AI Powered Cyber Resilient Cloud Infrastructure: Real Time Threat Detection and Secure Data Pipelines for Public and Enterprise Systems

The rapid digitalization of public institutions and enterprise ecosystems has intensified reliance on cloud infrastructure while simultaneously expanding the cyber threat surface. Hybrid and multi cloud environments now process mission critical workloads, financial transactions, citizen services, and industrial telemetry in real time. However, traditional perimeter based security architectures and static monitoring systems are insufficient against advanced persistent threats, zero day exploits, and insider attacks. This paper proposes an AI Powered Cyber Resilient Cloud Infrastructure (AI CRCI) framework that integrates real time threat detection, adaptive security orchestration, and secure data pipeline management for public and enterprise systems. The architecture leverages containerized microservices, Kubernetes orchestration, encrypted service meshes, and AI driven anomaly detection engines to deliver continuous monitoring, rapid incident response, and policy driven compliance automation. The framework embeds secure data pipelines that enforce encryption, schema validation, and integrity verification from ingestion to analytics layers. AI models analyze behavioral logs, network telemetry, and API activity to identify deviations in real time, while automated remediation workflows isolate compromised workloads. The architecture incorporates a Cyber Resilience Index (CRI) derived from threat detection precision, mean time to remediation, and compliance stability metrics. Cross domain validation across finance, smart grid infrastructure, and public service platforms demonstrates measurable reductions in detection latency and improved operational continuity. The proposed model provides a scalable blueprint for designing cyber resilient cloud ecosystems capable of supporting secure digital transformation in both public and enterprise sectors.