Red Hat News

Maintaining operational stability while securing infrastructure against an ever-growing list of vulnerabilities is a daily balancing act. Red Hat Lightspeed provides the core services required to manage your infrastructure at scale.However, getting the most out of these services requires making a strategic decision: How should your infrastructure connect to Red Hat Lightspeed?The process of choosing a deployment architecture goes beyond engineering; it must also take business strategy into account, as it affects your data privacy, infrastructure costs, and operational agility. Red Hat Lightspe

AI is rapidly transforming how system administrators and developers interact with their environments. In 2025, we introduced the command line assistant, an optional tool available in Red Hat Enterprise Linux (RHEL) that allows users to ask questions and get natural language troubleshooting guidance right from the terminal.We're now announcing the next step in our AI-driven systems management journey—the availability of the goose AI agent in the RHEL extensions repository for RHEL 9.8 and RHEL 10.2. A project of the Agentic AI Foundation, goose is a flexible, open source AI agent that can be

Every telecommunications service provider is operationalizing AI right now. Use cases include customer care-bots, network operation co-pilots, and managed AI-as-a-service (AIaaS) for external enterprise customers and others. The uncomfortable part is correlation of use case with business case, where the key factor is the cost of the AI accelerator, whether that's a graphics processing unit (GPU), tensor processing unit (TPU), or neural processing unit (NPU). Cost per inference decides whether these AI accelerators improve profit margins or erode them; to keep costs down, the AI model you sele

The velocity at which new features are added and applications evolve directly drives up architectural complexity. This relentless pace isn't just driven by the desire for new features; it is increasingly forced by the constant discovery of new vulnerabilities. Patching a single critical dependency can trigger a cascade of required updates and unexpected architectural changes as each product and solution comes with a myriad of implementation options and various features to enable or disable. Tailoring this complexity to your specific environment makes it challenging to guarantee every component

The financial services industry, like many other sectors, is aiming to make best use of their hardware in the age of resource-intensive AI workloads. Financial services companies have come to rely on the advantages of a container-based architecture, but may worry that container orchestration platforms add performance penalties that could exacerbate resource constraints. The results of a new STAC-AI™ LANG6 (Inference-Only) audit, the industry-standard benchmark for evaluating large language model (LLM) inference performance in financial services, may help assuage those worries when it comes t

IBM, Red Hat and Palo Alto Networks Expand Project Lightwell to Help Organizations Respond to Software VulnerabilitiesRed Hat, IBM, and Palo Alto Networks have expanded Project Lightwell to combat AI-driven threats. The collaboration delivers rapid, network-level virtual patching alongside open-source software remediation to securely fix vulnerabilities. Learn more Ask Noah Show - Episode 494 with Mike McGrathRed Hatter Mike McGrath joins the podcast to discuss RHEL security best practices, the relationship with CentOS Stream, and how Red Hat balances open-source values with enterprise AI in

AI tools are transforming how system administrators and developers manage their infrastructure, but when using generic AI assistants to troubleshoot Red Hat Enterprise Linux (RHEL) systems, the advice can sometimes lack distribution-specific context. An AI tool might assume a different Linux environment, suggest commands from other distributions that do not apply to RHEL, or recommend disabling critical security controls to resolve a permissions issue.To help bridge this gap, we are introducing 2 new integrations, currently in developer preview, designed to bring Red Hat knowledge directly in

Every network operations (NetOps) professional knows the anxiety of a manual configuration update. Even when you meticulously plan the change, a single unrecorded variable or an ad hoc configuration tweak can cascade into unexpected downtime. Across large enterprises, network teams are constantly fighting a losing battle against configuration drift—the gradual, undocumented departure of live network state from the intended design—caused by these sorts of errors.This problem is commonly rooted in human data entry. When network engineers must manually copy and paste details between systems,

Platform engineering, security architecture, and operations teams are being asked to support 2 realities at once: modern application platforms such as Red Hat OpenShift, and long-lived Red Hat Enterprise Linux (RHEL) fleets that still run critical automation. These parallel systems introduce risk, especially around how users, workloads, or automation identities can be authenticated across environments. But there’s also larger questions at play: What boundary exists after privileged work starts? How are exceptions approved? How can operators prove that the intended boundary was active when t

AI models are outpacing human-scale security operations. AI can surface vulnerabilities across major systems faster than teams can act, and most organizations lack the patching capacity to keep up. The solution isn't more intelligence—it's a smarter approach to remediation and risk management.The bottleneck isn’t intelligence, it’s response. IT teams must react to threats while maintaining a hardened posture, and automation makes that possible at machine speed. Before AI models, security teams, site reliability engineers, and IT operations teams were already challenged with the volume of

