Why proprietary software isn’t a retrospective trend but a trap for failureThink proprietary softwa…

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As automation practices mature, the workflows they need to support naturally grow more complex—there are more teams, trigger types, decision points, and AI recommendations to account for. Automation orchestrator is a composable workflow canvas for Red Hat Ansible Automation Platform that enables teams to weave their existing job templates and automation libraries together with logic nodes, event-driven triggers, and AI agent recommendations into more complex IT operations workflows. Red Hat is now making the new automation orchestrator generally available as an add-on for Red Hat Ansible Autvia Red Hat Blog ift.tt/kqwbpn1

For a network that helps the financial community securely exchange payment instructions representing the equivalent of the world's GDP every 3 days, security isn't just a concern, it's the number one priority. At Red Hat Summit 2026, Praveen Siddu, Senior Platform Architect at Swift, and Otmane Benali, Container Platform Product Owner at Swift, alongside Ryan Riley, Senior Solutions Architect at Red Hat, shared how they modernized container platform security. Their journey details how Swift, a financial cooperative connecting over 11,500 institutions in more than 200 countries, established zervia Red Hat Blog ift.tt/hEj9rgK

AI teams struggle because data, experiments, models, and deployments often live in separate systems. The DagsHub AI quickstart for Red Hat OpenShift AI gives teams a way to manage dataset versioning, annotation, experiment tracking, model registry, and deployment workflows in a single OpenShift-based environment.Developing robust AI solutions demands managing a complex ecosystem of data, experiments, and models. One of the primary hurdles data science teams face is the fragmentation of their initial workflows. To build effective and accurate models, teams must be able to seamlessly connect mulvia Red Hat Blog ift.tt/nQgARV1

The telecommunications industry is considering one of its most consequential security debates in decades. Amid growing network complexity, 1 narrative argues that proprietary software is inherently more secure than its open source counterparts. Its central tenet is that the source code is not publicly accessible. The logic sounds intuitive: If attackers can’t read the code, they can’t find vulnerabilities. In the era of AI, that argument isn’t just outdated, it’s operationally dangerous.This blog challenges that narrative. It looks at how AI has changed the economics of vulnerability dvia Red Hat Blog ift.tt/XeQzvTM

In Part I and Part II of this series, we explored why efficient inference is becoming an economic necessity and why sovereign AI requires infrastructure independence. But who should build the infrastructure that powers AI?AI infrastructure influences far more than performance. It determines how quickly organizations can adopt new hardware, integrate emerging models, and adapt as the AI capabilities continue to advance.AI is moving too fast for one companyEvery layer of AI technology is changing simultaneously. New foundation models are released constantly, accelerator vendors continue introducvia Red Hat Blog ift.tt/hucsi6d