Operations | Monitoring | ITSM | DevOps | Cloud

Every investigation, detection, dashboard, and AI-assisted workflow depends on one thing: data that teams can trust. But as environments grow more distributed, the data behind those experiences gets harder to manage. New applications, cloud services, security tools, infrastructure, and network devices constantly generate machine data, and each new source can introduce new formats, missing fields, inconsistent mappings, and pipeline changes that require expert attention.

If you are leading technology strategy today, you face consequential choices about how to manage your enterprise telemetry. Your decisions determine not only where logs, metrics, traces, and events are stored, but also who controls how operational data is collected, shaped, governed, and put to work in an optimal way for the security, observability, analytics, and AI systems that power your business.

At.conf25, we announced our vision for Cisco Data Fabric, an architecture designed to help organizations unlock the value of machine data, fuel AI with trusted context, and support more intelligent and resilient operations. Today, that vision has become reality. Key Splunk Platform innovations including Machine Data Lake, Catalog, and Agent Launchpad, together with expanded Federated Search and Data Management capabilities, are now generally available.

Observability has always been about seeing clearly under pressure. But the pressure has changed. Applications are more distributed. Kubernetes environments keep expanding. Digital experiences depend on services, APIs, networks, third-party providers, and now AI models and agents that can make decisions faster than a human team can review every signal.

As we wrap up RSAC, one theme that repeatedly emerged in conversations with security leaders is that the modern enterprise has reached a critical inflection point where the velocity of machine-generated telemetry has outpaced the capacity of traditional architectures. This trend requires an approach that moves beyond the storage of information to the activation of it in ways that don’t simply exacerbate alert fatigue.

The world is generating data at a pace that defies the human ability to draw insights and comprehend. By 2028, we’ll reach almost 400 zettabytes of global data—with over 55% of it coming from machines talking to machines. For enterprises, this isn’t just a storage problem; it’s an existential challenge.

Imagine starting your day with a dozen disconnected apps where your calendar does not sync with your reminders, your maps do not know your appointments, and your contacts are not linked to your messages. You would constantly be scrambling, missing key details, and reacting late to what matters most. In our personal lives, we depend on tight integration to keep pace with the world. In business, the stakes are even higher.

In the modern enterprise, data is no longer just a byproduct of business; it is the lifeblood. However, we have moved beyond the era of simple transactional data. We are now living in the age of machine data.

We are excited to announce the release of the Splunk App for Data Science and Deep Learning (DSDL) version 5.2.3. Since 2018, DSDL has served as an innovation hub for custom AI integrations within Splunk. In 2025, the release of DSDL 5.2.0 introduced customizable Large Language Model (LLM) integrations, bringing Retrieval Augmented Generation (RAG) and Agentic AI workflows to Splunk users.