Red Hat News

There are a lot of choices when it comes to container base images, so why should you select Red Hat Universal Base Image (UBI)? First of all, the code in Red Hat Universal Base Image is derived from Red Hat Enterprise Linux (RHEL), and the mission of RHEL is to be your source for safe and reliable Linux innovation that makes your workloads successful. Because all containers are Linux, and UBI is derived from RHEL, the same values apply to UBI, including:Innovate: Organizations are constantly looking to innovate quickly without friction and provide consistency from the data center to the edge b

Red Hat Enterprise Linux (RHEL) 9.0 Extended Update Support (EUS) has been included in the Catalogue of Information and Communication Technology Security Products and Services (CPSTIC) of the National Cryptologic Centre (CCN). This inclusion enables RHEL for deployment in public sector bodies operating under the strictest requirements of Spain's National Security Framework (ENS).The ENS is the rigorous cybersecurity framework mandatory not only for all Spanish public administrations but also for the extensive network of private companies that provide them with services. It is a regulation aime

Organizations running Microsoft SQL Server 2025 on Red Hat Enterprise Linux (RHEL) in Azure can now install and integrate with the SQL IaaS Agent extension (see SQL Server IaaS Agent Extension for Linux - SQL Server on Azure VMs), which enables full licensing flexibility for their SQL deployment. As of today, the extension supports three licensing options. When registering in lightweight mode, you can choose:PAYG (Pay-As-You-Go): Pay per usage. The pay-as-you-go model means that the per-second cost of running the Azure VM includes the cost of the SQL Server license.AHB (Azure Hybrid Benefit):

At Red Hat, our IT and Engineering functions encounter the same challenges and make the same decisions our customers face every day, from infrastructure optimization and application delivery to automating and enhancing the security of our global business. Right now, almost every organization we talk to is navigating the complexities of an AI journey, and we’re in that same boat. As users of our own products—because we love and believe in the technology we build—we want to pull back the curtain on our internal experience. We hope that the lessons we’ve learned through some foresight an

The data is there. The insight isn't.Telecommunications networks generate unprecedented volumes of telemetry. Every authentication attempt, packet flow, device heartbeat, controller update, and service interaction produces telemetry. As networks expand into Wi-Fi offload, 5G standalone, and distributed edge deployments, that volume continues to grow. Yet turning that data into operational intelligence remains one of the industry's hardest challenges.The challenge for operators today is not collection. It is a correlation. Modern networks produce multiple operational signals simultaneously: Net

Security is an important aspect of any digital undertaking, and Kubernetes is no different. We’ve built Red Hat Advanced Cluster Security for Kubernetes to form a foundational layer of security across fleets, estates, and platforms, be it public, private, or hybrid clouds. Today we release Red Hat Advanced Cluster Security for Kubernetes version 4.10 as part of our ongoing effort to make life easier for Red Hat OpenShift users when it comes to building and enforcing security policies for their clusters.Chief among these updates is the new integration of vulnerability management into OpenShif

Red Hat OpenShift Virtualization 4.21 is now Generally Available. The release of Red Hat OpenShift Virtualization 4.21 introduces new capabilities that simplify virtual machine (VM) management, enhance operational efficiency, and expand deployment flexibility across hybrid and multi-cloud environments. This release brings streamlined VM administration with multi-cluster management, guided networking configuration through new physical and virtual network creation workflows, and generative AI–powered assistance with OpenShift Lightspeed integrated directly into virtualization operations. Addi

Red Hat news and announcements from KubeCon + CloudNativeConSee the latest Red Hat news and content from KubeCon + CloudNativeCon Europe in Amsterdam, including updates on OpenShift 4.21, cloud-native security, and AI. Learn more SiliconANGLE - Red Hat sees inference as AI’s next battleground — with Kubernetes at the coreAs AI demands drive orders-of-magnitude increases in token consumption, the challenge now is less about training larger models than about running them reliably, cost-effectively and at scale. Red Hat has contributed llm-d, an open-source project for running LLMs across K

Moving to the edge isn't just a trend; it’s a response to the need for faster results. By processing data right where it’s created, organizations are finding they can finally unlock real-time decision-making and make their operations significantly more efficient.Whether it’s a factory floor, a wind turbine, or a retail backroom, the edge is where the most impactful business data is being generated. Most operational leaders already recognize that moving processing power closer to that data is the key to transforming how they work. The real challenge, however, isn’t just getting there—

In our first 3 articles, we framed AI security as protecting the system, not just the model, across confidentiality, integrity, and availability, and we showed why the traditional secure development lifecycle (SDLC) discipline still applies to modern AI deployments. We also focused on guardrails and different architectural approaches such as dual LLMs and CaMeL to help protect against prompt injection and unsafe actions.This article completes the defense strategy by focusing on the backbone that makes guardrails enforceable in production—identity, authentication, authorization, and zero trus

