Operations | Monitoring | ITSM | DevOps | Cloud

Government AI strategies are ultimately constrained or enabled by the infrastructure beneath them. Agencies that can continuously validate controls, maintain visibility, and automate policy enforcement will be better positioned to deliver AI capabilities securely, responsibly, and at scale.

Australia’s critical infrastructure landscape has changed significantly. The Security of Critical Infrastructure Act 2018 (SOCI Act) has evolved from a framework focused primarily on identifying critical assets and reporting incidents into a broader risk-management and operational-resilience regime. The 2024 reforms reinforced that direction, increasing the focus on the systems, data, and technology dependencies that underpin Australia’s essential services.

In November 2025, Progress Chef outlined their plans to deprecate Chef Infra Server; which will reach end of life effective November 30, 2026. For Infra Server users, this means you have an opportunity to evaluate your infrastructure solutions to make sure it meets enforces internal or regulatory compliance policies, lets you manage infra everywhere it lives today, and sets you on the right path for the future.

In conversations with customers, compliance rarely starts as a tooling discussion. It usually starts with a practical concern: how do we keep moving, keep modernizing, and still know that our infrastructure is aligned to the policies we are accountable for? That question comes up because the pressure is real. Teams are managing hybrid estates, responding to audit requests, dealing with drift, supporting new platforms, and being asked to move faster without creating more risk.

Financial services regulators increasingly expect organizations to demonstrate that controls operate continuously, not just on audit day. Yet many firms still rely on point-in-time reviews and manually assembled evidence, creating compliance gaps, operational overhead, and unnecessary risk exposure. Continuous configuration enforcement helps address both the regulatory and operational challenge by generating evidence as changes occur. Back to top.

The tech world has been building up towards the shift to a fully agentic development life cycle for a few years now. AI is changing how software gets built. Across the Puppet ecosystem, we’re seeing a shift toward more agentic engineering workflows. AI helps generate code, shape documentation, and accelerate how Puppet modules evolve.

End-of-life (EOL) systems rarely create risk all at once. The real challenge is what happens as each layer of support starts to fall out of sync.

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.