SAFE Governance Standards Whitepaper: Governance Architecture for AI-Mediated Child Safeguarding

SAFE (Safeguarding Architecture for Foundational Environments) is an independent governance architecture designed to address the absence of independent verification infrastructure for AI-mediated child safety. This publication introduces the SAFE governance framework, including: SAFE Council, an independent standards and governance body; SAFE Labs, an independent certification and validation authority; SAFE-001, a behavioural risk taxonomy focused on interaction trajectories rather than isolated content classification; and SAIL, an interaction-layer safeguarding implementation model operating post-deployment within live environments. The framework proposes structural separation between governance, certification, and implementation as a foundational requirement for credible AI safety assurance in environments where children are present. The publication also introduces the AGORA Taxonomy Alignment Appendix, mapping SAFE against the thematic governance domains used within the MIT AI Risk Repository and CSET’s ETO AGORA governance mapping framework. SAFE positions independent certification infrastructure as a missing governance layer within current AI safety ecosystems and proposes a scalable framework capable of supporting regulatory interoperability, institutional trust, procurement benchmarking, and ongoing behavioural verification for AI-mediated environments. Keywords: AI governance, child safety, independent verification, behavioural risk, governance infrastructure, AI regulation, digital governance, trust and safety, certification infrastructure, AI-mediated environments.