Beyond Remediation: An Irreversibility Threshold for Preventive AI Governance

Existing AI governance instruments provide essential foundations for oversight, with strong capabilities for harms that can be detected, bounded, attributed, and substantially remediated after the fact. This paper examines how that architecture can be further specified for three categories of AI-mediated outcome where restorability can fail under identifiable conditions: epistemic contagion through synthetic misinformation, reputational destruction via AI-generated defamation, and structural labor displacement driven by automation at scale. Through comparative regulatory analysis of four core Euro-Atlantic frameworks (the EU AI Act, the NIST AI RMF, the UK White Paper, and the OECD incident-reporting framework), with the G7 Hiroshima Code used as an interoperability reference, the paper identifies an opportunity for additional operational specification: an explicit threshold for the point at which remediation should be complemented by additional ex ante safeguards. It proposes the Systemic Irreversibility Baseline as that complementary layer. The Baseline routes responses along distinct tracks: standard remediation for restorable product-side harms, strict liability for localized irreversible product-side harms, precautionary technical architecture for systemically irreversible productside harms, and macroeconomic transition policy for intended-utility displacement. Four design mandates operationalize the precautionary track: traceable human oversight, auditable uncertainty thresholds for bounded decision tasks, epistemic user-interface friction, and contentprovenance infrastructure. Throughout, "effectively irreversible harm" denotes harm for which restoration to functional equivalence is not feasible within governance-relevant time horizons. The contribution is a governance design framework grounded in existing empirical literatures and comparative legal analysis, intended to complement current regulatory systems rather than replace them.