In product security, AI represents a new and critical frontier. As artificial intelligence becomes mainstream in both defense tools and exploitation methods, security professionals must master these technologies to more effectively protect and enhance their systems.What is AI in cyber security?AI in cyber security is the application of advanced technologies like machine learning and automated reasoning to detect, prevent, and respond to digital threats at a scale and speed that exceeds human capabilities.AI systems are able to perform a growing variety of tasks, such as pattern recognition, le

We recently announced that Red Hat OpenShift Virtualization is now available on OpenShift Dedicated on Google Cloud allowing customers the ability to migrate and modernize their VMs to Google Cloud. Running on Google Cloud C3 bare-metal instances, OpenShift Virtualization provides direct access to CPU and memory resources to help support performance-sensitive virtual machine (VM) workloads. Combined with the fully managed experience of OpenShift Dedicated, organizations can migrate and run VMs in the cloud while building a foundation for future innovation with cloud-native technologies.As orga

In the era of gen AI and rapid machine learning (ML) adoption, enterprise AI is no longer just a research experiment—it’s a core business driver. But as organizations rush to operationalize their AI initiatives, they’re hitting a significant roadblock: deployment and management at scale.To help bridge the gap between AI innovation and IT operations, Red Hat Ansible Certified Content Collection for Google Cloud provides native support for Google Cloud’s Vertex AI platform. This release enables a shift in how operations and data science teams manage the lifecycle of their AI services, br

In previous articles, we framed AI security as protecting confidentiality, integrity, and availability of the whole AI system, not just the model. We also mapped AI risks onto familiar secure development lifecycle (SDLC) thinking, treating data and model artifacts as first-class build inputs and outputs.This article examines the primary security risk for enterprise large language model (LLM) applications: prompt injection. This vulnerability occurs when the model fails to distinguish between data and instructions, allowing external prompts to seize control of the system. The risk is particular

With the migration toolkit for virtualization 2.11 (MTV) release, users gain an improved learning experience.A new “Tips and tricks” drawer was introduced as part of the 2.10 release and further improved in the following 2.11 release, which allows users to access contextual help, tips, and best practices directly within the interface. This feature is designed to reduce the learning curve for new users and provide immediate, in-context guidance for common and complex migration tasks, helping users learn key MTV workflows without ever leaving their current view.Learn while doing: Interactive

If you’ve been following our journey from elevating multicluster operations in 2.12 to expanding hybrid cloud reach in 2.13, then you know our goal has always been unified control. However, as fleets grow, "unified" can quickly turn into "crowded." In our previous 2.15 update, we focused on helping you see more and click less. With Red Hat Advanced Cluster Management for Kubernetes 2.16, we're moving beyond mere visibility into intelligent, self-service operations that work for your entire team.Here are the four ways 2.16 helps you reclaim your nights and weekends.1. Mobility without boundar

Red Hat Enterprise Linux (RHEL) is now validated on the new AWS Graviton5-based Amazon EC2 M9g instances, now available in public preview. At Red Hat, we aim to deliver a solid infrastructure that serves as a foundation for the important work you do. We're committed to doing this during the early phase of rollout, which provides ample time for users to experiment with new infrastructure. By validating RHEL on M9g instances during the public preview phase, we're giving technical leads and architects the green light to start testing workloads, from high-performance databases and web-scale applic

Standard AI security benchmarks can't check for all of the possible ways an AI model can be compromised. A backdoor trigger could cause targeted failure, a competitor could clone your API model through repeated queries, or a privacy probe might reveal whether a specific person’s data was used in training. For this reason, organizations deploying AI must understand the variety of potential attacks and proactively address them during model training and after deployment.In our previous article, What does "AI security" mean and why does it matter to your business?, we talked about protecting A

The era of AI exploration has opened doors to incredible possibilities. Today, the most forward-thinking organizations are moving toward a new horizon: turning those successful experiments into a standardized, high-performance engine for growth. To deliver the full benefits of intelligence across the entire business, teams are adopting an industrial-grade system known as the AI Factory.Elevating AI from initiative to infrastructureThe AI Factory is more than just a workflow; it’s a unifying environment that enables core disciplines to thrive at scale. While standard MLOps focuses on the mode

Today, we are contributing llm-d to the Cloud Native Computing Foundation (CNCF) as a Sandbox project.This isn't just a hand-off of code. It’s a commitment to making high-performance AI serving a core, portable capability of the cloud-native stack. When we launched llm-d in May 2025, we set out to solve the massive capabilities gap between AI experimentation and mission-critical production inference at scale. By moving llm-d into the CNCF, we’re expanding the target of a multi-vendor coalition—including CoreWeave, IBM, Google, and NVIDIA—to build the open standard for distributed infer