Hardware-Enabled Governance

Quick Assessment

Overview

Hardware-enabled governance mechanisms (HEMs) represent a potentially powerful but controversial approach to AI governance: embedding monitoring and control capabilities directly into the AI chips and computing infrastructure used to train and deploy advanced AI systems. Unlike export controls that prevent initial access to hardware or compute thresholds that trigger regulatory requirements, HEMs would enable ongoing verification and enforcement even after hardware has been deployed.

The appeal is significant. RAND Corporation research argues that HEMs could "provide a new way of limiting the uses of U.S.-designed high-performance microchips" that complements existing controls. The policy urgency is real: an estimated 100,000 export-controlled GPUs were smuggled into China in 2024 alone, with some estimates ranging up to one million. If AI governance requires not just knowing who has advanced chips, but verifying how they're used, hardware-level mechanisms offer a potential solution. Remote attestation could verify that chips are running approved workloads; cryptographic licensing could prevent unauthorized large-scale training; geolocation constraints could enforce export controls on a continuing basis.

However, HEMs also raise serious concerns. Privacy implications, security risks from attack surfaces, potential for abuse by authoritarian regimes, and fundamental questions about appropriate scope of surveillance make this a highly contested intervention. A RAND workshop with 13 experts in April 2024 found that narrow-scope HEMs may be more feasible, whereas broader designs could pose greater security and misuse risks. Implementation would require unprecedented coordination between governments and chip manufacturers, with chip design cycles of 2-3 years before new features can reach production. HEMs represent high-risk, high-reward governance infrastructure that merits serious research while demanding careful attention to safeguards.

Policy Landscape and Current Developments

The policy debate around HEMs has accelerated significantly in 2024-2025, driven by concerns about enforcement of existing export controls.

Legislative Proposals

Industry Response

Enforcement Gap

Current export controls face significant enforcement challenges:

Technical Mechanisms

Hardware-enabled governance encompasses several distinct technical approaches with different capabilities, costs, and risks:

flowchart TD
  subgraph DETECTION["Detection Mechanisms"]
      A[Remote Attestation] --> A1["Verify chip is running<br/>expected software"]
      B[Usage Metering] --> B1["Track compute consumption<br/>on-device"]
      C[Geolocation] --> C1["Verify physical location<br/>of hardware"]
  end

  subgraph CONTROL["Control Mechanisms"]
      D[Cryptographic Licensing] --> D1["Require authorization<br/>for operation"]
      E[Kill Switch] --> E1["Remote disable<br/>capability"]
      F[Workload Filtering] --> F1["Prevent specific<br/>computation patterns"]
  end

  subgraph GOVERNANCE["Governance Applications"]
      A1 --> G[Verify compliance]
      B1 --> G
      C1 --> G
      D1 --> H[Enforce restrictions]
      E1 --> H
      F1 --> H
  end

  style DETECTION fill:#e1f5ff
  style CONTROL fill:#fff3cd
  style GOVERNANCE fill:#d4edda

Mechanism Overview

Trusted Platform Module (TPM) Foundation

Many HEM proposals build on existing Trusted Platform Module technology:

TPMs are already deployed in most modern computers. Extending this infrastructure for AI governance is technically feasible but raises scope and purpose questions.

RAND Research Framework

RAND Corporation's 2024 working paper, authored by Gabriel Kulp, Daniel Gonzales, Everett Smith, Lennart HeimPersonLennart HeimCompute governance researcher. Affiliated with RAND, Epoch AI, and GovAI. Leading expert on AI compute supply chains and governance. Co-authored RAND working papers on hardware-enabled mechanisms (..., Prateek Puri, Michael J. D. Vermeer, and Zev Winkelman, provides the most comprehensive public analysis of HEMs for AI governance. The research specifically focuses on Export Control Classification Numbers 3A090 and 4A090 (advanced AI chips).

Two Main HEM Approaches Proposed

Proposed Mechanisms Detail

Design Principles (RAND)

1. Proportionality: Governance mechanisms should match risk levels
2. Minimal intrusiveness: Collect only necessary information
3. Fail-safe design: Errors should default to safe states
4. International coordination: Effective only with broad adoption
5. Abuse prevention: Strong safeguards against misuse

Limitations Acknowledged

RAND explicitly notes that HEMs would "provide a complement to, but not a substitute for all, export controls." Key limitations include:

• Cannot prevent all circumvention—improvements in algorithmic efficiency decrease compute required for given capabilities
• Require ongoing enforcement infrastructure costing $10-200M annually
• Create attack surfaces for adversaries—must defend against state-level actors
• May be defeated by determined state actors with sufficient resources
• New chips must gain significant market share before affecting adversary capabilities (5-10 year cycle)

Location Verification Technology

Location verification has emerged as the most concrete near-term HEM proposal, with active prototyping on Nvidia H100 chips.

