Sovereign AI
11 articles
Sovereign AISovereignty is about placement: compute and AI where you choose
A practical reading of sovereign AI for people who run or buy it: choose where work runs, who can use it, who holds the keys and what you can show afterwards, with the limits of each stated.
4 min read
Sovereign AISovereign AI is a placement problem
Sovereign AI is often discussed as a question of who owns the data center. In practice it comes down to three engineering questions: where each request runs, who controls the keys, and what can be proven afterwards. Each one can be answered in the infrastructure itself.
5 min read
Enterprise AIA Corporate AI Strategy for 2026: From Pilots to Production
Most enterprises use AI; few run it at scale. A practical framework for moving from pilots to production, with a clear answer to the question pilots skip: where the inference capacity comes from.
5 min read
Model routingBuild, rent or network? Sourcing AI inference in 2026
CTOs now have three ways to source AI inference: build on their own hardware, rent from a cloud or model API, or buy from an open network of independent providers. This guide compares them on the criteria that decide the outcome: demand shape, model choice, jurisdiction, verification and exit.
5 min read
Confidential computingConfidential Computing for Regulated AI: What Attestation Proves
TEE sessions and remote attestation let a regulated workload run on someone else's hardware with evidence of what code ran. What a report actually proves, what it cannot, and how Tenzro verifies, binds and routes confidential work.
5 min read
Data residencyData residency by design: pinning AI work to a jurisdiction
Residency rules for AI are moving from contract clauses into the request path. This article explains how to make jurisdiction a routing constraint that fails closed, what evidence a signed record of locality can and cannot give you, and how the rules differ across the EU, UK, MENA and APAC.
6 min read
Distributed trainingDistributed Training Across Clusters: What Works Over the WAN Today
Low-communication training has moved from paper to practice, with models trained across continents and among peers who come and go. What the research shows, what still binds, and where an open network of verifiable machines helps.
4 min read
Sovereign AIEU AI Act, NIS2 and the Data Act: a guide for AI operators
Three EU laws now shape how AI infrastructure is run in Europe. This guide sets out the dates that apply after the 2026 Digital Omnibus on AI, the obligations that reach operators, and the evidence worth producing by default.
7 min read
SecurityPreparing AI Infrastructure for Post-Quantum Cryptography
NIST's ML-KEM, ML-DSA and SLH-DSA standards are final, the IETF has standardized hybrid key exchange for TLS, and the US and EU have set dates. A practical migration plan for AI infrastructure, and how Tenzro applies hybrid post-quantum keys today.
5 min read
Public sectorPublic Sector AI: Buying Capability Without Buying Lock-In
Governments in the US, UK and EU now tell agencies to avoid AI vendor lock-in, protect government data from commercial training and assess impacts before deployment. A procurement checklist that turns those policies into contract terms and architecture.
4 min read
Telco edgeTelcos as AI edge clouds: inference inside the latency budget
Telecom operators have the sites, the fibre, the licenses and the customer relationships that low-latency AI needs. This article covers how to split an inference request across device, edge and regional GPU, what a latency budget actually allows, and what a telco needs in order to sell edge inference to buyers it has never met.
5 min read