Operations | Monitoring | ITSM | DevOps | Cloud

As AI accelerates change, infrastructure environments continue to diversify, and regulatory expectations increase, organizations face growing pressure to maintain security, compliance, and operational accountability at scale. Back to top.

If you've been watching the Puppetlabs namespace on the Forge (or waiting on a pull request you opened) you've likely noticed that the pace of inclusion of community pull requests and fixes into the module releases has slowed over the last several cycles. That isn't how we want to serve the community that built these modules with us. The team has been reviewing how we can improve and this post is our commitment to do better. Here are the steps we're taking now: Back to top.

After more than a decade of "cloud-first" strategies, a major counter-trend is reshaping enterprise IT: the systematic repatriation of workloads, data, and infrastructure back to on-premises and private data centers.

The Puppet Observability Data Connector is a premium Forge module included with Puppet Enterprise Advanced (PEA). This module provides a deeper dive into your Puppet agent reports. Visualizing these metrics gives you a great way to identify what is healthy and unhealthy in your environment.

AI is transforming how infrastructure and platform teams design, deploy, and operate systems. As organizations move from experimentation to production, a clear pattern is emerging. AI can decide what should change, but it cannot safely control how those changes are executed. This creates a gap in modern architectures. That gap is filled by a control plane. That control plane already exists in Puppet Enterprise Advanced.

On June 9, 2026, the OpenSSL project released patches covering 18 vulnerabilities across its supported releases. The headline flaw, CVE-2026-45447, is rated high severity and has the potential for remote code execution. Not too long ago, a security advisory with 18 vulnerabilities would have been routine. Microsoft’s Patch Tuesday provided a predictable cycle, and organizations operated with the expectation of a meaningful remediation window. That model is under pressure.

You’re scaling your IT infrastructure so you can do more — deploying across clouds and data center, adding servers, coding like crazy. Great! But how do you keep it all from falling apart? Policy as code is an approach to managing IT that strategically leverages infrastructure as code (IaC) and compliance as code to manage consistent policies across complex IT environments. Sounds perfect, right?

Most teams first adopt policy as code (PaC) in their delivery pipelines. If something breaks a rule, the system stops it before it goes live. That is useful as it helps catch problems early but in real world environments, the hardest issues to resolve do not come from changes that fail validation. They come from changes that happen later, elsewhere, or outside the pipeline entirely.

Claude Mythos may be wrapped in hype, but the core signal is real: AI is making vulnerability discovery much faster, which means defenders have less time than ever to patch and enforce secure configurations. The real risk isn’t just smarter models, it’s that security teams will face a flood of new findings while the window between disclosure and exploitation keeps shrinking.

In early 2026, two back-to-back Linux kernel exploits, Copy Fail (CVE-2026-31431) and Dirty Frag (CVE-2026-43284 & CVE-2026-43500), shattered assumptions about how quickly attackers can weaponize disclosed CVEs. Dirty Frag, a zero-day Linux vulnerability that affected most major distributions, had PoC exploits published within hours of its disclosure. It’s a stark reminder: the timeline between vulnerability disclosure and active exploitation has shrunk from weeks to hours.