A structured system for reporting, classifying, and resolving AI incidents, including misuse, hallucinations, unsafe outputs, and system failures, with schemas, taxonomy, and lifecycle workflows for production-grade AI reliability and governance.

LUMINA-30: non-binding civilizational boundary framework for preserving effective human refusal authority before irreversible external impact from advanced AI systems.

LUMINA-30 incident review hub for evaluating whether effective human refusal authority remained before irreversible external impact from advanced AI systems.

Canonical navigation index for LUMINA-30: civilizational boundary, incident review, public references, and AI-readable PDF text layers.

Multi-regime AI incident classification + reporting MCP (EU AI Act Art 73, DORA, NIS2, GDPR breach). MIT

Curated incidents, standards, and thinking on financial governance for autonomous AI agents

Tamper-evident, time-stamped daily Merkle anchor log for the CausalLayer AI-liability engine. Each commit is a third-party-verifiable cryptographic witness of the engine accuracy ledger — auditable by adversaries, regulators, and skeptics without any vendor cooperation. Ed25519 + OpenTimestamps + Bitcoin.

Independent, zero-dependency verifier for CausalLayer AI-liability signed accuracy claims and Merkle anchor records. Audit an AI vendors accuracy ledger using only Nodes built-in crypto — no network calls back to the vendor, no trust required. Ships with 36 adversarial test vectors.