AI-Native Information Technology Architecture and Emerging Organizational Issues: Cloud Governance, Cybersecurity Resilience and Digital Trust

Abstract

AI-native IT architecture is fundamentally reshaping the modern enterprise and accelerating the shift toward decentralized autonomous networks. While this transition unlocks unprecedented capabilities, it also introduces a wave of complex security vulnerabilities. Today, enterprises face a tripartite challenge which are securing hyper-connected cloud environments, mitigating sophisticated cyber threats and ensuring algorithmic accountability. To address these interconnected issues, this study synthesizes recent literature (2022–2024) from Scopus and Web of Science bridging three disciplines typically studied in isolation of cloud governance, cybersecurity resilience and digital trust engineering. Technological adoption alone is insufficient for sustainable digital transformation. Organizations must pivot from reactive, post-deployment compliance to embedding proactive governance directly into their system architecture from inception. By examining dual-loop governance models and policy-as-code frameworks, this paper translates high-level theories into actionable deployment pathways and measurable performance indicators. Furthermore, as stringent regulations like the EU AI Act take effect, cyber defenses must evolve in tandem. Enterprises urgently require predictive resilience frameworks powered by Explainable AI (XAI) to pierce algorithmic opacity which is an effort that will rely heavily on quantitative trust assessments and decentralized identity infrastructures. Finally, to validate these concepts beyond theoretical constructs, we ground our proposed model in real-world application scenarios across the healthcare, finance and critical infrastructure sectors. Ultimately, for AI-native enterprises to achieve long-term viability, they must successfully balance rapid, autonomous innovation with adaptive security and verifiable stakeholder trust.