Confidential Computing Summit 2026

Unlike server-side confidential AI, on-device confidential AI must balance strong protection of sensitive personal data with efficient operation under limited computational resources.

In this talk, we explore the impacts of CC on on-device AI performance for various AI models and tools by identifying some root-causes. First, we recognise that CC overheads vary across AI models during critical operations such as data read/write, model loading and inference phases, supported by detailed experiments. Second, we investigate multiple designs for AI agent tools in CC, especially by considering different AI memory modules, that present distinct overheads compared to traditional AI models. To enable systematic evaluation, we develop a modular software framework integrated with the open-source ISLET CC project. This framework supports configurable benchmarking of AI agent tools, and will be publicly released to foster the reproducibility and collaboration within the CC community. Lastly, since these performance drops can negatively impact the user experience, we propose a set of techniques that minimise the overhead related with model loading while ensuring robust privacy protection.

Kubernetes shares host kernels, network stacks, storage paths, and control planes across tenants. These shared primitives become attack surfaces when tenants cannot trust each other or the infrastructure operator.

We enumerate the threat surfaces in confidential Kubernetes deployments, from eBPF snooping and conntrack hijacking to hardware-assisted virtualization rootkits. We then present a production architecture that eliminates shared-trust assumptions by flattening the virtualization stack so every workload runs as a TEE-protected guest, gating all secrets, identities, and device access on a composite attestation chain, and wrapping each shared primitive in a hardened overlay for compute, network, storage, control-plane, identity, and observability.

Attendees will learn which Kubernetes primitives leak across tenant boundaries, how composite attestation closes those gaps, and practical steps toward true multi-tenancy in confidential Kubernetes deployments.

AI agents are making decisions, calling tools, and talking to other agents, often with access to sensitive data they shouldn't be able to see in plaintext. The usual answer is to just trust the infrastructure, but that falls apart in multi tenant clouds and cross org workflows. This talk covers what it actually takes to run agentic AI workloads inside Trusted Execution Environments. I'll walk through the architecture for isolating agent-to-agent communication using confidential VMs, how attestation works when agents need to dynamically invoke external tools, and the gotchas we hit around key management and session state. We'll look at real performance numbers and what the overhead looks like on GPU backed inference inside TEEs and where the bottlenecks actually are. Attendees will leave with a concrete reference architecture for deploying AI agents with hardware-rooted trust boundaries, plus practical guidance on attestation flows for multi-party agent pipelines. If you're building agentic systems that handle regulated or sensitive data, this talk gives you a starting point that doesn't require rearchitecting everything from scratch.