Confidential Computing Summit 2026

We'll walk through where NVIDIA CC overheads appear in the model inference and training pipeline across GPU architectures and try to understand why

NATO DIANA, NATO’s innovation accelerator, is building a heterogeneous, cross-Allied ecosystem spanning innovators, mentors, test centers, ministries of defense, and other agencies across 32 Allied nations. This is the trust problem the Internet of Agents faces at global scale: how people, organizations, and their respective agents prove identity, authority, and credentials across trust boundaries without leaking private, confidential, or national-security knowledge.

To address this challenge, DIANA sought a “chip-level zero-trust” identity infrastructure. Confidential Computing sits at the root: identity, authentication, authorization, credentialing, and key management are all unified and verified from the chips up. Each entity acts through its agent with its own cryptographic identity, trust boundary, knowledge isolation, and globally verified execution.

In this session, we will share lessons from the DIANA pilot and show why Global Agentic Identity is the foundational layer of Direct Programmable Trust for the Internet of Agents: a model for sovereign ecosystems where agents interact, coordinate, and transact under hardware-backed guarantees of verifiability, confidentiality, and privacy.

Loan fraud in India is a $4 billion annual problem. Simultaneously, it is very hard to detect and prevent this when each lender sees only their slice of a borrower's activity. India's Open Finance framework, called Account Aggregator, establishes the foundation for coordinated fraud prevention at scale. However, lenders cannot pool raw borrower data to combat it.

Aikya, built on a Trusted Execution Environment, provides the answer by running cross-institutional velocity checks inside a secure enclave where no participant sees another's data, turning a privacy constraint into a structural guarantee.

Sahamati Foundation governs India's Open Finance framework enabling individuals and businesses to share real-time financial data across financial institutions and fintechs with their consent. With over 1,000 participating entities and tens of millions of active data flows, it is one of the largest Open Finance deployments in the world.

AI model advancement demands cross-enterprise data collaboration, but strict privacy regulations create barriers. This session explores a commercialized Data Clean Room in Japan by Acompany and KDDI, a Fortune Global 500 telecom company.

We will share the architecture enabling secure data matching and privacy-preserving AI development. We detail how this satisfies the strict third-party data transfer restrictions under Japan's Act on the Protection of Personal Information (APPI). By keeping the calculation process protected, enterprises can jointly analyze sensitive large-scale datasets—including personal and location data—without exposing raw information to partners.

Furthermore, we explore the relationship between policy discussions and CC in Japan. With CC recognized as an essential data security technology in public and private sectors, we discuss the potential for market expansion. We provide insights into how bridging governance and technology creates a scalable confidential AI infrastructure.

Note: Session content is subject to minor changes.