Insights, reports, and guides on sovereign AI infrastructure. Written for practitioners and decision-makers navigating the private AI landscape.
Technical depth without the hype. Our team writes about what we see building production AI infrastructure for regulated enterprises.
Encryption is necessary but not sufficient for AI security. We examine why physical GPU isolation — not just logical separation — is the critical security control for enterprises processing sensitive data in AI workloads. Includes analysis of side-channel attack vectors on shared GPU infrastructure.
Read moreHealthcare organizations are deploying AI for clinical documentation, imaging analysis, and care optimization — but HIPAA creates specific technical requirements that most AI cloud providers cannot meet. This guide maps HIPAA's technical safeguards to AI infrastructure design decisions.
Read moreOpen-weight models have closed the capability gap with proprietary APIs — but the more important story is what you gain beyond capability: weight ownership, fine-tuning freedom, zero inference logging, and deployment on your own infrastructure. We break down the real trade-offs for enterprise decision-makers.
Read moreMany enterprises assume private AI infrastructure is cost-prohibitive. We model three approaches — internal build, hyperscaler configuration, and purpose-built sovereign AI provider — across a realistic enterprise workload profile. The results challenge common assumptions about the price of control.
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