Operations | Monitoring | ITSM | DevOps | Cloud

Security and operations teams are collecting more telemetry than ever, and AI is accelerating that curve. IDC projects the world will generate 393.9 zettabytes of data in 2028, up from 149 zettabytes in 2024, with AI and machine learning workloads driving much of that growth. That growth forces a hard trade-off. Ingest everything, and you pay for it. Filter aggressively, and you risk missing the signal that matters.

If you’ve spent any time digging for insights in logs, you know the shape of the problem. A single log line might contain an IP address, a status code, a response time, and a user ID, but it’s all buried in one long, unstructured string. You know the information is there. Getting it into a field you can filter, group, or chart on is a different matter.

“AI-ready” is everywhere right now, attached to nearly every product in every category. The catchy label rarely means anything specific, just as additional questions are warranted when vendors claim to be “AI-native”. After fighting through all the marketing jargon, there needs to be a standard, not a slogan. And the definition changes depending on what the data is for. AI-ready for a data warehouse and AI-ready for live operational telemetry are not the same problem.