved from Mordor Intelligence
National Institute of Standards and Technology (NIST). (2022). Secure software development framework (SSDF). U.S. Department of Commerce.
OWASP. (2022). Secure coding guidelines. Retrieved from OWASP
Rahman, M., Williams, P. A. H., & Gill, A. Q. (2021). DevSecOps: Towards a model of agile security in software development. Information and Software Technology, 134, 106558. https://doi.org/10.1016/j.infsof.2021.106558
Revelo. (2025). Security in software development: Emerging trends. Retrieved from Revelo
Sommerville, I. (2020). Software engineering (10th ed.). Pearson.
Sun, X., & Li, J. (2022). A survey of software security testing techniques. Journal of Systems and Software, 186, 111170.
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