Deep Learning-Enhanced Hybrid AI and Zero Trust Framework for Secure Cloud-Centric Software Development

Abstract

As organizations embrace cloud technologies and software-driven operations, they gradually become targets for adversaries who take advantage of intricate supply chains, miss configurations, and the proliferation of identities. Traditional, reactive defenses that focus on perimeter security struggle to counter advanced persistent threats, ransom ware attacks, and breaches in software supply chains. This paper introduces a hybrid cyber security framework that integrates Artificial Intelligence (AI), deep learning, Zero Trust Architecture (ZTA), and cloud-native security practices to safeguard the software development lifecycle (SDLC) comprehensively. The framework offers several key benefits: (i) AI-enhanced threat intelligence and risk assessment at every stage from code creation to deployment and runtime; (ii) deep learning-supported integrity checks for CI/CD provenance and verifiable change management; (iii) continuous authentication and least-privilege authorization based on Zero Trust principles; and (iv) cloud security posture management that aligns with shared responsibility models. We outline the threat model, architecture, data flows, and a detailed implementation plan. Our evaluation employs a mixed-methods approach, incorporating a systematic literature review, industry surveys, and a case study in the healthcare sector. We also correlate our controls with top industry standards, including NIST SP 800-207/53/218, CSA CCM, and ISO/IEC 27001. The results show significant improvements in detection accuracy, a reduction in mean time to respond (MTTR), robust tamper-evident pipelines, and lower risks of lateral movement. Our contributions include a reference architecture, a maturity model, and compliance mapping designed for secure cloud-centric development.