Hardware-Enabled Governance

📋Page Status

Page Type:ResponseStyle Guide →Intervention/response page

Quality:70 (Good)⚠️

Importance:78.5 (High)

Last edited:2026-01-29 (3 days ago)

Words:3.6k

Structure:

📊 25📈 3🔗 11📚 70•10%Score: 15/15

LLM Summary:RAND analysis identifies attestation-based licensing as most feasible hardware-enabled governance mechanism with 5-10 year timeline, while 100,000+ export-controlled GPUs were smuggled to China in 2024 demonstrating urgent enforcement gaps. Location verification prototyped on H100 chips offers medium-high technical feasibility but raises significant privacy/abuse risks; appropriate only for narrow use cases like export control verification and large training run detection.

Critical Insights (4):

Counterint.The appropriate scope for HEMs is much narrower than often proposed - limited to export control verification and large training run detection rather than ongoing compute surveillance or inference monitoring.S:4.5I:4.0A:4.5
ClaimHardware-enabled governance mechanisms (HEMs) are technically feasible using existing TPM infrastructure but would create unprecedented attack surfaces and surveillance capabilities that could be exploited by adversaries or authoritarian regimes.S:4.0I:4.5A:4.0
Quant.Implementation costs for HEMs range from $120M-1.2B in development costs plus $21-350M annually in ongoing costs, requiring unprecedented coordination between governments and chip manufacturers.S:3.5I:4.0A:4.0

Issues (2):

QualityRated 70 but structure suggests 100 (underrated by 30 points)
Links21 links could use <R> components

Quick Assessment

Dimension	Assessment	Evidence
Technical Feasibility	Medium-High	Location verification already prototyped on H100 chips; TPM technology widely deployed
Implementation Timeline	5-10 years	Requires chip design cycles (2-3 years) plus deployment; RAND estimates significant market penetration needed
Privacy Risk	Medium-High	Could enable compute surveillance; delay-based verification reveals only coarse location data
Security Risk	High	Creates new attack surfaces; must defend against state-level adversaries per RAND workshop
Abuse Potential	High	Authoritarian regimes could misuse for suppression; requires international governance safeguards
Current Status	Early Research	RAND working paper (2024); Chip Security Act proposed in Congress; Nvidia piloting tracking software
Grade	B-	High potential but significant risks; appropriate only for narrow use cases

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

Legislation	Sponsors	Key Provisions	Status
Chip Security Act (CSA)	Sen. Tom Cotton, Reps. Bill Huizenga, Bill Foster	Requires geolocation tracking of GPUs; 180-day implementation timeline	Introduced May 2024
Foster Tracking Bill	Rep. Bill Foster (D-IL)	Embedded tracking technology; remote disable capability for unlicensed chips	In preparation
AI Diffusion Framework	BIS (Biden Admin)	Three-tier country system; location verification for NVEU authorization	Published Jan 2025; rescinded May 2025

Industry Response

Actor	Position	Actions
Nvidia	Cautious cooperation	Piloting opt-in tracking software; explicitly states “no kill switch”
Semiconductor Industry Association	Opposed to CSA	Letter urging reconsideration of “burdensome” tracking requirements
Google	Already implementing	Uses delay-based tracking for in-house TPU chips
China	Strongly opposed	Warning Nvidia against tracking features; launched security investigation into Nvidia chips

Enforcement Gap

Current export controls face significant enforcement challenges:

Metric	Estimate	Source
Smuggled GPUs to China (2024)	100,000+ (range: tens of thousands to 1 million)	CNAS upcoming report
Value of chips diverted in 3 months	$1 billion	Financial Times investigation
Entities added to Entity List (2025)	65 new Chinese entities	BIS actions
Tier 2 GPU cap (2025-2027)	≈50,000 GPUs	AI Diffusion Framework

Technical Mechanisms

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

Loading diagram...

Mechanism Overview

Mechanism	Description	Technical Feasibility	Governance Use	Risk Profile
Remote Attestation	Cryptographically verify hardware state and software configuration	High	Verify chips running approved firmware	Medium
Secure Enclaves	Isolated execution environments for sensitive operations	High	Protect governance checks from tampering	Low-Medium
Usage Metering	On-chip tracking of compute operations	Medium	Monitor for large training runs	Medium
Cryptographic Licensing	Require digital license for operation	Medium	Control who can use chips	Medium-High
Geolocation	Track physical location of chips	Medium	Enforce geographic restrictions	High
Remote Disable	Ability to shut down chips remotely	Medium-High	Enforcement mechanism	Very High
Workload Detection	Identify specific computation patterns	Low-Medium	Detect prohibited uses	Medium-High

Trusted Platform Module (TPM) Foundation

Many HEM proposals build on existing Trusted Platform Module technology:

Feature	Current TPM	Enhanced for AI Governance
Secure boot	Verify startup software	Verify AI framework integrity
Attestation	Report device state	Report training workload characteristics
Key storage	Protect encryption keys	Store governance credentials
Sealed storage	Encrypt to specific state	Bind data to compliance state

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 Heim, 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

