Building Trustworthy AI: Explainability and Security in Modern Cloud-Native Data-Driven Ecosystem Platforms
DOI:
https://doi.org/10.15662/IJEETR.2026.0802012Keywords:
Artificial Intelligence, Explainable AI, Cloud-Native Architecture, Data-Driven Systems, AI Security, Trustworthy AI, Zero Trust Security, Model Interpretability, Privacy Preservation, Distributed Systems, Microservices, AI Governance, Cybersecurity, Data Integrity, Ethical AIAbstract
The rapid adoption of Artificial Intelligence (AI) within cloud-native, data-driven ecosystems has significantly transformed modern enterprise platforms across industries. However, the increasing reliance on AI-driven decision-making systems raises critical concerns related to trust, transparency, security, and ethical accountability. This paper explores the design and implementation of trustworthy AI systems by integrating explainability, security, and governance mechanisms within cloud-native architectures. It highlights the importance of Explainable AI (XAI) techniques in enhancing model interpretability, enabling stakeholders to understand, validate, and trust automated decisions. Additionally, the study examines security challenges such as data breaches, adversarial attacks, and model vulnerabilities, proposing robust mitigation strategies including secure data pipelines, encryption, and zero-trust architectures. The research further investigates how modern cloud-native technologies—such as microservices, containerization, and distributed data platforms—support scalable, resilient, and secure AI deployment. A comprehensive framework is proposed that combines explainability models, privacy-preserving techniques, and real-time monitoring to ensure reliability and compliance in AI systems. This framework aims to bridge the gap between high-performance AI and ethical, transparent operations. The findings demonstrate that integrating explainability and security into AI lifecycle management not only improves system trustworthiness but also enhances regulatory compliance and user confidence. Ultimately, the paper contributes to advancing trustworthy AI practices for next-generation cloud-based intelligent platforms.References
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