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  • RAND’s Secure AI Data Center Starts at $37M and Needs an Owner, Not New Tech

    RAND’s Secure AI Data Center Starts at $37M and Needs an Owner, Not New Tech

    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.

  • Executive Order Seeks Early Government Access to Frontier AI Models

    Executive Order Seeks Early Government Access to Frontier AI Models

    President Donald Trump has signed an executive order seeking early government access to powerful artificial intelligence models, according to a June 1, 2026 report from Cybersecurity Dive. The order targets so-called frontier models — the largest, most capable AI systems built by leading developers — and signals a shift toward more formal federal oversight of how those systems are tested and reviewed before they reach the public.

    Executive Summary

    The announcement, as reported, is short on detail but significant in direction: the federal government wants to see the most powerful AI models before, or at least earlier than, the general public does. Until now, pre-deployment testing arrangements between US government bodies and frontier AI developers have been largely voluntary. An executive order — a directive from the president to federal agencies that carries the force of law within the executive branch — moves that relationship from handshake to instruction, at least on the government’s side.

    Why it matters: early access is the mechanism by which a government evaluates whether a new model creates national-security or cybersecurity risks — for example, whether it meaningfully helps attackers write malware or discover vulnerabilities — before those capabilities are broadly available. For AI developers, it raises immediate compliance questions about what must be shared, with whom, under what protections, and on what timeline. For enterprises and infrastructure operators downstream, it introduces a new gating step in how frontier AI reaches the market.

    From Voluntary Commitments to Executive Direction

    Pre-release government testing of frontier models is not new as a concept. In 2024, leading US developers including OpenAI and Anthropic signed voluntary agreements giving the US AI Safety Institute (housed in NIST, the National Institute of Standards and Technology, and later reorganized under the current administration) access to major new models for evaluation before and after public release. What the reported order appears to change is the footing: voluntary arrangements depend on each company’s continued willingness, while an executive order directs federal agencies to institutionalize the practice. The precise obligations on companies — as opposed to agencies — cannot be determined from the initial report, and that distinction matters legally, since executive orders bind the government, not private firms, unless anchored in existing statutory authority.

    The direction of travel is consistent with the administration’s broader posture: after rescinding the previous administration’s 2023 AI executive order in early 2025, the White House has framed its AI agenda around American competitiveness and national security rather than broad model regulation. Seeking early access fits that frame — it is oversight aimed at the security properties of the most capable systems, not a general licensing regime.

    The Cybersecurity Logic — and Its Limits

    The strongest case for early government access is a timing problem. Frontier models increasingly show capabilities relevant to offense and defense in cybersecurity: assisting vulnerability discovery, generating exploit code, or automating reconnaissance. If a model materially shifts that balance, the government’s security agencies want to know before adversaries and criminals can probe the same system in the wild. Early evaluation also feeds defensive preparation — agencies and critical-infrastructure operators can harden systems against capabilities they have actually measured rather than speculated about.

    The limits of that logic deserve equal attention. Evaluation is only as good as the tests run and the expertise applied, and independent assessments of government AI-evaluation capacity have long noted resource constraints. There is also a concentration-of-risk question: a government repository of, or privileged access channel to, unreleased frontier models is itself a high-value target. The reported order’s cybersecurity directives will need to answer how that access is secured — a detail the initial reporting does not cover.

    Compliance Questions for AI Developers

    For the handful of companies training frontier models, the operational questions are concrete. Does “access” mean structured API-based testing, deeper access to model weights, or disclosure of training details? Model weights — the learned parameters that constitute the model itself — are among the most valuable trade secrets these companies hold, and any transfer or hosted-access arrangement raises intellectual-property and security questions that voluntary agreements handled through negotiated terms. A mandate framework will need equivalents: confidentiality protections, liability allocation if pre-release access leaks, and clarity on whether findings can delay a launch.

    There is also a competitive dimension. If early-access obligations attach only to US companies, developers may argue it disadvantages them against foreign rivals; if the government ties access to procurement eligibility — a lever prior administrations have used — compliance becomes a cost of selling to the federal market rather than a pure mandate. Which lever this order pulls is not stated in the source report, and it is the single most important detail for assessing the order’s real force.

    What It Means Downstream: Buyers and Infrastructure

    For enterprises consuming frontier AI, the near-term effect is likely procedural rather than dramatic: potentially longer or more structured pre-release evaluation windows, and possibly stronger security documentation accompanying new models — useful inputs for corporate AI-governance and vendor-risk programs. Federal evaluation findings, if any are published, could become a de facto benchmark that security teams reference in their own assessments.

    For the infrastructure layer — data centers, connectivity, and cloud platforms hosting these models — formalized government engagement with frontier AI reinforces a trend already visible in export controls and cloud know-your-customer proposals: the largest AI workloads are being treated as strategic assets. That tends to raise the compliance bar for the facilities and networks that host them, from physical security to attestation about where and how model weights are stored. Operators positioned to meet elevated security requirements stand to benefit; those serving frontier workloads without them face a rising floor.

