Cloud-native architecture has changed the role of telemetry from ancillary to integral operating metric. This chapter provides a comprehensive guide to building secure, compliant, and AI-fortified observability systems to ingest, correlate, and analyze metrics, logs, and traces in real time. Based on open standards like OpenTelemetry and scalable building blocks such as Fluent Bit, Amazon Kinesis, and AWS Lambda, this architecture provides high-throughput, low-latency telemetry pipelines on distributed systems. Responding to the growing complexity and volume of observability data, this chapter integrates artificial intelligence into the telemetry life cycle. This includes log summarization using foundation models like Llama 2 in AWS Bedrock, anomaly detection using statistical and learning-based methods, as well as vector-based trace recall for root cause analysis. It also tackles security and compliance needs, including field-level redaction, tenant-aware telemetry boundaries, role-based access controls, and auditability features. While the implementation examples are from AWS, the patterns and practices underlying them have broad applicability across cloud environments. This research provides observability platform architecting and principal engineers with an exhaustive and flexible model for building secure and scalable observability platforms. In addition, such platforms have the potential to integrate artificial intelligence, reducing mean time to resolution and improving visibility into operations.