Federated Intelligence Platforms for Secure AI Operations across Cloud-Native Enterprise Ecosystems
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Abstract
The rapid adoption of artificial intelligence (AI), cloud-native technologies, and distributed enterprise ecosystems has transformed the operational landscape of modern organizations. Enterprises increasingly rely on federated intelligence platforms to enable secure, scalable, and collaborative AI operations across multi-cloud and hybrid infrastructures. This study explores the role of federated intelligence platforms in supporting secure AI operations within cloud-native enterprise ecosystems. Federated intelligence refers to decentralized AI architectures that allow multiple systems, organizations, or devices to collaboratively process and analyze data without directly sharing sensitive information. The research examines how federated learning, edge intelligence, cloud orchestration, and AI-driven cybersecurity mechanisms enhance data privacy, operational resilience, and intelligent decision-making. The study also investigates cybersecurity challenges associated with cloud-native infrastructures, including data breaches, insider threats, adversarial AI attacks, and compliance risks. Technologies such as zero-trust architecture, encryption frameworks, identity and access management, and automated threat intelligence are analyzed as critical components of secure AI ecosystems. A qualitative and analytical research methodology is employed to evaluate existing technological frameworks, implementation strategies, and operational challenges. The findings indicate that federated intelligence platforms significantly improve secure AI collaboration, scalability, data governance, and enterprise innovation while minimizing cybersecurity vulnerabilities. The study concludes that integrating federated intelligence with cloud-native security architectures is essential for sustainable and trustworthy AI operations in modern enterprise environments.
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