How Delay-Based Verification Works

Unlike GPS (which cannot penetrate data center walls and is easily spoofed), delay-based verification uses the physics of signal propagation:

1. A trusted "landmark server" at a known location sends a cryptographic challenge to the chip
2. The chip responds with its authenticated identity
3. By measuring round-trip delay based on the speed of light, servers can verify the chip is within a certain distance
4. Multiple landmark servers can triangulate approximate location without revealing exact position

Implementation Status

Implementation Requirements

According to IAPS analysis, scaling location verification would require:

1. Firmware update allowing AI chips to perform rapid location verification (estimated 6 months)
2. Landmark network of trusted servers near major data centers worldwide
3. Policy framework defining who operates servers and what actions follow verification failure

Comparison with Alternative Approaches

Implementation Considerations

Current Industry Practices

Hardware-enabled mechanisms are already widely used in defense products and commercial contexts:

Extending these mechanisms for governance involves primarily scope and purpose changes rather than fundamental technical innovation. The Trusted Platform Module (TPM) standard, endorsed by NSA for device attestation, provides a foundation that could be extended for AI governance.

Required Infrastructure

Effective HEM deployment would require:

flowchart LR
  subgraph CHIP["Chip-Level"]
      A[Hardware Security Module]
      B[Attestation Capability]
      C[Tamper Detection]
  end

  subgraph NETWORK["Network Level"]
      D[Licensing Servers]
      E[Monitoring Infrastructure]
      F[Update Distribution]
  end

  subgraph GOVERNANCE["Governance Level"]
      G[Policy Definition]
      H[Verification Authority]
      I[Enforcement Authority]
  end

  A --> D
  B --> E
  C --> I
  D --> G
  E --> H
  F --> G

  style CHIP fill:#e1f5ff
  style NETWORK fill:#fff3cd
  style GOVERNANCE fill:#d4edda

Cost Estimates

Cost estimates are highly uncertain given the nascent state of HEM development:

For comparison, the U.S. and EU have each invested approximately $10 billion through their Chips Acts in semiconductor manufacturing subsidies.

Risk Analysis

Security Risks

The RAND workshop emphasized that HEMs must be "robustly secured against skilled, well-resourced attackers," potentially including state-level adversaries:

Privacy Risks

Privacy-preserving measures are essential to uphold established data and code privacy norms. If not implemented carefully, HEMs could enable harmful surveillance:

Critics have drawn comparisons to the Clipper Chip controversy of the 1990s, when the U.S. government proposed mandatory backdoors for encrypted communications. Advocates counter that location verification is fundamentally different—revealing only where chips are, not what they compute.

Abuse Risks

Strategic Assessment

Arguments For HEMs

Arguments Against HEMs

Where HEMs Might Be Appropriate

Given the risk/benefit tradeoffs, RAND analysis suggests HEMs may be appropriate for narrow, high-value use cases:

Key insight from RAND: "Although it is premature to definitively endorse the use of HEMs in such high-performance chips as GPUs, dismissing HEM use outright is equally premature."

HEM Governance Ecosystem

The following diagram shows how HEMs fit within the broader AI governance landscape:

flowchart TD
  subgraph POLICY["Policy Layer"]
      A[Export Controls<br/>3A090/4A090] --> B[Compute Thresholds<br/>10^26 FLOP]
      B --> C[Reporting<br/>Requirements]
  end

  subgraph HEM["Hardware-Enabled Mechanisms"]
      D[Location<br/>Verification] --> E[Delay-Based<br/>Attestation]
      F[Licensing<br/>Controls] --> G[Offline<br/>Renewal]
      H[Usage<br/>Monitoring] --> I[Training Run<br/>Detection]
  end

  subgraph ACTORS["Key Actors"]
      J[BIS/Commerce<br/>Dept]
      K[Chip Makers<br/>Nvidia/AMD]
      L[Data Center<br/>Operators]
  end

  subgraph OUTCOMES["Outcomes"]
      M[Smuggling<br/>Deterrence]
      N[Compliance<br/>Verification]
      O[Enforcement<br/>Actions]
  end

  A --> D
  A --> F
  B --> H
  J --> D
  K --> E
  L --> G
  E --> M
  G --> N
  I --> O

  style POLICY fill:#e1f5ff
  style HEM fill:#fff3cd
  style ACTORS fill:#d4edda
  style OUTCOMES fill:#f8d7da

International Dimensions

Coordination Challenges

Relationship to Export Controls

HEMs would function alongside export controls:

