TL;DR · 30-second read

The Short Version

RAND, a nonprofit research group, has published a blueprint for a data center (a building full of computers) designed to stop the world’s best-funded spy agencies from stealing or tampering with an artificial intelligence system.

The surprise is that it needs no new inventions. Ordinary, commercially available computer hardware will do. A small test version would cost about $37 million to $50 million and could be built in as little as 14 months if treated as a national priority.

What is missing is someone to own it and a place to put it. RAND is asking a tech company or government agency to step forward.

RAND published a research report on August 4, 2026, titled Highly Secure Inference Data Centers: A Vertically Integrated Strategy for Security Engineering (RR-A4827-1). It sets out a design for secure inference data centers (SIDCs): purpose-built facilities meant to run already-trained AI models while protecting them against the most capable nation-state adversaries. That protection target is what RAND calls Security Level 5.

The authors, led by Steven F. Comer, estimate $37 million to $50 million for a proof-of-concept facility and $277 million to $345 million for an enterprise-scale version. They say construction and deployment could take as little as 14 months under emergency or national-priority conditions. They also say no fundamental research breakthroughs are required, because the design uses proven, off-the-shelf compute hardware.

Executive Summary

The report treats a trained AI model as something close to a strategic asset. Its weights, the billions of learned numbers that make up the model, together with its algorithms and the data it processes when answering queries, are the things an adversary would want to steal or alter. RAND’s answer is a facility whose security properties are derived from the outcomes it must prevent, rather than controls added onto general-purpose infrastructure after the fact.

The notable finding is not that such a facility is possible. It is that RAND considers it buildable now, at a cost that is modest by the standards of AI infrastructure, with existing hardware. That moves the question from research to procurement. The report’s own recommendations accordingly focus on organisation, not engineering: find an implementing entity, choose a site early, and start prototyping the critical security components.

RAND is candid that the design is not for everyone. Limited connectivity, fixed software and restricted physical access make an SIDC a poor fit for commercial cloud workloads. The authors recommend it for national security, emergency response and pilot deployments.

The Bottleneck Is Ownership and Siting, Not Technology

The report makes a specific claim: no research breakthroughs are needed, the compute hardware is off-the-shelf, and a pilot costs $37 million to $50 million. If that holds, the constraint on having a nation-state-resistant AI facility is no longer technical. The report’s first recommendation says as much. An AI laboratory, cloud provider, US government agency or public-interest organisation should step forward, hire a system integrator (the firm that designs and assembles the full system) and begin detailed design. As of publication, no such entity is named.

The second constraint is land and buildings. RAND notes that major construction phases are largely sequential, so each step waits on the one before it. That makes site selection the first domino on the critical path, and every month spent choosing a site adds directly to the delivery date. The 14-month figure is explicitly conditioned on emergency or national-priority treatment. RAND’s suggested shortcut is to house the facility inside an existing government site, which it says can bypass or significantly shorten key development steps. The shortcut works because such sites already have perimeter security, access control and approvals in place.

This matters to several groups: agencies weighing whether to sponsor a pilot, AI labs deciding where their most sensitive models could run, integrators positioned to bid on detailed design, and operators of existing secure government facilities that could host one. It also means the timeline depends on who commits first. A capability RAND says could exist in little more than a year does not exist yet only because nobody has taken ownership of it.

Security Designed In, Down to the Circuit

RAND calls its method concept-to-circuit. The designers start from the strategic failures the system must prevent, such as weights being copied out or inference results being tampered with, and work down to hardware-level choices. This reverses the usual pattern in commercial data centers, where general-purpose infrastructure is built first and security controls are layered around it.

Three mechanisms carry the design. The first is rigorous system partitioning, which splits the facility into separate zones. The second is unidirectional data diodes, network links that physically allow data to flow in only one direction, so a compromised zone cannot send information back out. The third is formally verified cross-realm protocols. Formal verification means using mathematical proof, rather than testing alone, to show that the software governing traffic between zones behaves exactly as specified.

The design also deliberately cuts features. RAND argues that excess capacity introduces unnecessary risk, so the facility should contain only what its mission requires. That runs against the instincts of commercial operators, who value flexibility and spare headroom. It is also why the authors restrict the model to a narrow set of uses.

A Specialist Facility, Not a New Template for Cloud

The operating constraints RAND lists, including limited connectivity, fixed software and restricted physical access, are the price of the security guarantee. A facility that cannot easily take software updates or connect freely to outside networks cannot serve the fast-changing, multi-tenant workloads that fill commercial AI data centers. SIDCs therefore look less like a new standard for the industry and more like a specialised tier, similar in spirit to classified computing environments.

The report’s headline assurance is that an SIDC built to its strategy can preserve the confidentiality and integrity of model weights, algorithms and inference data over a five-year operating period. That is a design claim, and a serious one. No facility built to the specification exists yet to test it against. This is why the third recommendation, prototyping the formally verified protocols and one-way data flows now, independently of construction, is arguably the most useful near-term step. The authors describe it as low-cost, and it would test the riskiest assumptions before anyone pours concrete.

Background

RAND is a nonprofit, nonpartisan research organisation that has long advised US policymakers on national security and technology. Its Center on AI, Security, and Technology has produced a series of studies on protecting advanced AI systems. These include Securing AI Model Weights: Preventing Theft and Misuse of Frontier Models (May 2024), which framed AI security in escalating levels, and Securing AI Algorithmic Insights (July 2026).

The new report moves from threat assessment to facility design. As AI models are used in higher-stakes government and security work, the physical buildings they run in, and the hardware, networks and people inside them, become part of the attack surface. Conventional commercial data centers are optimised for scale, connectivity and flexibility. RAND’s design gives up much of that flexibility in exchange for stronger security guarantees.

Sources

Source: How to Create Highly Secure Data Centers – RAND, RAND’s research report Highly Secure Inference Data Centers: A Vertically Integrated Strategy for Security Engineering (RR-A4827-1), published August 4, 2026.