Operations | Monitoring | ITSM | DevOps | Cloud

Over the past decade, significant progress has been made in incident detection and response across enterprise IT environments. Observability platforms, event correlation engines, and AIOps capabilities have measurably reduced mean time to detection and mean time to resolution. Operational teams are better equipped to identify anomalies, triage alerts, and coordinate remediation across increasingly complex architectures.

Your developers install agent plugins every day: pulling from unmanaged GitHub repos, copying Cursor commands out of Slack, pointing Codex at a personal Git fork. Each of those is a new, uncontrolled distribution channel inside your software development lifecycle, and your platform team has zero visibility into any of it. A plugin is not a preference file. It is executable software, and right now it’s arriving on developer machines with no versioning, no provenance, and no audit trail.

Feature flag migrations have a reputation problem. Ask anybody who’s been through one before and you’ll hear the stories, usually from someone still a little frustrated about a bad cutover, with a postmortem or two to show for it. The reputation is mostly undeserved. While the risks are real, they’re well understood and easily controlled. Getting a migration right doesn’t require a big coordinated effort.

Retention policies apply to locations. Retention labels apply to items. Both live in Microsoft Purview, both retain content, and admins regularly use the wrong one. What each actually does and when to use which.

If your organization handles the personal information of people in South Africa, POPIA compliance is not optional. The Protection of Personal Information Act has been fully enforceable since 1 July 2021, and the Information Regulator now backs it with administrative fines of up to ZAR 10 million. The requirement your IT and security teams own most directly is security safeguards under Section 19, and it is the first place a regulator looks after a breach.

This is the first in a series we’re calling AI ROI Dispatches, where we share stories from CloudZero and our customers on tying AI spend to real business outcomes.

Eight new Azure recommendation types now scan your environment for idle, unattached, and over-provisioned resources, then tell you exactly what to cut.

Platform engineering has become one of the most discussed topics in cloud native infrastructure. Yet despite the rising focus, most conversations around platform engineering skip over the uncomfortable truths. What actually works at scale? When should you build versus buy? And how do you avoid the traps that trip up even experienced teams?

AI has made writing code fast, and you can feel it. Commits are up, pull requests are up, new repos spin up over a weekend, and your engineers swear they are faster. But where are all the new products? If every team really got faster, the software you use every day should be getting visibly better. AI helped your engineers ship more code. It didn't help your team ship more products.

AI adoption has accelerated faster than most organizations expected. What started with chatbots has quickly evolved into AI systems capable of making decisions across enterprise environments, with the promise of faster service and more efficient teams. But many organizations are discovering an unexpected challenge: as AI usage expands, costs become harder to predict. Most AI platforms operate on token-based pricing models.