Deep Learning Assisted DevSecOps Automation for High-Availability Enterprise Applications
DOI:
https://doi.org/10.63282/3117-5481/AIJCST-V8I3P102Keywords:
Devsecops, Deep Learning, Enterprise Applications, High Availability, Intelligent Automation, CI/CD Pipeline, Predictive Analytics, Cybersecurity, Autonomous Infrastructure, Cloud Computing, AI-Driven Security EngineeringAbstract
The rapid evolution of enterprise digital ecosystems has significantly increased the complexity of software deployment, infrastructure management, and cybersecurity operations. Traditional DevOps methodologies primarily emphasize automation, agility, and continuous delivery, yet modern enterprise applications require integrated security, intelligent orchestration, and high-availability infrastructure support to withstand dynamic cyber threats and operational disruptions. In this context, Deep Learning (DL)-assisted DevSecOps automation has emerged as a transformative paradigm capable of integrating predictive analytics, intelligent anomaly detection, automated remediation, and adaptive infrastructure management into enterprise software engineering pipelines. This research article investigates the role of deep learning technologies in enhancing DevSecOps automation for high-availability enterprise applications. The study explores the integration of neural network-driven security analytics, AI-powered CI/CD pipelines, intelligent vulnerability assessment, automated threat prediction, and self-healing cloud infrastructure mechanisms. The article further examines how deep learning models improve deployment efficiency, reduce downtime, strengthen cyber resilience, and optimize operational scalability in distributed enterprise systems. Comparative analysis between traditional DevOps and AI-assisted DevSecOps frameworks demonstrates that intelligent automation significantly improves incident response time, security posture, deployment accuracy, and infrastructure reliability. The proposed framework introduces a multilayered intelligent DevSecOps architecture incorporating continuous monitoring, reinforcement learning-based orchestration, predictive maintenance, and autonomous policy enforcement. Experimental observations and industry-oriented discussions indicate that organizations adopting DL-assisted DevSecOps architectures achieve enhanced operational continuity, reduced security risks, and improved service availability. The findings contribute to the growing body of research on intelligent software engineering and AI-driven enterprise automation, offering practical insights for cloud-native organizations, cybersecurity professionals, and enterprise architects.
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