Supreme1 v3 Extension: A Defensive Evidence-Protection Package for AI Governance under Institutional Asymmetry

The progressive integration of artificial intelligence into regulatory and institutional environments has amplified structural asymmetries between individual contributors and large organizations. While prevailing AI governance frameworks emphasize compliance, transparency, and system-level risk mitigation, limited attention is devoted to the protection of individuals when informational imbalance or potential evidence of unacknowledged reuse emerges.

This work presents an extension to the Suprême1 v3 governance kernel: a defensive, evidence-gated package designed to support user protection under conditions of institutional asymmetry. The proposed module activates only in the presence of strong documentary evidence and provides structured guidance for evidence classification, preservation, and risk-aware decision-making. It explicitly avoids accusation, escalation, or legal adjudication, prioritizing proportionality, reversibility, and human-in-the-loop control.

The extension is intended as a complementary governance component, addressing ethical risk containment at the user level rather than institutional enforcement, and aims to reduce asymmetric harm in post-deployment AI contexts.