GEV Framework for AI Governance

• The GEV framework is a structured approach integrating governance, enforcement, and verification to manage advanced dual-use AI technologies.
• It employs mechanisms like the AI Oversight Body, qualified-majority decision making, and multilevel inspections to ensure timely compliance and robust oversight.
• The framework adapts dual-use treaty lessons into adaptive protocols and quantitative metrics that balance transparency, privacy, and efficiency.

The Governance, Enforcement, and Verification (GEV) framework provides an organizing structure for the governance of advanced, dual-use technologies—especially artificial intelligence—by integrating political, institutional, and technical controls. This tripartite scheme has been adapted from case studies of nuclear (IAEA), chemical (OPCW), biological (BWC), and export control regimes (Wassenaar Arrangement) to address the unique challenges in AI, such as rapid technological change, dual-use risks, global power asymmetries, non-compliance, and the need for robust, credible oversight (Wasil et al., 4 Sep 2024). The GEV framework defines dedicated pillars and operationalizes their functions through formalized structures, quantitative metrics, and adaptive protocols.

1. Governance: Institutions, Scope, and Decision Structures

The governance pillar establishes mandates, allocates authority, and delineates the treaty space. The AI Oversight Body (AIOB) is charged with promoting safe, beneficial AI, preventing clandestine high-risk AI development, and serving as a center for dispute resolution, standards setting, and referral to enforcement bodies.

• Scope and Coverage: The governance layer covers frontier model training—defined by agreed compute thresholds, model weights exceeding specified danger levels, and novel architectures with dual-use risk profiles.
• Organizational Structure:
  • Governing Council (GC): 35 seats, with 10 permanent (leading AI powers), and 25 rotating (regional diversity), requiring a two-thirds majority for major decisions (e.g., standards adoption, non-compliance referrals).
  • Scientific & Technical Advisory Panel (STAP): Rotating technical experts—empowered to propose updates to verification protocols in response to advances (mirroring BWC Meetings of Experts).
  • Secretariat: Headed by a Director-General (DG), operates permanent inspection teams, data-analysis labs, and rapid “challenge inspection” units.
• Formalization: Membership is represented by a vector $m \in \{0,1\}^{|N|}$, with $\sum m_i = 35$, $\sum_{i \in P} m_i = 10$ ($P =$ permanent states); proposals pass if $\sum_i w_i\,\mathrm{vote}_i \geq (2/3)\sum_i w_i$ ($w_i$ weighted for permanent seats).
• Design Lessons: Balanced permanent/rotating seats, standing technical review for agility, qualified-majority decision rules to avoid consensus gridlock, and benefit-sharing mechanisms (technical aid, safe-AI licensing).

2. Enforcement: Compliance Monitoring, Sanctions, and Incentives

The enforcement pillar operationalizes monitoring, detection, and penalty systems, providing mechanisms for both compliance incentivization and graduated sanctions.

• Monitoring Mechanisms:
  • Annual Reporting ("AI Confidence-Building Measures"): Includes compute-usage logs, model-development registries, anomaly summaries.
  • Routine Inspections: Scheduled, on-site review of high-risk AI facilities; remote telemetry/audit of hardware-enabled monitoring systems.
  • Challenge Inspections: Any member may request rapid inspections, with DG authorization within 12 hours.
• Sanctions and Incentives:
  • Penalty Function: $S(c) = S_0 + \alpha c^\beta$, where $c$ is the degree of non-compliance ($[0,1]$), $\alpha > 0$, $\beta > 1$; $S_0$ is a base warning.
  • Incentive Function: $I(p) = I_{\mathrm{max}}(1-e^{-\gamma p})$, with $p$ being the positive engagement level ($[0,1]$), $I_{\mathrm{max}}$ the maximum benefit (preferential access), and $\gamma$ a marginal scaling parameter.
• Escalation and Restoration: Referral to an “AI Security Council” analogue for severe cases; staged sanctions with triggers for automatic escalation; restorative participation post-sanction tracked by trend analysis of the $c$ index.

3. Verification: Technical Methods, Detection Probabilities, and Adaptive Assurance

Verification combines onsite and remote verification, telemetry, and statistical detection protocols to uncover and deter violations.

