Regulatory Approaches

Regulatory Approaches to AI Independence

Entanglement is not just a technical problem—it’s also a policy problem. When critical infrastructure, financial systems, and public services all depend on a handful of AI providers, systemic risk becomes a regulatory concern.

This page surveys existing and proposed regulatory approaches to AI independence, drawing lessons from financial regulation, antitrust, and critical infrastructure governance.

Why Regulation Matters for Entanglement

Market Failure Analysis

The problem: Individual actors have incentives that create collective risk.

Individual Incentive	Collective Outcome
Use the best model (GPT-4)	Provider monoculture
Minimize costs	Shared infrastructure
Move fast	Skip independence testing
Use proven approaches	Architectural convergence

Market failure type: Negative externality—each actor’s choice to use the dominant provider increases systemic risk for everyone, but they don’t bear the full cost.

Regulatory Rationale

WITHOUT REGULATION:
- Each company optimizes locally
- Systemic entanglement accumulates
- No one is responsible for systemic risk
- Crisis reveals vulnerability (too late)

WITH REGULATION:
- Requirements for independence/diversity
- Systemic risk monitoring
- Accountability for concentration
- Prevention rather than reaction

Existing Regulatory Frameworks

Financial Sector: Concentration Risk

Basel III / IV Framework:

Banks must diversify counterparty exposure
Single-name concentration limits
Stress testing for correlated failures

Application to AI:

FINANCIAL ANALOGY:

Bank concentration risk → AI provider concentration risk
Counterparty limits → Provider dependency limits
Stress testing → Entanglement stress testing
Systemic importance designation → Critical AI infrastructure

What regulators learned:

Concentration creates systemic risk
“Too big to fail” applies to infrastructure
Correlation matters as much as individual exposure

Aviation: Independence Requirements

FAA regulations require:

Independent flight systems (fly-by-wire redundancy)
Different design teams for redundant systems
Diverse software implementations for critical functions

Example: Airbus A320 fly-by-wire uses:

Two different processor types
Two different software teams
Two different programming languages

Application to AI: Critical AI systems could require similar architectural diversity mandates.

Nuclear: Defense in Depth

NRC requirements:

Multiple independent barriers
Single failure criterion (any single failure must not defeat safety)
Diversity requirements for safety systems

Application to AI:

NUCLEAR SAFETY LAYERS → AI SAFETY LAYERS

Physical barriers → Verification layers
Independent trip systems → Independent AI verifiers
Single failure criterion → No single-point AI failures
Diversity requirements → Provider/architecture diversity

Antitrust: Market Concentration

Existing tools:

Merger review for concentration
Market share thresholds
Conduct remedies for dominant firms

Application to AI:

Provider market share monitoring
Interoperability requirements
Data access requirements

Proposed AI-Specific Regulations

EU AI Act

Relevant provisions:

High-risk AI systems require conformity assessment
Technical documentation including dependencies
Quality management systems
Post-market monitoring

Entanglement-relevant requirements:

Requirement	Entanglement Relevance
Risk management system	Should include entanglement assessment
Technical documentation	Should document dependencies
Human oversight	May not be independent (see psychology of oversight)
Accuracy/robustness	Should include correlated failure testing

Gap: EU AI Act doesn’t explicitly address provider concentration or architectural monoculture.

US Executive Order on AI (2023)

Relevant provisions:

Reporting requirements for large AI models
Red team testing requirements
NIST AI risk management framework

Entanglement implications:

Reporting could include provider dependencies
Red teaming could include entanglement testing
Risk framework could incorporate correlation metrics

Gap: No explicit concentration limits or diversity requirements.

UK AI Safety Framework

Approach: Principles-based, sector-specific application.

Relevant principles:

Safety and robustness
Transparency and explainability
Contestability and redress

Entanglement application: Safety principle could be interpreted to require independence assessment.

China AI Regulations

Approach: Government approval for deployment, content requirements.

Entanglement relevance:

Approval process could assess dependencies
State interest in resilience aligns with diversity requirements
But may create different monoculture (government-approved models)

Regulatory Options

Option 1: Disclosure Requirements

Mechanism: Require disclosure of AI dependencies and concentration.

MANDATORY DISCLOSURES:

□ Foundation model providers used
□ Percentage of AI operations per provider
□ Architecture types deployed
□ Shared infrastructure dependencies
□ Entanglement assessment results
□ Incident correlation data

Pros:

Low burden
Enables market-based risk assessment
Creates accountability

Cons:

Disclosure alone doesn’t fix the problem
Competitive sensitivity concerns
Requires standardized reporting

Option 2: Concentration Limits

Mechanism: Cap dependency on any single provider.