Approach	Mechanism	Use Case	RAND Assessment
Offline Licensing	Renewable licenses limit processing per chip; requires authorization from chipmaker or government	Prevent unauthorized users from utilizing illicitly obtained chips	Most feasible; builds on existing TPM infrastructure
Fixed Set	Restricts networking capabilities to prevent aggregation of computing power	Prevent large-scale unauthorized training clusters	Technically challenging; requires chip redesign

Proposed Mechanisms Detail

Mechanism	RAND Assessment	Implementation Path	Timeline Estimate
Attestation-based licensing	Most feasible	Build on existing TPM infrastructure	2-3 years
Compute tracking	Technically challenging	Would require chip redesign	3-5 years
Geographic restrictions	Moderate feasibility	Delay-based verification (not GPS)	6 months for firmware; 2+ years for deployment
Remote disable	Technically feasible	Requires fail-safe design	3-5 years

Design Principles (RAND)

Proportionality: Governance mechanisms should match risk levels
Minimal intrusiveness: Collect only necessary information
Fail-safe design: Errors should default to safe states
International coordination: Effective only with broad adoption
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:

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

Property	Specification
Location precision	Coarse-grained only (country/region level)
Data revealed	Does not expose what computation is occurring or data being processed
Privacy model	Similar to consumer devices (iPhones can be remotely located/disabled)
Spoofing resistance	Higher than GPS; requires physical proximity to landmark servers

Implementation Status

Milestone	Status	Source
Proof of concept on H100	Completed	AI Frontiers
Nvidia tracking software pilot	Active	CNBC
H100 hardware security features	Already present	Firmware verification, rollback protection, secure non-volatile memory
Encryption keys for tracking	Already embedded	Future of Life Institute analysis of Nvidia documentation
BIS policy integration	Partial	NVEU authorization conditional on location verification capability

Implementation Requirements

According to IAPS analysis, scaling location verification would require:

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

Comparison with Alternative Approaches

Approach	Precision	Privacy	Spoofability	Data Center Compatibility
GPS	High	Low	High (easily spoofed)	Low (signals blocked)
IP geolocation	Low	Medium	High (VPNs)	High
Delay-based verification	Medium	High	Low	High
Cell tower triangulation	Medium	Low	Medium	Variable

Implementation Considerations

Current Industry Practices

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

Feature	Current Use	AI Governance Extension	Example Deployment
Device attestation	DRM, enterprise security	Verify compute environment	Apple iPhone (prevents unauthorized apps)
Remote wipe	Lost device protection	Enforcement mechanism	Consumer smartphones
Licensing servers	Software activation	Compute authorization	Windows, Adobe products
Firmware verification	Security patches	Policy updates	Nvidia H100 (already has this)
Hardware attestation	Chip integrity	Compliance monitoring	Google TPUs (verify chips not compromised)
TPM-based anti-cheat	Video game integrity	Prevent compute circumvention	Many modern games

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:

Loading diagram...

Cost Estimates

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

Component	Development Cost	Ongoing Cost	Who Bears Cost	Notes
Chip modifications	$10-200M	$1-20M/year maintenance	Manufacturers	Similar to existing security feature development
Landmark server network	$10-100M	$1-50M/year	Governments or public-private partnership	Depends on geographic coverage
Verification infrastructure	$10-200M	$10-50M/year	Governments	Software, personnel, legal framework
Enforcement systems	$10-50M	$10-30M/year	Governments	Investigation, penalties, coordination
Compliance systems	$1-5M per company	$1.5-2M/year per company	Operators	Integration with existing IT infrastructure

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:

Risk	Description	Mitigation	Severity
New attack surface	Governance mechanisms can be exploited; critical infrastructure integration increases stakes	Security-first design; formal verification	High
Key management	Compromise of governance keys catastrophic	Distributed key management; rotation; HSMs	Critical
Insider threats	Those with access could abuse systems	Multi-party controls; auditing; whistleblower protections	High
Nation-state attacks	Advanced adversaries target infrastructure	Defense in depth; international redundancy; robust anti-tamper techniques	Critical
Supply chain attacks	Compromised chips introduced during manufacturing	Trusted foundry programs; hardware verification	High

Privacy Risks

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

Risk	Description	Mitigation	Privacy-Preserving Alternative
Compute surveillance	Detailed visibility into all computation	Minimal logging; privacy-preserving attestation	Delay-based verification reveals only coarse location, not computation content
Location tracking	Continuous geographic monitoring	Limit to high-risk contexts only	Country/region level only; no exact coordinates
Workload analysis	Infer sensitive research activities	Aggregate reporting; differential privacy	Verify workload size without revealing type
IP exposure	Model weights or training data could leak	Hardware isolation; secure enclaves	Confidential computing preserves IP while enabling attestation

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

Risk	Description	Mitigation
Authoritarian use	Regimes use for oppression	International governance; human rights constraints
Competitive weaponization	Block rival companies/countries	Neutral administration
Mission creep	Expand beyond AI safety	Clear legal constraints; sunset provisions
Capture	Governance controlled by incumbents	Diverse oversight; transparency

