Organizations in banking, healthcare, and defense are hitting a wall: they possess vast amounts of valuable data, but privacy regulations and national security rules prevent them from uploading it to third-party AI platforms. A joint solution from Duality Technologies and Red Hat now offers a practical path forward, combining advanced privacy techniques with enterprise-grade open-source infrastructure to enable what's called "sovereign AI".

What Is Sovereign AI and Why Does It Matter?

Sovereign AI addresses a critical vulnerability in how artificial intelligence systems work today. Traditional AI pipelines require data to be decrypted in memory to run inference queries or train machine learning models, leaving sensitive information exposed to memory dumps, system administrators, or other threats. Sovereign AI keeps data encrypted throughout the entire process, ensuring that training data, intermediate model weights, and final outputs remain under local legal jurisdiction and administrative control.

For enterprises in highly regulated sectors, this distinction is not academic. Banks handling customer financial records, hospitals managing patient health data, and government agencies protecting classified intelligence all face strict compliance frameworks like the Health Insurance Portability and Accountability Act (HIPAA) and the European Union's General Data Protection Regulation (GDPR). Traditional cloud-based AI platforms simply cannot meet these requirements.

How Does the Technical Architecture Actually Work?

The Duality and Red Hat solution operates on four integrated layers of protection. At the foundation, Red Hat OpenShift provides enterprise infrastructure to orchestrate what are called "confidential containers," which use hardware-based trusted execution environments (TEEs) to isolate running workloads inside encrypted memory enclaves. Even if an attacker gains root access to the host operating system or cloud hypervisor, the data inside the enclave remains encrypted and inaccessible.

Rather than pulling raw datasets into a single central repository, the system uses federated learning, which pushes model training to the data sources themselves. Each participating node computes model updates locally on its own infrastructure. Only the encrypted intermediate mathematical parameters are transmitted back to a central server for aggregation, meaning raw sensitive records never leave their local premises.

To prevent reverse-engineering attacks on these intermediate updates, the architecture adds two additional layers of mathematical protection. Fully homomorphic encryption (FHE) allows calculations to run on encrypted data without decrypting it, while differential privacy adds mathematically bounded noise to model weights and query outputs to guarantee that observers cannot infer whether a specific record was included in the dataset.

Steps to Deploy Sovereign AI Infrastructure

Organizations looking to implement this approach can follow a structured deployment path using components from the Red Hat ecosystem:

• Confidential Containers Operator: Installed in the openshift-sandboxed-containers-operator namespace, this operator provisions the kata-cc runtime and orchestrates hardware-isolated virtual machines such as Intel Trust Domain Extensions (TDX), where Duality's confidential workloads safely execute away from host-level visibility.
• Red Hat Build of Trustee: Operating within the trustee-operator-system namespace, this component verifies hardware integrity and acts as the key broker, handling Key Broker Service configuration and verifying that the hardware environment meets security requirements before any code executes.
• Hardware Acceleration Support: The architecture leverages underlying hardware capabilities like AMD's Secure Encrypted Virtualization (SEV) and Intel's Software Guard Extensions (SGX) instruction codes to establish trusted execution environments on standard enterprise infrastructure.

What Real-World Problems Does This Solve?

The practical applications span multiple industries facing acute data-sharing challenges. In retail, store owners with similar store segmentations can share sales data anonymously to optimize stock purchasing and forecasting without exposing competitive information. Financial institutions can collaborate on anti-money laundering and financial crime detection without exchanging customers' personally identifiable information or breaking cross-border banking secrecy laws.

Healthcare organizations and research institutions can train diagnostic models and perform genome-wide association studies across global networks while complying with HIPAA and GDPR. Government agencies can share intelligence indicators and execute protected inference across multicloud or classified on-premise enclaves without revealing source data or tradecraft.

Why Open Source Matters for True Sovereignty

A critical principle underpins this approach: relying on proprietary, closed-source systems for sovereign AI is fundamentally contradictory. True technical sovereignty requires complete transparency into every software layer. By running the Duality platform natively on Red Hat's open-source infrastructure, enterprises gain three key advantages.

First, workloads remain fully portable across on-premise datacenters, sovereign national clouds, or major hyperscalers without rewriting application logic, providing vendor independence. Second, open-source components provide transparent software bills of materials (SBOMs) and AI bills of materials (AIBOMs) to verify code provenance and regulatory compliance. Third, unified governance through policy management, fine-grained access controls, and detailed operational audit logs satisfies strict compliance frameworks while maintaining central governance across distributed clusters.

This technical blueprint moves sovereign AI from theoretical concept to production deployment across privacy-critical industries. Organizations no longer need to choose between data protection and AI innovation; they can leverage the full predictive power of distributed data assets while maintaining total ownership over their models, inputs, and outputs.