AI-Driven Telemetry Analytics for Predictive Reliability and Privacy in Enterprise-Scale Cloud Systems

The exponential growth of distributed and cloud-native systems has amplified the complexity of telemetry data collection, processing, and analysis across enterprise environments. While existing observability tools such as Prometheus, AWS CloudWatch, and Datadog provide valuable insights, they rely heavily on static thresholds and manual tuning limiting scalability and responsiveness in dynamic workloads. This paper proposes an AI-driven telemetry analytics framework that unifies predictive reliability and privacy-preserving observability for large-scale enterprise systems. The framework employs machine learning–based anomaly detection and cross-layer correlation of metrics, traces, and logs to predict service degradation before it impacts critical business operations. A privacy-preserving data pipeline ensures compliance with enterprise governance policies and emerging data protection regulations (e.g., GDPR, CCPA). Experimental evaluation within hybrid and multi-cloud environments demonstrates notable improvements in reliability metrics, including a 35% reduction in mean time to detect (MTTD), a 40% decrease in false positives, and a 30% reduction in monitoring overhead compared to traditional static monitoring systems. The findings emphasize the feasibility of AI-enhanced observability pipelines in enabling proactive fault management, operational resilience, and regulatory compliance in distributed enterprise architectures. This work contributes to bridging the gap between academic observability research and real-world industry adoption