I’m genuinely excited about what we're rolling out with Copilot Editor, an update to our AI that’s truly packed with new capabilities designed to help you automate pipeline development. You can read about these capabilities here. I wanted to take a moment to share our thinking on a core principle that guides how we build, especially regarding the impactful, and sometimes daunting, world of generative AI. You see, our updated Cribl Copilot Editor is designed to work productively with humans in the loop. This isn't just a feature; it's a core tenet of our thinking about generative AI.
There's been a lot of buzz about AI agents or "agentic AI" completely automating complex enterprise processes. And don't get me wrong, the idea of full autonomy is compelling. But honestly? I don’t think these systems are quite there yet, especially for the intricate, often messy, world of log data. And they might never be, not in the way some imagine. Automation is certainly useful with telemetry data, but removing humans from the process can have significant consequences.
The repercussions of letting AI loose on your data without human oversight can be incredibly costly. Think about it: the telemetry data you, our customers, work with often feeds directly into mission-critical systems. Do you deploy software changes without proper testing? No, of course not. So why would you want an AI autonomously deciding to drop certain data from your logs, scale an environment up or down, deploying configurations with security holes, or inadvertently logging sensitive PII? That's where human oversight becomes not just a nice-to-have, but a crucial safety net. A human operator is there to hit the brakes.
That's precisely why we've designed Copilot Editor to be an intelligent partner for our operators. Our goal isn't to replace your expertise; it's to supercharge it. Copilot Editor acts as a sophisticated assistant, giving you the power to steer the AI. It’s built to be transparent, explaining upfront exactly what it's doing with the ability for you to modify its actions. You're always in control, never just pushing a button and hoping for the best.
Human operators bring an indispensable layer of implicit context and experience. You've got years of intuition, specific knowledge of your unique environment, and the ability to make judgments that an algorithm simply can't. For generative AI to suggest truly optimal, or even consistently correct, courses of action, it needs a depth of context that rarely exists in the raw, sprawling types of telemetry data our users work with every single day. Without that rich context, AI is operating in the dark, and its solutions will be, at best, suboptimal.
However, there are areas where tremendous context does exist, and that’s where AI becomes a game-changer. Take, for example, the task of mapping log data to a standardized schema like OCSF. The rules are well defined, the outcomes are clear but the task is cumbersome. In this scenario, Copilot Editor shines. It can jumpstart the process by suggesting the right functions to begin building and mapping pipelines, saving you immense time and effort. It’s about leveraging AI where it’s strongest to solve well-defined, complex problems.
At Cribl, we believe the future of AI in IT and security lies in augmenting human intelligence, not replacing it. Copilot Editor is built on this principle, ensuring you maintain control, gain efficiency, and unlock more value from your data, with the confidence that a skilled human is always in the driver's seat.
