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Press release · September 10, 2026 · globenewswire.com

ABI Research Says Confidential AI Expands From CPUs to GPUs

Summary

ABI Research said Confidential AI moved beyond CPU-focused confidential computing as performance gains, new hardware protocols, and security demands increased. The firm said the shift reflects enterprise pressure to protect data and model-related information while AI workloads run.

ABI Research reported that GPU-based confidential computing became a major driver for adoption because it addressed protecting data while it was processed. The firm also cited rising interest in confidential containers and confidential virtual machines for cloud-native and Kubernetes-based environments.

In ABI Research’s description, confidential computing expanded from CPUs into GPUs, multi-GPU environments, containers, and “agentic AI workflows.” The company also associated the approach with protecting inference, training, and agentic workflows, while referencing attestation, confidential containers, and auditability as part of platform and control development.

ABI Research pointed to work by NVIDIA, Intel, AMD, and Arm on confidential GPU and CPU foundations, and it cited software and control efforts from Anjuna, Red Hat, Fortanix, Decentriq, and IBM covering model-weight security, attestation, and governance requirements for agentic AI. “Confidential computing is no longer a CPU-only conversation,” said Aisling Dawson, senior analyst at ABI Research.

“Agentic AI is creating a new security inflection point for the confidential computing market,” Dawson said. “While confidential computing will not solve every agentic risk, it is emerging as one of the most credible hardware-rooted approaches for securing agent execution, protecting sensitive memory, strengthening auditability, and building trust in high-value enterprise AI systems. Vendors that pair strong attestation, performance optimization, and ecosystem collaboration will be best positioned to capture this opportunity.”

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