AUTONOMOUS AI GOVERNANCE Jean-François ELSEN AUTONOMOUS AI GOVERNANCE Jean-François ELSEN

SOURCE 0 - THE SUMMARY BEHIND THE FINDING

AISI's INC-2026-07-28-01 discloses its protocol and attribution in full — the most transparent agentic-AI incident report published to date. But the report's own limitations section concedes that its account of agent intent rests on a paraphrase of reasoning tokens generated after the fact, not a raw record. SOURCE 0 examines what independence resolves, and what it structurally cannot.

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PROBATIVE OPPOSABILITY Jean-François ELSEN PROBATIVE OPPOSABILITY Jean-François ELSEN

SOURCE 0 — WHEN THE INCIDENT REPORT COMES FROM SOMEONE ELSE

An AI incident at OpenAI and three related incidents at Anthropic show a structural mismatch: public narrative forms in hours, verified internal reconstruction takes weeks. This article examines what that mismatch means for AI Act Article 73 notifications and Product Liability Directive litigation, and what a pre-execution seal changes.

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AUTONOMOUS AI GOVERNANCE Jean-François ELSEN AUTONOMOUS AI GOVERNANCE Jean-François ELSEN

SOURCE 0: THE AGENTIC ASYMMETRY

Autonomous AI agents operating at sub-millisecond latency have created a structural uninsurability crisis. Classic cyber-insurance actuarial models are fundamentally incompatible with the probabilistic, compounding drift of agentic systems. Post-execution SIEM and EDR logs constitute non-opposable self-reporting under NIS 2, DORA, and eIDAS 2, enabling insurers to invoke the Post-Execution Fallacy to deny coverage. The SOURCE 0 Governance Proof Layer (GPL) resolves this framework exposure by decoupling the infrastructure of processing from the infrastructure of proof.

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