The Deep Observability market is evolving rapidly, addressing the increasing need for advanced monitoring and analytics across IT and cloud infrastructures. Deep observability enables organizations to derive actionable insights from vast amounts of telemetry data. It goes beyond metrics, events, logs, and traces to provide granular data visibility across network, security, and application layers and ensure system performance, security, and reliability. The observability pipeline serves as the data backbone, collecting, processing, and routing telemetry data to the right destination and eliminating the need to centralize the data into a single location. With an increase in cloud-native architectures and microservices, it has been a challenge for organizations to be in complete control of lateral or east-west traffic between cloud instances. As most of this traffic is encrypted, it is possible for attackers to hide behind this veil and infiltrate the network. Deep observability solutions help organizations improve their security posture by providing complete visibility into not just north-south data movement but also encrypted east-west traffic. This allows organizations to build a solid foundation for a zero-trust framework. Major players in the Deep Observability space include Gigamon, Arista Networks, Keysight Technologies, Cribl, and Kentik. Other players like Splunk, Datadog, and Dynatrace play in the broader observability market. Emerging vendors like Grafana Labs focus on open-source observability tools that provide cost-effective solutions for small and medium-sized enterprises. This market is projected to grow significantly, driven by the rising complexity of distributed systems and the need for real-time, actionable insights. This paper will focus on the key aspects of Deep Observability and analyze how choosing the right solution based on an organization’s needs is crucial to managing risk and enhancing security in a multi-cloud environment to build cyber resiliency.

Author: Vinay Biradar