Confidential Containers becomes a CNCF incubating project

2026-07-22 · Source: Cloud Native Computing Foundation · Field: Technology & Digital — Cloud Computing & IT Infrastructure, Cybersecurity & Data Privacy, Artificial Intelligence & Machine Learning · Depth: Advanced, short

Summary

Confidential Containers has been accepted as a CNCF incubating project, addressing the critical need to protect data in use within cloud-native environments. Initiated in 2021 by Red Hat, Intel, IBM, and other partners, the project utilizes hardware-based Trusted Execution Environments (TEEs) to encrypt data during computation, ensuring infrastructure operators cannot access sensitive workloads. It integrates TEEs into the Kubernetes ecosystem via Kata Containers, simplifying confidential workload deployment. Since joining the CNCF Sandbox, it has garnered over 1,000 GitHub stars, 1,200 merged pull requests, and 150 active contributors, with support from major organizations like Microsoft Azure, Intel, and AMD. Its components include Confidential Containers Pods, Trustee for attestation, Helm Charts & Controllers for lifecycle management, and a Hardware Abstraction Layer supporting Intel® TDX and AMD SEV-SNP. The roadmap focuses on delivering a stable solution in 2-6 months and use-case-driven development over 6-18 months, including integration with KServe for confidential AI.

Key takeaway

For AI Architects or MLOps Engineers deploying sensitive workloads in cloud-native environments, Confidential Containers offers a standardized approach to data-in-use protection. You can now run confidential AI inference and other sensitive applications on public clouds with assurance, utilizing familiar Kubernetes patterns. This reduces the complexity of integrating hardware TEEs like Intel® TDX or AMD SEV-SNP, allowing you to focus on application logic while ensuring data privacy and integrity at scale.

Key insights

Confidential Containers secures data in use within Kubernetes by abstracting hardware TEEs, enabling sensitive cloud-native workloads.

Principles

• Data in use requires hardware-based protection.
• Abstracting TEE complexity into Kubernetes simplifies adoption.
• Open governance fosters industry standards for privacy.

Method

Confidential Containers deploys unmodified containers into TEEs via Kubernetes Pods, using Trustee for node/workload attestation and Helm Charts for lifecycle management across heterogeneous TEEs.

In practice

• Run sensitive AI inference workloads on public clouds.
• Deploy confidential applications using standard Kubernetes workflows.
• Verify TEE integrity before granting secrets or keys.

Topics

• Confidential Computing
• Kubernetes
• Trusted Execution Environments
• Cloud Native Security
• Data-in-Use Protection
• Kata Containers
• AI Inference

Code references

• orgs/confidential-containers
• confidential-containers/confidential-containers
• cncf/toc

Best for: CTO, VP of Engineering/Data, Director of AI/ML, AI Engineer, MLOps Engineer, AI Architect

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Editorial summary, takeaway, and curation by AIssential. Original article published by Cloud Native Computing Foundation.