Navigating AI risk: Building a trusted foundation with Red Hat

Navigating AI risk: Building a trusted foundation with Red Hat Understanding enterprise AI security risks Red Hat's layered approach to AI security The proven backbone of Red Hat OpenShift A platform for what’s next with Red Hat OpenShift AI Best practices Hardening the AI platform and deployment pipelines Protecting active AI workloads and data Final thoughts Get started with AI Inference About the authors Martin Isaksson Christopher Nuland More like this Blog post Blog post Original podcast Original podcast Keep exploring Browse by channel Automation Artificial intelligence Open hybrid cloud Security Edge computing Infrastructure Applications Virtualization Share 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. As 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 AI lifecycle. Many AI projects struggle to reach production because of these significant safety and security concerns. Some of the challenges organizations face include: Evolving AI-specific threats: AI applications and models are becoming attractive targets for malicious actors. Beyond conventional software vulnerabilities, critical concerns include training data poisoning, model evasion or theft, and adversarial attacks. Complex software supply chain: The AI lifecycle involves numerous components, increasing vulnerability risks. AI applications also often depend on a vast ecosystem of open source libraries, pre-trained models, and complicated data pipelines. A single vulnerability or malicious component introduced at any stage—from data ingestion and third-party libraries to the base container images—can compromise the integrity and security of the entire AI system. Recent supply chain attacks highlight the urgent industry need for verifiable integrity and provenance for all software artifacts, including AI models and their dependencies. Critical AI safety requirements: Trust is built on the assurance that AI models will operate as intended and without bias.