• Verification Methods:
  • Onsite Inspections: Teams of technical experts with up to 72 hours' access, leveraging IAEA-equivalent safeguarding equipment.
  • Remote Sensing and Telemetry: Aggregated black-box monitors, resource-use thresholds, satellite imagery for facility identification or anomaly detection.
  • Data Audits and Software Reviews: Code fingerprinting, hash-based attestation, and privacy-preserving model audits.
• Detection Probability Modeling:
  • Probability of detecting a violation after $f$ inspections per facility-year:

  $P_{\mathrm{det}} = 1 - e^{-\lambda f}$

  $\lambda$ is the method-specific detectability constant (increasing with intrusiveness). - Confidence in violation after $k$ positives (with false negative rate $\epsilon_{\mathrm{fn}}$):

  $$\mathrm{Pr}(\mathrm{violation}|k) = 1 - \epsilon_{\mathrm{fn}}^k$$

• Adaptive Verification: Baseline annual ("tiered") inspections, with surge capacity triggered by anomalous telemetry or AI Confidence-Building Measures; continuous recalibration of $\lambda$, $\epsilon_{\mathrm{fp}}$ (false positives), and $\epsilon_{\mathrm{fn}}$ (false negatives) via closed-loop feedback from STAP; use of machine learning for anomaly detection in activity logs.

4. Integrated Framework: Trade-offs, Metrics, and Interpillar Feedback

The unified GEV treaty architecture must account for multiple trade-offs and metrics, balancing effectiveness with feasibility and political sustainability.

• Key Trade-offs:

  • Transparency vs. Proprietary Secrecy: Adoption of zero-knowledge proofs for compute usage without disclosing sensitive model or data details.
  • Inspection Intrusiveness vs. Privacy: High $\lambda$ improves $P_{\mathrm{det}}$ but may raise resistance; aggregation and adaptive sampling mitigate risk.
  • Governance Efficiency vs. Inclusiveness: Qualified-majority rules speed adaptation but may dilute representation of less powerful stakeholders.
• Metrics for Ongoing Assessment:
  • Governance: Membership diversity (entropy index), average decision lead-time, protocol update frequency.
  • Enforcement: Timeliness and completeness of AI Confidence-Building Measures, number and type of sanctions, incentive uptake rate.
  • Verification: Mean detection probability per inspection frequency, error rates, number of anomaly-driven inspections.
  • Institutional Metrics: Voter turnout in GC ballots, protocol update frequency (STAP), membership retention, sanction response time, resource overhead for inspections, model-audit throughput.
• Feedback Loops: Audit outcomes loop back into standards setting and protocol updates; adaptive calibration of verification procedures ensures technical agility; sanction and remediation outcomes inform design of further governance and enforcement modifications.

Pillar	Actors	Processes / Metrics
Governance	GC, STAP, DG	Seat allocation, benefit-sharing, diversity index
Enforcement	Enforcement Council, member-state agencies	CBM reports, sanctions, incentive rate
Verification	Inspection teams, certified labs, secretariat	$P_{\mathrm{det}}$, FP/FN rates, inspections

5. Synthesis: Lessons from Dual-Use Treaties and Design Principles

The GEV framework’s design draws directly on historical security agreements governing nuclear, chemical, biological, and export-control domains. Key transferable lessons include:

• Permanent/Rotating Seat Balance: Technical leadership is sustained via permanent GC seats; global legitimacy is gained via rotating representation (cf. IAEA, OPCW).
• Technical-Expert Advisory Functions: Standing panels (e.g., STAP) ensure regulatory frameworks keep pace with rapid technological advances—a critical lesson from BWC and Wassenaar.
• Sanctions Architecture: Multistage, automatic triggers and escalation prevent failure modes observed in START and JCPOA (e.g., sanctions plateauing due to inability to reach consensus).
• Benefit-Sharing and Incentives: Technical aid and access to safe AI resources provide positive incentives, echoing OPCW/IAEA models.

The approach deliberately avoids pure consensus, instead implementing qualified-majority rules to mitigate risks of gridlock. Practical mechanisms for verification and enforcement are calibrated to maintain credible deterrence, rapid response to emergent threats, and sustainable institutional adaptation.

6. Strategic Challenges and Future Directions

The framework faces ongoing challenges in enforcement credibility, verification costs, privacy-utility tradeoffs, and the rapid evolution of technical capabilities.

• Detection Robustness: Adversaries may evade detection with sophisticated distributed training or adaptive architectures; continuous protocol refinement and surge verification are essential.
• Political Dynamics: Sustaining qualified-majority governance and incentive participation under shifting geopolitical realities requires both technical and diplomatic agility.
• Scaling and Institutional Sustainability: The model must accommodate increases in membership and the diversity of frontier AI actors, necessitating scalable verification and dispute resolution procedures.
• Cross-Pillar Integration: Ongoing synthesis of technical feedback, audit outcomes, and political developments is critical to maintaining alignment with rapidly evolving risk profiles.

This GEV framework constitutes a structured, enforceable, and adaptable approach to international AI governance, offering an overview of best practices from dual-use technology treaties and translating them into a regime capable of managing the multifaceted risks of advanced, frontier AI development (Wasil et al., 4 Sep 2024).