Future Research Needs

Technical Research

Policy Research

Risks Addressed

Key Projects and Legislation

• FlexHEGProjectFlexHEG (Flexible Hardware-Enabled Guarantees)FlexHEG is a nascent but technically serious proposal to embed tamper-resistant governance processors into AI accelerators, enabling cryptographically verifiable enforcement of international AI com... - Yoshua Bengio's $4.1M project developing Guarantee Processor and Secure Enclosure architectures for hardware-enabled governance
• Chip Security ActPolicyChip Security ActThe Chip Security Act (H.R. 3447/S. 1705) would require location verification mechanisms on advanced AI chips to prevent export control evasion, representing a concrete compute governance intervent... - Bipartisan U.S. bill requiring location verification mechanisms on advanced AI chips
• Stop Stealing Our Chips ActPolicyStop Stealing Our Chips ActThe Stop Stealing Our Chips Act (Senate Apr. 2025, House Dec. 2025) creates an SEC-modeled whistleblower incentive program at BIS to enforce AI chip export controls against smuggling to China and o... - Whistleblower incentive program for AI chip smuggling enforcement
• CHIPS AllianceOrganizationCHIPS AllianceOpen-source hardware development organization under the Linux Foundation. Hosts the Caliptra project (open-source silicon Root of Trust) with contributors including AMD, Google, Microsoft, NVIDIA, ... / Caliptra - Open-source silicon Root of Trust with cross-vendor support from AMDOrganizationAMDAdvanced Micro Devices (AMD) is the principal competitor to Nvidia in AI accelerator chips, with its Instinct MI300 series GPUs targeting AI training and inference workloads. Founded in 1969 and he..., Google, Microsoft, NVIDIAOrganizationNVIDIANVIDIA holds 90-95% of the AI accelerator market, with its H100, H200, and B200 GPUs powering virtually all frontier AI training. Revenue reached $130.5B in FY2025 (114% YoY growth), driven almost ...
• Lucid ComputingOrganizationLucid ComputingStartup building a hardware-rooted zero-trust verification platform for AI workloads. Uses Trusted Execution Environments (Intel TDX, NVIDIA H100 Confidential Computing) and latency-based location ... - Startup building hardware-rooted zero-trust verification using TEEs
• Hardware Mechanisms for International AI AgreementsAnalysisHardware Mechanisms for International AI AgreementsA comprehensive technical taxonomy of hardware-based AI verification mechanisms—location attestation, TEEs, compute metering, interconnect limits, chip tracking, and remote disablement—mapped to sp... - Detailed analysis of technical mechanisms, threat models, and implementation challenges

Complementary Interventions

• Export ControlsPolicyUS AI Chip Export ControlsComprehensive empirical analysis finds US chip export controls provide 1-3 year delays on Chinese AI development but face severe enforcement gaps (140,000 GPUs smuggled in 2024, only 1 BIS officer ...Quality: 73/100 - Initial access controls that HEMs verify
• Compute ThresholdsConceptCompute ThresholdsComprehensive analysis of compute thresholds (EU: 10^25 FLOP, US: 10^26 FLOP) as regulatory triggers for AI governance, documenting that algorithmic efficiency improvements of ~2x every 8-17 months...Quality: 91/100 - Thresholds that HEMs could detect
• Compute MonitoringApproachCompute MonitoringAnalyzes two compute monitoring approaches: cloud KYC (implementable in 1-2 years, covers ~60% of frontier training via AWS/Azure/Google) and hardware governance (3-5 year timeline). Cloud KYC targ...Quality: 69/100 - Broader monitoring framework
• International RegimesConceptInternational Compute RegimesComprehensive analysis of international AI compute governance finds 10-25% chance of meaningful regimes by 2035, but potential for 30-60% reduction in racing dynamics if achieved. First binding tre...Quality: 67/100 - Governance for global coordination

Sources

Primary Research

• RAND Corporation (2024): Hardware-Enabled Governance Mechanisms - The most comprehensive public analysis of HEMs for AI governance, exploring attestation-based licensing and fixed-set approaches
• RAND Workshop Proceedings (2024) - Insights from 13 experts on HEM feasibility, risks, and design principles
• GovAI (2023): Computing Power and the Governance of AI - Framework for why compute is a feasible governance lever (detectable, excludable, quantifiable)
• CNAS (2024): Technology to Secure the AI Chip Supply Chain - Analysis of supply chain security and smuggling concerns
• IAPS: Location Verification for AI Chips - Technical analysis of delay-based verification mechanisms

Policy Developments

• BIS AI Diffusion Framework (2025) - Three-tier country classification and NVEU authorization requirements
• Sidley Austin Analysis (2025) - Seven key takeaways on export control developments
• Chip Security Act Coverage - Analysis of legislative proposals and industry response

Technical Background

• NSA TPM Use Cases (2024) - Government guidance on Trusted Platform Module applications
• Hardware-Enabled Mechanisms for Verifying AI Development (2025) - Academic analysis of HEM security and privacy considerations
• CNCF TPM Attestation for Confidential Computing (2025) - Technical implementation of hybrid attestation frameworks

News and Analysis

• CNBC: Nvidia Tracking Software (2025) - Coverage of Nvidia's pilot tracking program
• AI Frontiers: Location Verification Analysis - Deep dive on technical implementation and policy implications
• Data Innovation: Policy Critique (2024) - Arguments against hardware kill switches