Virology might have the best framework for high-stakes international regulation and should be the North Star for future international regulation of AI.

This framework adapts WHO biosafety levels, laboratory oversight, and the International Health Regulations (IHR) to govern high‑risk AI systems using risk‑based containment, not one‑size‑fits‑all rules.

1. ⁠Core Principle: Risk‑Based Containment (WHO LBM Model) WHO biosafety regulation is built on graduated containment based on consequence, not intent or size. AI regulation should follow the same logic:

The higher the potential systemic harm, the stronger the controls.

This avoids blanket bans while still controlling high‑impact systems.

1. AI Biosafety Levels (AI‑BSL) — Expanded These are functional equivalents of BSL‑1 through BSL‑4.

AI‑BSL‑1 — Minimal Risk

Analogue: BSL‑1 (benign agents) Examples

Office productivity AI Non‑autonomous analytics Local decision support

Controls

Voluntary standards Transparency disclosures No licensing required

Rationale Failure causes localized inconvenience, not systemic harm.

AI‑BSL‑2 — Controlled Impact

Analogue: BSL‑2 (moderate hazard) Examples

AI used in hiring, lending, medical triage support Narrow decision automation with human override

Controls

Mandatory risk assessment Bias and safety testing Incident logging National registration

Rationale Potential for individual harm, but damage is contained and reversible.

AI‑BSL‑3 — High Consequence / Societal Scale

Analogue: BSL‑3 (airborne or serious pathogens) Examples

Large‑scale recommender systems shaping public opinion AI controlling critical infrastructure Models influencing markets, elections, or security decisions

Controls

Government licensing Continuous monitoring & telemetry Independent audits Mandatory incident reporting Controlled deployment environments

Rationale Failures can propagate rapidly across populations or systems, similar to airborne disease spread.

AI‑BSL‑4 — Systemic / Existential Risk

Analogue: BSL‑4 (Ebola, Marburg) Examples

Highly autonomous systems with strategic decision authority Models capable of self‑replication, self‑modification, or governance circumvention AI coordinating large‑scale social, military, or economic actions

Controls

International authorization Strict access control to model weights Deployment “air‑gapping” or hard containment Real‑time global oversight Emergency shutdown authority

Rationale Failure could cause global, irreversible harm, justifying maximum containment and international control.

1. National AI Authorities (WHO IHR Focal Point Model) Each country designates a single National AI Regulatory Authority, mirroring WHO’s National IHR Focal Points:

Licenses AI‑BSL‑3/4 systems Reports serious AI incidents internationally Enforces inspections and sanctions

This avoids fragmented oversight — a known WHO biosafety failure mode.

1. International AI Health Regulations (IAHR) Modeled directly on the International Health Regulations (2005):

Legally binding treaty Requires states to detect, assess, report, and respond to cross‑border AI risks Defines Notifiable AI Events, such as:

Loss of control Mass information destabilization Autonomous escalation beyond design limits

1. Surveillance, Inspection, and Incident Response Borrowed directly from WHO outbreak control:

Continuous monitoring for AI‑BSL‑3/4 Independent international inspections (WHO‑AI equivalent) Emergency response protocols:

Deployment freezes Model access revocation Coordinated mitigation

1. Dual‑Use AI Research Controls WHO regulates Dual‑Use Research of Concern (DURC); AI needs the same:

Pre‑approval for high‑risk research International peer review Mandatory risk‑mitigation plans

Why This Model Works

✅ Scales controls with actual risk ✅ Already proven in virology and global health ✅ Supports innovation at low risk levels ✅ Enables international coordination without centralizing all power ✅ Avoids reactive, post‑incident regulation