Structured safety case with empirical evaluation suites required before training bring-up.
💡 Safety Case: A rigorous technical dossier proving model risk is bounded below critical thresholds.
ARCHITECTURAL OVERVIEW
Safety Cases & Frontier Evaluation Protocols
A Safety Case is a structured, evidence-based technical dossier demonstrating that a frontier AI model's catastrophic risk profile is bounded below acceptable thresholds. Rather than relying on informal assertions, safety cases combine empirical red-teaming benchmarks, mechanistic guarantees, and hardened operational safeguards.
AI Safety Levels (ASL / RSP Frameworks)
Defines tiered security commitments (ASL-1 to ASL-4) inspired by biosafety levels. Transitioning to higher levels mandates specific containment controls (e.g., air-gapped training, multi-party key authorizations).
Dangerous Capability Evaluations
Quantitative thresholds for: (1) Autonomous Cyber Offense (ability to find and exploit zero-day vulnerabilities), (2) CBRN Synthesis Assistance, (3) Autonomous Self-Replication (acquiring cloud servers and paying with cryptocurrency).
Independent Third-Party Auditing
Pre-deployment evaluations conducted by external security auditors (e.g. US/UK AI Safety Institutes) with unfettered white-box model access and canary protections.
MATHEMATICAL FORMULATION · BOUNDED RISK PROBABILITY CONDITION
A valid safety case demonstrates that either the model's capability remains strictly below the critical risk threshold T_crit, or operational containment mechanisms have failure probability bounded below 1e-6.
REAL-WORLD PRODUCTION ENGINEERING
Maintain continuous automated safety regression suites: run 10,000+ adversarial canary tests across every fine-tuning checkpoint.
Implement cryptographic kill-switches and distributed consensus shutdown protocols for frontier model clusters.
Publish structured Safety Case summaries alongside technical model cards for public accountability.