Map formally verified properties directly to regulatory requirements. Proofs don't expire or go stale. Compliance evidence that is mathematically locked in.
Today's compliance evidence is point-in-time. You pass an audit in January, and by March your AI has been updated, retrained, or reconfigured. Your evidence is stale. Your risk is live.
ΛXIØM's compliance evidence is mathematical. A formal proof is valid forever. When your governance properties are proven — not tested, not benchmarked, but proven — your compliance evidence doesn't decay.
Map ΛXIØM properties to high-risk AI system requirements: transparency, human oversight, technical robustness, and data governance.
Align with NIST AI Risk Management Framework functions: Govern, Map, Measure, Manage — with proven mathematical evidence.
Meet Monetary Authority of Singapore FEAT principles and EU Digital Operational Resilience Act requirements for AI in financial services.
Provide auditable evidence for the AI Management System standard. Mathematical proofs as conformity assessment artifacts.
Each regulatory requirement maps to one or more formally verified ΛXIØM properties. The mapping is explicit, auditable, and available for independent review.
| Regulatory Requirement | ΛXIØM Property | Status |
|---|---|---|
| Transparency & explainability | 72-bit deterministic fingerprint per interaction | Proven |
| Technical robustness | Lipschitz contraction (10% → 1.5% max change) | Proven |
| Human oversight | 4-verdict classification with human-in-loop escalation | Proven |
| Accuracy & reliability | Spectral gap convergence (γ ≈ 0.853) | Proven |
| Data governance | Conservation laws — governance mass cannot be lost | Proven |
| Bias & fairness | Deterministic processing — 0 stochastic components | Proven |
100+ properties proven. Each property is written in Lean 4 and available for independent mathematical audit under NDA. This is not a compliance checklist — it is a proof chain.
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