    Background

    US federal policy on frontier AI has swung between frameworks over three years. The Biden administration’s October 2023 executive order used the Defense Production Act to require developers of the most powerful models to share safety-test results with the government, and established the US AI Safety Institute at NIST, which struck voluntary pre-release testing agreements with OpenAI and Anthropic in 2024. The Trump administration rescinded the 2023 order in January 2025, reoriented the safety institute toward standards and security, and in July 2025 released an AI Action Plan emphasizing American AI dominance, infrastructure build-out, and national security.

    The June 2026 order reported here fits that trajectory: rather than broad model regulation, it pursues government visibility into the most capable systems on security grounds. It arrives as frontier models demonstrate growing dual-use capability in cybersecurity — useful for both defense and offense — which has made pre-deployment evaluation a central tool in every major government’s AI-security playbook.

    Source: Trump signs EO seeking early government access to powerful AI models — Cybersecurity Dive report, June 1, 2026, on a new executive order covering pre-release federal evaluation of frontier AI systems.

  • Trump Order Targets Foreign Tech in US Power Grid

    Trump Order Targets Foreign Tech in US Power Grid

    The Trump administration is advancing measures to bar foreign technology considered a national-security risk from the US bulk-power system, according to a Nextgov/FCW report dated May 8, 2026. The move revives and extends earlier executive efforts to police the origins of transformers, inverters, control systems and other grid-connected equipment.

    Executive Summary

    Washington is again training its regulatory attention on the electric grid’s supply chain. The reported action would restrict the use of equipment from designated foreign adversaries in US power infrastructure, echoing a 2020 executive order that was paused and then partially unwound before returning to the policy agenda.

    For data-center operators, the stakes are practical rather than abstract. High-voltage transformers, medium-voltage switchgear, battery inverters and grid-tied controls increasingly determine whether new capacity comes online on schedule. Any rule that narrows the pool of eligible suppliers reshapes procurement, lead times and cost curves for hyperscale and colocation builds alike.

    What ‘Risky Foreign Technology’ Actually Means

    The phrase is broad by design. In earlier iterations, US officials focused on bulk-power equipment sourced from countries designated as foreign adversaries, with particular concern about large power transformers and digital control systems that could be remotely accessed or tampered with. The underlying worry is that embedded firmware, software updates or hardware backdoors in critical grid equipment could be exploited during a conflict or crisis.

    For a lay reader, the concern is less about a single dramatic hack than about slow, quiet dependence. If a handful of foreign vendors supply components that sit inside substations for thirty or forty years, replacing them later is expensive and disruptive. Regulators appear to be trying to prevent that lock-in from deepening while alternatives still exist.

    Direct Line to Data-Center Power

    Data centers do not run on abstractions; they run on transformers, switchgear and increasingly on-site generation. The industry is already contending with multi-year lead times for large transformers and constrained global manufacturing capacity. A rule that narrows sourcing options, even at the margin, tightens an already tight market and raises the premium on domestic and allied-country supply.

    Operators building AI-scale campuses should expect procurement teams to be asked new questions: Where was this transformer wound? Whose firmware runs the relay? Is the inverter vendor on a restricted list? Compliance overhead is real, but the bigger operational risk is discovering late in a project that a specified component is no longer eligible.

    Winners, Losers and Second-Order Effects

    Domestic manufacturers of transformers, switchgear and inverters stand to benefit if the policy sticks and is enforced consistently. Allied suppliers in Europe, Japan, South Korea and Canada are likely secondary beneficiaries. The clearest losers would be Chinese-origin equipment makers and, indirectly, US buyers who had been counting on lower-cost imports to hold down capital budgets.

    The second-order effect is timing. Even a well-intentioned rule can slow projects if the domestic industrial base cannot expand fast enough to absorb displaced demand. That risk deserves scrutiny on its own merits, separate from the security rationale.

    An Even-Handed Read of the Politics

    Supply-chain security in the grid is not a partisan invention; both the 2020 Trump executive order and subsequent Biden-era reviews concluded that the sector had exposure worth addressing. Where reasonable people differ is on scope, speed and how narrowly to define ‘risky.’ Overly broad rules can raise costs without proportionate security gains; overly narrow ones can leave gaps. The forthcoming details, not the headline, will determine which category this action falls into.

    Background

    Concerns about foreign-made equipment in the US grid escalated in May 2020, when the first Trump administration issued Executive Order 13920 declaring a national emergency over bulk-power system supply chains. That order was suspended early in the Biden administration pending review, and subsequent policy focused on voluntary guidance, prohibited-transaction rules for specific equipment and expanded domestic manufacturing incentives.

    In parallel, US utilities and data-center developers have wrestled with a global shortage of large power transformers, lead times that can stretch past two years, and rapid load growth driven by AI, electrification and reshoring. Those pressures form the practical backdrop against which any new sourcing restrictions will be judged.

    Source: Trump admin moves to block risky foreign technology from US power grid – Nextgov/FCW — reporting on federal action to restrict adversary-linked equipment in the US electric grid.