Operations | Monitoring | ITSM | DevOps | Cloud

Every enterprise contract I’ve signed in the last two years has the same clause. “Vendor will not use Customer Data to train machine learning models.” Sometimes it’s a paragraph. Sometimes it’s a whole section. The language varies but the intent is identical: don’t feed our production data into your AI. I get it. I sign the same clause as a vendor. But here’s what’s been bothering me: that clause is a promise, not an architecture.

If there was a theme this month, it was making the hard parts of a telemetry pipeline less risky. For SIEM customers, we shipped an ASIM-native Microsoft Sentinel destination and automatic OCSF mapping in Pipeline Intelligence, two of the most-requested pieces for teams routing security data through Bindplane. On the platform side, we added config rollback, which turns "I changed something and now it is behaving differently" into a one-click trip back to a known-good version.

SIG Network introduced Ingress in 2015 as a minimal way to expose HTTP services from a cluster. That simplicity was an advantage at a time when most workloads were HTTP, clusters were single-tenant, and the occasional gap could be papered over with a vendor annotation.

As teams scale AI and agentic workloads, log volumes can grow fast. That growth can force teams into a difficult trade-off: Keep logs searchable in their existing workflows, or store them cost-effectively for longer periods. For teams that rely on logs during incident response, compliance reviews, and long-running investigations, losing either affordability or searchability can slow down troubleshooting. Datadog and ClickHouse are partnering to help remove that trade-off.