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You’ve trained the model, packaged it on Red Hat OpenShift AI, and it’s ready to work. The next move is exposing it through an API so people and applications can use it. At that moment, your model stops being an internal experiment and becomes a front-door service. And like any front door, somebody is going to knock … sometimes it’s the right user, sometimes not.Your model is no longer just a project in a lab: it’s a production endpoint. And like any endpoint, it’s a target. How do you ensure that only the right applications and users are interacting with it? How do you protect the

These are exciting times for AI. Enterprises are blending AI capabilities with enterprise data to deliver better outcomes for employees, customers, and partners. But as organizations weave AI deeper into their systems, that data and infrastructure also become more attractive targets for cybercriminals and other adversaries.Generative AI (gen AI), in particular, introduces new risks by significantly expanding an organization’s attack surface. That means enterprises must carefully evaluate potential threats, vulnerabilities, and the risks they bring to business operations. Deploying AI with a

We are writing to provide an update regarding a security incident related to a specific GitLab environment used by our Red Hat Consulting team. Red Hat takes the security and integrity of our systems and the data entrusted to us extremely seriously, and we are addressing this issue with the highest priority. What happenedWe recently detected unauthorized access to a GitLab instance used for internal Red Hat Consulting collaboration in select engagements. Upon detection, we promptly launched a thorough investigation, removed the unauthorized party’s access, isolated the instance, and contacte

Standardizing your company’s operating environment starts with the operating system (OS), but it doesn’t end there. As the number of systems grows, configurations drift, maintenance becomes repetitive, and updates can quickly turn into a headache. At Red Hat, we support your standardization journey by providing you with what you need to deliver a robust, coherent, and integrated solution for your standard operating environment.In this post, I explore the key areas you should take into account along your standardization journey, and how these can be simplified using Red Hat technologies, pr

Recently, we’ve shared a lot about post-quantum cryptography, the great work we’re doing to make it available to you through our products, and the importance of preparing for a future with quantum computers powerful enough to break classic RSA-based cryptography. You may have heard about “Q-day,” the day when a cryptographically relevant quantum computer (CRQC) is available to break public-key encryption–the underpinning of our digital world today. If you missed it, this risk is real, and proactive organizations are already preparing for it. Q-day is predicted to occur between 2029 a

From communal effort to legal mandateThe world runs on open source. From the applications you use daily to the critical infrastructure powering our society, open source software is ubiquitous. However, this widespread adoption has brought with it an escalating need for robust security, a reality starkly highlighted by incidents like SolarWinds and the more recent XZ Utils vulnerability. While the open source community often demonstrates remarkable resilience and collaboration in addressing threats, a significant shift in responsibility is now underway, driven in part by legislation, such as th

Red Hat helps organizations embrace AI innovation by providing a comprehensive and layered approach to security and safety across the entire AI lifecycle. We use our trusted foundation and expertise in open hybrid cloud to address the challenges around AI security, helping our customers build and deploy AI applications with more trust.Understanding enterprise AI security risksAs organizations adopt AI , they encounter significant security and safety hurdles. These advanced workloads need robust infrastructure and scalable resources and a comprehensive security posture that extends across the A

AI is one of the most significant innovations to emerge in the last 5 years. Generative AI (gen AI) models are now smaller, faster, and cheaper to run. They can solve mathematical problems, analyze situations, and even reason about cause‑and‑effect relationships to generate insights that once required human expertise. On its own, an AI model is merely a set of trained weights and mathematical operations, an impressive engine, but one sitting idle on a test bench. Business value only emerges when that model is embedded within a complete AI system: data pipelines feed it clean, context‑

The Confidential Clusters project integrates confidential computing technology into Kubernetes clusters. It's an end-to-end solution that provides data confidentiality on cloud platforms by isolating a cluster from its underlying infrastructure. In a confidential cluster, all nodes run on top of confidential virtual machines (cVM). Before a node can join the cluster and access secrets, the platform and environment's authenticity are verified through remote attestation. This process involves communication with a trusted remote server.Confidential Clusters enables you to use Red Hat OpenShift,

Organizations looking to better understand the lineage of their software artifacts have begun to adopt signing as a way to improve their security posture. By applying digital signatures to software artifacts, trust can be established to verify that assets have not been substituted or tampered with through the software development and delivery process.Red Hat Trusted Artifact Signer, a key component of Red Hat’s Trusted Software Supply Chain portfolio, provides a suite of tools that supports signing and verifying assets from first commit to deployment. Since Trusted Artifact Signer was first