InfoWorld AI·
For years, Kubernetes held an almost mythic place in enterprise IT. It was positioned as the control plane for the future, the standard abstraction for cloud-native systems, and the platform that would finally free enterprises from infrastructure lock-in. To be fair, some of that was true. Kubernetes brought discipline to container orchestration, enabled portable deployment models, and provided architects with a powerful framework for managing distributed applications at scale. However, the market is changing, and so are enterprise expectations. The question is no longer whether Kubernetes is technically impressive. It clearly is. The question is whether it still represents the best fit for a growing number of mainstream enterprise use cases. In many cases, the answer is increasingly no. What we are seeing is not the death of Kubernetes but the end of its unquestioned dominance as the default strategic choice. Here’s why. Too operationally expensive As Kubernetes adoption grew, many o
Read full articleSimplify Kubernetes operations by managing storage, data protection, and disaster recovery for AI workloads, containers and VMs directly within the Red Hat OpenShift console. SANTA CLARA, Calif., May 12, 2026 […] The post Everpure’s Portworx Makes Data Management Native to Red Hat OpenShift appeared first on AIwire.
Microsoft and Google are adding new controls for AI agents, as enterprise IT teams try to keep up with tools that can access corporate data and act across business applications. Microsoft’s Agent 365, made generally available for commercial customers on May 1, is designed to help organizations discover, govern, and secure AI agents, including those operating across Microsoft, third-party SaaS, cloud, and local environments. Google’s new AI control center for Workspace, announced this week, focuses more specifically on giving administrators a centralized view of AI usage, security settings, data protection controls, and privacy safeguards within Workspace. The timing reflects a shift in enterprise AI use. Many companies are no longer just testing chatbots, but are beginning to use agents that can reach corporate systems and carry out tasks on behalf of users. Analysts said the shift changes how CIOs and CISOs should think about AI agents inside the enterprise. “By placing agent controls
Over the years, enterprise IT execs have gotten frighteningly comfortable having little control or visibility over mission-critical apps, from SaaS to cloud and even cybersecurity. But generative AI (genAI) and agentic systems are taking that problem to a new extreme, with vendors able to dumb down a system IT is paying billions for without so much as a postcard. It’s not necessarily that AI changes are made to boost profits or revenue. Even if we accept the vendor argument that such changes are in the customer’s interest, companies still need for their systems to do on Thursday what they did on Tuesday, let alone what they did when the purchase order was signed. Alas, that is no longer the case. Consider a recent report from Anthropic that detailed a lengthy list of changes the company made to some of its AI offerings — including one that explicitly dumbed down answers — without asking or telling customers beforehand. The report describes various changes the Anthropic team made on t
A supply chain attack on SAP-related npm packages has put fresh scrutiny on the developer tools and build workflows that enterprises rely on to produce software. The campaign, referred to as “mini Shai-Hulud,” affected packages used in SAP’s JavaScript and cloud application development ecosystem. The malicious versions added installation-time code that could steal developer credentials, GitHub and npm tokens, GitHub Actions secrets, and cloud credentials from AWS, Azure, GCP, and Kubernetes environments. Researchers at SafeDep, Aikido Security, Wiz, and several other security firms said the affected packages included mbt@1.2.48, @cap-js/db-service@2.10.1, @cap-js/postgres@2.2.2, and @cap-js/sqlite@2.2.2. The suspicious versions were published on April 29 and were later replaced by safe releases. The malware encrypted stolen data and sent it to public GitHub repositories created from victims’ own accounts, according to the researchers. It also used stolen GitHub and npm tokens to add ma
If you’re using Kubernetes, especially a managed version like Azure Kubernetes Service (AKS), you don’t need to think about the underlying hardware. All you need to do is build your application and it should run, its containers managed by the service’s orchestrator. At least that’s the theory. However, implementing a platform that abstracts your code from the servers and network that support it brings its own problems, and a whole new discipline. Platform engineers fill the gap between software and hardware, supporting security and networking, as well as managing storage and other key services. Kubernetes is part of an ecosystem of cloud-native services that provide the supporting framework for running and managing scalable distributed systems, including the tools needed to package and deploy applications, as well as components that extend the functionality of Kubernetes’ own nodes and pods. Key components of this growing ecosystem are the various service meshes. These offer a way to m
Running databases on Kubernetes is popular. For cloud-native organizations, Kubernetes is the de facto standard approach to running databases. According to Datadog, databases are the most popular workload to deploy in containers, with 45 percent of container-using organizations using this approach. The Data on Kubernetes Community found that production deployments were now common, with the most advanced teams running more than 75 percent of their data workloads in containers. Kubernetes was not built for stateful workloads originally—the project had to develop multiple new functions like StatefulSets in Kubernetes 1.9 and Operator support for integration with databases later. With that work done over the first 10 years of Kubernetes, you might think that all the hard problems around databases on Kubernetes have been solved. However, that is not the case. Embracing database as a service with Kubernetes Today we can run databases in Kubernetes successfully, and match those database workl
