Operations | Monitoring | ITSM | DevOps | Cloud

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.

To succeed in the AI era, customers need fast, easy access to security solutions that can harness the power of agentic AI and deliver business outcomes. They need seamless access to their data for faster threat detection, simpler incident response, and reduced risk. They need technology vendors to work together and not in silos.

The Tier 2 SOC Analyst or the Incident Responder (often hailed as the "Sherlock Holmes of the network") faces an increasingly complex and relentless digital landscape. In a world where analysts are being overwhelmed by alerts, held back by fragmented, manual tooling and inefficient workflows, incident responders are charged with the critical task of identifying, analyzing, and mitigating security threats.

Modern systems don't fail because engineers lack skills; they fail because teams can't see why systems are failing at all or can’t see why they’re failing fast enough. Often, the problem isn't a lack of tools — it's a lack of clear, connected visibility across data, teams, and systems. This is where observability transforms how organizations operate. It's no longer just about keeping systems running.

At.conf25, we took a bold step forward—introducing the concept of MachineGPT, which brings the power of generative AI to one of the most overlooked resources: machine data. MachineGPT speaks the language of machines. Just like ChatGPT learned the grammar of words and sentences to understand questions and respond in human language, MachineGPT can learn the hidden “grammar” of how systems behave through machine data.

The digital landscape has transformed dramatically, and with it, the demands on our systems have grown exponentially. Traditional monitoring tools struggle to provide sufficient insight into complex, distributed, cloud-native environments. Observability is the answer, moving beyond merely knowing "what" is happening to understanding "why" it's happening, and its impact on user experience and business outcomes.