As the world prepares for the next era of human spaceflight, ensuring the health of astronauts on missions to the Moon and Mars is a top priority. However, it presents a significant challenge for missions the farther away from Earth we go: providing high-quality medical care when real-time communication with Earth-based doctors could be limited or impossible.Researchers at NASA’s Johnson Space Center in Houston are testing the Crew Medical Officer Digital Assistant (CMO-DA) to meet this challenge. Powered by RamaLama for local AI inference, this clinical decision support system is designed t

AI coding assistants are transforming software development. The debate is no longer about whether to adopt them, but how to integrate them without compromising platform security, code quality, or operational compliance.AI has changed the economics of code productionWhether you're developing software in a proprietary enterprise or open source environment, you've witnessed a fundamental shift over the past year. Your developers are using AI coding assistants (GitHub Copilot, Cursor, Claude Code, ChatGPT Codex, etc.) to accelerate development. Productivity gains are real—features can ship faste

This past March, the community gathered for Red Hat OpenShift Commons in Amsterdam. As the "zero day" kickoff to KubeCon and CloudNativeCon Europe 2026, there was no shortage of innovative presentations and discussions, but one session stood out, "Sit, Stay, Deploy: Teaching a Robot Dog with Red Hat OpenShift." Figure 1., ITQ staffers Johan van Amersfoort, Chief Evangelist and AI Lead, and Sander Harrewijnen, Technologist speaking at the OpenShift Commons Gathering in Amsterdam.Presented by ITQ staffers Johan van Amersfoort, Chief Evangelist and AI Lead, and Sander Harrewijnen, Technologist, t

Every technology—from the steam engine to the smartphone—follows a predictable arc: slow start, explosive ascent, then a plateau as physics or economics set in. This arc is the innovation S-curve, and it's a lens for understanding why Broadcom bought VMware, why Kubernetes has defied all expectations, and why the jump from hypervisors to cloud-native platforms is the most consequential architectural decision a modern enterprise can make.The 3 phases of the S-curveThe S-curve plots time or effort against performance or adoption, always producing the same shape.Phase 1 - Ferment: Progress is

For years, the industrial world has anticipated the imminent shift toward open automation. Initiatives like OPAF, Industry 4.0, and Margo have signaled a move, but the missing ingredient has been the technical rigor required to take these concepts from slide decks to the plant floor.The journey of IT and OT convergence continues to evolve. To move from vision to reality, IT and OT teams are finding new ways to align their specialized expertise. Achieving meaningful results requires a collaborative approach that respects the rigorous demands of the factory floor while leveraging the agility of

Artificial intelligence is a long-standing operational asset for telecommunication service providers, but the focus has shifted from localized capabilities to massive scale and quick return on investment. Telco service providers need to deliver real, industrial-scale results. My message to Red Hat partners is clear: Technology alone will not determine who wins the enterprise AI race. Success depends entirely on ecosystem alignment.According to a report from MIT, roughly 80% of AI initiatives fail to deliver tangible value because of misalignment between technology and business functions. Servi

For years, the telecommunications industry has looked at sovereignty through a defensive lens. It was treated as a compliance checklist and an (understandable) regulatory requirement for telco networks.After meeting with several customers across the globe at both MWC and Red Hat Summit this year, one clear takeaway is that the defensive era is over and sovereignty represents a new business opportunity where service providers can differentiate.Today, sovereignty is about control as a currency and differentiator. In a world where AI is the new engine of growth, the ability to maintain data resid

You've picked a model. Maybe it's a 70 billion parameter large model because someone on the team saw it top a leaderboard. Now you need it running in production on your Red Hat OpenShift AI cluster. So you start tuning batch sizes, figuring out quantization, sizing GPU requests, writing Kubernetes manifests, and hoping the out of memory errors stop before your deadline hits.We've watched this play out enough times to see the pattern. The hard part of enterprise AI isn't just picking a model, it's the stretch between "this model looks good" and "this model is serving traffic reliably." That str

Enterprise organizations are pushing past initial AI experimentation, shifting priorities from testing isolated models to safely deploying governable, production-ready workflows across the open hybrid cloud. Managing this transition requires an infrastructure strategy that balances rapid automation and platform innovation with a rock-solid security posture that safeguards data perimeters against emerging threats. Check out this curated roundup of the top cross-portfolio posts our readers are exploring right now. The content spans from groundbreaking command-line AI assistants and quantum-resis

Many organizations now operate under strict data governance requirements—whether driven by the EU’s General Data Protection Regulation (GDPR), Digital Operational Resilience Act (DORA), or NIS2 directive, by other national security classifications, or by sector-specific regulations in financial services, healthcare, government, and defense. These organizations, like everyone today, are increasingly seeking to adopt AI-powered infrastructure management and intelligence, but regulatory constraints mean they must figure out how to do so without sending data to the cloud.Red Hat Lightspeed (fo