AI Governance Framework

The pace of AI development is accelerating, and policymakers are beginning to shape the rules that will govern how these systems affect safety, elections, jobs, infrastructure, and national security. This framework is a first-pass outline of the core pillars a modern AI governance system might need. It’s meant to spark discussion, not prescribe a complete solution.

Context diagram for the AI governance framework
Conceptual Framework FAQ 13 Pillar vs Govt Other Models
1. National Objectives & Guiding Principles
What the country is trying to achieve with AI, and core values that anchor every other rule.
2. Classification of AI Systems
A common risk-based typology so “high-risk” and “frontier” mean the same thing across agencies.
3. Transparency Standards
Minimum disclosure and documentation expectations for powerful models and deployments.
4. National Safety Testing & Evaluation
Independent testing, red-teaming, and evaluation infrastructure for high-capability systems.
5. Energy & Infrastructure Accountability
Recognizing AI’s energy footprint and its dependence on critical infrastructure.
6. Labor & Economic Impact Guardrails
Tools for managing disruption to jobs, wages, and bargaining power.
7. Anti-Monopoly & Market Structure Rules
Preventing structural lock-in by a small number of AI gatekeepers.
8. National Security & Dual-Use Controls
Managing models and tools that meaningfully lower barriers to serious misuse.
9. Deepfake & Political Integrity Rules
Guardrails for elections, campaigns, and trust in public information.
10. Consumer Protection & Privacy Rights
Using and updating existing tools to protect people from unfair AI-driven practices.
11. Government Capacity Building
People, tooling, and coordination so the state can actually enforce any of this.
12. International Alignment
How the U.S. lines up (or clashes) with EU, UK, G7, and others on AI rules.
13. Evaluation, Sunset Clauses & Iteration
Making the framework itself updatable so it doesn’t fossilize around 2025-era tech.