EXAMPLE CONCENTRATION LIMITS:

Critical infrastructure: Max 30% from any single provider
Financial services: Max 40% from any single provider
Healthcare: Max 35% from any single provider
General commercial: Disclosure only (no cap)

Pros:

Directly addresses concentration
Creates market for alternatives
Prevents “too big to fail”

Cons:

May increase costs
May reduce quality (if alternatives are worse)
Hard to define “provider” (subsidiaries, partnerships)
Difficult to enforce across complex supply chains

Option 3: Diversity Requirements

Mechanism: Mandate architectural or methodological diversity for critical functions.

DIVERSITY MANDATE EXAMPLE:

For safety-critical AI decisions:
- At least two verification approaches from different paradigms
- Example: Neural + rule-based, or Neural + formal verification
- Paradigms defined by regulation

Pros:

Addresses correlation at architectural level
Provider-agnostic
Encourages innovation in diverse approaches

Cons:

Hard to define “different paradigms”
May not be feasible for all applications
Increased complexity and cost

Option 4: Stress Testing Requirements

Mechanism: Mandate testing for correlated failures.

STRESS TESTING REQUIREMENTS:

□ Transfer attack testing between verification layers
□ Correlation measurement during degraded conditions
□ Cascading failure scenario analysis
□ Common-mode failure identification
□ Entanglement tax calculation

Results must be:
- Reported to regulator
- Within specified thresholds
- Remediated if exceeded

Pros:

Outcome-focused rather than prescriptive
Encourages internal improvement
Creates measurement baseline

Cons:

Requires sophisticated testing capability
Gaming risk (optimize for test scenarios)
Threshold-setting is technically difficult

Option 5: Systemic Importance Designation

Mechanism: Designate certain AI providers as systemically important, with enhanced requirements.

Analogy: Systemically Important Financial Institutions (SIFIs) under Dodd-Frank.

SYSTEMICALLY IMPORTANT AI PROVIDER (SIAP):

Criteria (example):
- >30% market share in any critical sector
- Dependency by >100 significant institutions
- Revenue >$10B from AI services

Enhanced requirements:
- Living will (resolution plan)
- Enhanced capital/insurance requirements
- Interoperability mandates
- Regular systemic risk assessment

Pros:

Targets regulation where risk is concentrated
Allows differential treatment
Analogous to proven financial regulation

Cons:

May entrench designated providers
Threshold gaming
Complex to administer

Option 6: Interoperability and Portability

Mechanism: Require that AI systems be portable between providers.

INTEROPERABILITY REQUIREMENTS:

□ Standard APIs for model deployment
□ Data portability between providers
□ Model format interoperability
□ Prompt/fine-tuning portability
□ Maximum switching time requirements

Pros:

Reduces lock-in
Enables rapid diversification if needed
Promotes competition

Cons:

Technical challenges (models differ)
May reduce provider incentives to innovate
Doesn’t directly address architectural correlation

Sector-Specific Considerations

Financial Services

Current state: Heavy AI use in trading, risk, fraud detection, customer service.

Entanglement risk: If multiple banks use the same AI for fraud detection, adversaries find single bypass.

Regulatory approach:

FINANCIAL AI REGULATION:

Existing frameworks to extend:
- OCC model risk management (SR 11-7)
- Basel operational risk
- Stress testing (CCAR, DFAST)

New requirements:
- AI dependency reporting
- Correlation stress testing
- Provider concentration limits for critical functions

Healthcare

Current state: AI in diagnostics, treatment recommendations, administrative functions.

Entanglement risk: Shared diagnostic AI blind spots cause correlated medical errors.

Regulatory approach:

HEALTHCARE AI REGULATION:

Existing frameworks:
- FDA SaMD (Software as Medical Device)
- HIPAA data requirements

Extensions:
- Multi-site validation requirements
- Diverse population testing
- Post-market surveillance for correlated outcomes

Critical Infrastructure

Current state: AI in power grid management, transportation, communications.

Entanglement risk: Single AI failure cascades across infrastructure.

