"Recent years have seen the rise of network organizations � groups of "unbundled" companies collaborating across the value chain to deliver products and services to customers. By owning fewer assets and leveraging the resources of partner companies, network "orchestrators" such as Cisco, CNET, Schwab, and eBay require less capital, return higher revenues per employee, and spread the risks (and benefits) of a volatile market across the network."
Enterprise AI adoption keeps running into the same wall: the moment sensitive data, model weights, or agent memory leave a tightly controlled environment, security teams lose visibility into what happens to them while the workload is actually running. Confidential computing exists to close that gap, and new research from ABI Research shows the technology has matured enough, across CPUs, GPUs, containers, and now agentic AI, to become a foundational requirement for enterprises building sovereign AI infrastructure: computing environments they control end to end rather than simply rent. Protection is Moving From CPUs to GPUs ABI Research finds that CPU-based confidential computing is closest to mass-market adoption, the product of years spent hardening chip-level isolation for general workloads. GPU-based confidential computing is now generating the strongest momentum, and for good reason. It addresses one of the largest unresolved gaps in AI security: protecting data and models while inf...