Why Now Is the Time for AIOps with Deep Observability

Network observability is hot these days, as evidenced by the growing number of infrastructure entrants into the category. Cisco's recently completed acquisition of Splunk as well as the momentum behind generative AI and its disruptive potential point to an evolution of network and security management. AIOps is not a new concept, but infusing it with deep observability has the potential to deliver many advantages to organizations of all sizes.

From my perspective, Gigamon stands out as a company on a rapid ascent. The Gigamon Deep Observability Pipeline, fed with rich telemetry and ecosystem partnership integrations, is uniquely positioned to unlock new business value. Gigamon has been on a tear lately, launching a differentiated Gigamon Precryption™ technology in late 2023. Gigamon Precryption provides threat visibility into encrypted cloud traffic across a diverse set of threat detection tools, eliminating the cost and complexity of key management and virtual network routing for observability. The company's architecture supports visibility and scale cross-domain - spanning on-premises, hybrid and multi-cloud infrastructure, and containers. This is especially important given the heterogeneous nature of public cloud network service architectures and the visibility challenges that result with widespread multi-cloud adoption aimed at cost containment and discrete feature availability.

Furthermore, Gigamon recently announced an integration with Cribl, another telemetry pipeline provider, that aims to provide joint customers with deeper observability across hybrid cloud infrastructure. It marries GigaVUE Cloud Suite™ with Cribl Stream in a manner that formats and delivers telemetry intelligence based on how tools ingest data. The partnership is a great example of Gigamon's ability to interface with a host of threat intelligence and observability tools that aggregate multiple sources of telemetry to enrich data and ultimately lower risk and mitigate incident blast radius.

Next-generation AI workloads are thirsty for high-quality data. Gigamon meets this need, backed by their 20-year record of delivering highly performant network and security infrastructure. They start with immutable network data, then remove noise and transform and enrich that data. This enriched, high-quality data enables large language models, the critical underpinnings of AI, to operate at maximum efficiency, leading to better outcomes. Specifically, Gigamon provides visibility into lateral East-West traffic, something often invisible to many security tools. It also leverages actionable network-derived intelligence and insights to uncover lurking threats that can affect network availability and weaken resiliency.

From a deployment perspective, Gigamon complements existing cloud, security, and observability tools, eliminating the need to rip and replace prior IT and OT investments. As a result, enterprises can realize higher levels of business agility, ensure security across highly diverse deployments, eliminate tech stack silos, and contain management cost given today's uncertain economic environment.

At a high level, Gigamon is supercharging AIOps with deep observability, and in the process is enabling organizations to more efficiently secure and manage hybrid cloud infrastructure at scale and realize the resulting value.

I recently discussed some of these details with Michael Dickman, the chief product officer at Gigamon, and you can learn more here.

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