Strategic Assessment

Arguments For HEMs

Argument	Reasoning	Confidence
Unique verification capability	Software-only verification can be circumvented	High
Enforcement teeth	Export controls meaningless without enforcement	Medium
Scalability	Can govern millions of chips automatically	Medium
International coordination	Common technical standard enables cooperation	Medium
Proportional response	Different levels for different risks	Medium

Arguments Against HEMs

Argument	Reasoning	Confidence
Privacy threat	Creates unprecedented compute surveillance	High
Attack surface	New vulnerabilities in critical infrastructure	High
Authoritarian tool	Will be adopted and abused by repressive regimes	High
Circumvention	Sufficiently motivated actors will defeat	Medium
Chilling effect	Discourages legitimate AI research	Medium
Implementation complexity	International coordination very difficult	Medium-High

Where HEMs Might Be Appropriate

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

Context	Appropriateness	Rationale	Current Policy Status
Export control verification	Medium-High	Extends existing policy; addresses $1B+ diversion problem	NVEU authorization requires location verification capability
Large training run detection	Medium	Clear capability threshold (10^26 FLOP under EO 14110)	Under consideration
Post-incident investigation	Medium	Limited, targeted use	No current policy
Ongoing surveillance of all compute	Low	Disproportionate; massive privacy cost	Workshop consensus against broad scope
Inference monitoring	Very Low	Massive scope, limited benefit; chilling effect on AI deployment	Not under serious consideration

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:

Loading diagram...

International Dimensions

Coordination Challenges

Challenge	Description	Current Status	Potential Resolution
Chip manufacturing concentration	TSMC produces over 90% of advanced chips	Creates leverage but also single point of failure	Leverage market power for standards; diversify production
Three-tier country system	18 Tier 1 allies with no limits; ~120 Tier 2 with caps; ≈20 Tier 3 prohibited	Creates pressure for circumvention	Harmonized international controls
Technology transfer	HEM tech could be misused by authoritarian regimes	No international agreement	Careful capability scoping; human rights conditions
Verification of verifiers	Who monitors governance systems?	No multilateral framework	International oversight body (IAEA model discussed)
Chinese opposition	China has warned Nvidia against tracking features and launched security investigations	Creates market pressure on manufacturers	May require accepting reduced China market access

Relationship to Export Controls

HEMs would function alongside export controls:

Control Type	What It Does	HEM Complement
Export licenses	Control initial transfer	Verify ongoing location
End-use restrictions	Require stated purpose	Verify actual use
Entity lists	Block specific actors	Prevent circumvention
Compute thresholds	Trigger requirements	Detect threshold crossing

Future Research Needs

Technical Research

Question	Importance	Current Status	Key Researchers/Orgs
Privacy-preserving attestation	Critical	Active research; confidential computing integration	CNCF, cloud providers
Tamper-resistant design	High	Robust anti-tamper techniques needed for state-level adversaries	Defense contractors, chip makers
Minimal-information verification	High	Delay-based verification prototyped	IAPS, academic researchers
Formal security analysis	High	Limited public analysis	Academic security researchers
Quantum-resistant cryptography	Medium	NSA TPM guidance highlights transition need	NIST, cryptography community

Policy Research

Question	Importance	Current Status	Key Researchers/Orgs
Appropriate scope limitations	Critical	RAND workshop recommends narrow scope	RAND, GovAI
International governance models	High	IAEA analogy discussed; no concrete proposals	Arms control community
Abuse prevention mechanisms	Critical	Identified as major concern; underexplored solutions	Civil society, human rights orgs
Democratic accountability	High	Underexplored; few governance proposals	AI governance researchers
Human rights conditions	High	Not yet integrated into proposals	Human Rights Watch, Amnesty

Risks Addressed

Risk	Mechanism	Effectiveness
Export control evasion	Ongoing verification	Medium-High
Unauthorized large training	Compute detection	Medium
Geographic restrictions	Location verification	Medium
Incident response	Remote disable capability	High (if implemented)

Complementary Interventions

Export Controls - Initial access controls that HEMs verify
Compute Thresholds - Thresholds that HEMs could detect
Compute Monitoring - Broader monitoring framework
International Regimes - 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

AI Transition Model Context

Hardware-enabled governance affects the Ai Transition Model through multiple factors:

Factor	Parameter	Impact	Confidence
Civilizational Competence	Regulatory Capacity	Enables verification of safety requirements even after hardware deployment; could increase regulatory capacity by 20-40%	Medium
Misalignment Potential	Human Oversight Quality	Remote attestation could verify AI systems are running approved workloads; enables workload verification without exposing IP	Medium
Transition Turbulence	AI Control Concentration	Risk of authoritarian misuse if governance mechanisms are captured; requires strong abuse prevention	Medium-High

Key uncertainties:

Whether algorithmic efficiency improvements will outpace hardware controls
Whether international coordination can be achieved given Chinese opposition
Whether narrow HEM use cases can resist scope creep

HEMs are high-risk, high-reward infrastructure requiring 5-10 year development timelines; RAND analysis suggests appropriate use cases limited to export control verification and large training run detection.