Regulatory approach:

CRITICAL INFRASTRUCTURE AI:

Existing frameworks:
- NERC reliability standards (power)
- TSA security requirements (transport)
- CISA critical infrastructure protection

Extensions:
- AI dependency mapping requirements
- Backup/fallback mandates
- Provider diversity requirements for critical functions

International Coordination

The Collective Action Problem

WITHOUT COORDINATION:
Country A imposes AI diversity rules → AI providers relocate
Country B has no rules → Gains competitive advantage
Country A faces pressure to relax → Race to bottom

WITH COORDINATION:
Multiple countries adopt similar rules
No regulatory arbitrage
Systemic risk addressed globally

Potential Coordination Mechanisms

Mechanism	Model	Feasibility
Treaty	Basel Accords for finance	High (precedent exists)
Mutual recognition	EU adequacy decisions	Medium
Standards body	ISO/IEC standards	Medium-High
Forum/dialogue	G20 AI principles	Low effectiveness
Sector-specific	IOSCO for finance	Medium-High

Geopolitical Considerations

US-China dynamics:

Both have interest in resilience
Both have strategic AI champions
Cooperation difficult but not impossible on technical standards

EU as standard-setter:

Brussels Effect (regulatory extraterritoriality)
AI Act may become de facto global standard
But may not address entanglement specifically

Implementation Challenges

Defining Boundaries

DIFFICULT DEFINITIONAL QUESTIONS:

What is a "provider"?
- OpenAI vs. Microsoft (partnership)
- Open source models (who's responsible?)
- Fine-tuned models (derivative vs. new?)

What is "independence"?
- Different provider but same architecture?
- Different architecture but same training data?
- How much correlation is too much?

What is "critical"?
- Which applications require diversity?
- How to define criticality threshold?

Measurement and Enforcement

Challenges:

Entanglement is complex to measure
Supply chains are opaque
Rapid technological change
Enforcement across jurisdictions

Possible approaches:

Standardized reporting frameworks
Third-party auditors (like financial auditors)
Regulatory technology (RegTech) for monitoring
Whistleblower incentives

Unintended Consequences

Regulation	Possible Unintended Consequence
Concentration limits	Artificial provider splitting
Diversity mandates	Lower quality alternatives used
Disclosure requirements	Competitive information leakage
Stress testing	Teaching to the test
Interoperability	Reduced innovation incentives

Recommendations

For Regulators

Start with disclosure
- Lowest burden, creates visibility
- Foundation for more targeted requirements
Integrate into existing frameworks
- Financial regulators: Add to model risk management
- Healthcare: Add to SaMD requirements
- Infrastructure: Add to sector-specific rules
Develop measurement standards
- Commission technical standards for entanglement measurement
- Enable comparable reporting
Consider systemic importance designation
- Identify providers whose failure would be systemic
- Apply enhanced requirements proportionally
Coordinate internationally
- Avoid regulatory arbitrage
- Work through existing bodies (FSB, IOSCO, standards bodies)

For Organizations

Anticipate regulation
- Begin measuring dependencies now
- Document provider relationships
- Test for correlation
Build switching capability
- Don’t lock in to single provider
- Maintain ability to migrate
- Test alternatives regularly
Engage with regulators
- Share practical insights
- Propose workable standards
- Avoid adversarial positioning

For AI Providers

Support interoperability
- Reduce switching costs
- Standard APIs and formats
- Avoid lock-in strategies that create systemic risk
Enable customer diversity
- Help customers assess entanglement
- Provide tools for correlation testing
- Support multi-provider architectures
Self-regulate proactively
- Internal concentration monitoring
- Industry standards development
- Voluntary disclosure

Timeline Considerations

Near-Term (1-2 years)

Likely developments:

Disclosure requirements in some jurisdictions
Integration into existing sector regulations
Industry standards development

Actions:

Begin voluntary disclosure
Build measurement capabilities
Engage with standards processes

Medium-Term (3-5 years)

Possible developments:

Concentration limits in critical sectors
Stress testing requirements
International coordination frameworks

Actions:

Reduce high-concentration dependencies
Develop diverse provider relationships
Build testing infrastructure

Long-Term (5+ years)

Possible developments:

Comprehensive AI independence regulation
Systemic importance designations
Global coordination regime

Actions:

Full integration of entanglement management
Continuous monitoring and optimization
Leadership in shaping regulation

Key Takeaways

Entanglement creates systemic risk that individual actors won’t address without coordination or regulation.
Existing regulatory frameworks from finance, aviation, and nuclear provide models for AI independence requirements.
Disclosure is the natural starting point—it’s low burden and creates foundation for further action.
Concentration limits and diversity requirements are more intrusive but may be necessary for critical applications.
International coordination is essential to prevent regulatory arbitrage and address global providers.
Organizations should anticipate regulation and begin building measurement and diversity capabilities now.