DFAS-GDR: Governance deployment Roadmap — Implementation frameworks and adoption protocols for the DFAS Governance Convergence Doctrine.

Abstract

This manuscript provides the institutional deployment roadmap for the DFAS-AI Governance Convergence, a doctrinal framework for ethical and operational artificial intelligence governance in financial institutions. It delivers structured implementation strategies and standardized adoption protocols for banks, regulators, and auditors, addressing critical gaps identified in current AI governance practice. The proposed framework translates high-level governance principles into enforceable, auditable, and real-time operational systems, operationalizing recommendations of the OECD AI Principles and the EU AI Act within financial contexts. It integrates doctrinal components such as DFAS-FEP, DFAS-DAIF, DFAS-AAP, DFAS-CICP, DFAS-GIC, DFAS-CP, and DFAS-IFRS into institutional infrastructures, aligning ethical mandates with regulatory compliance and organizational resilience. This work addresses the urgent need for scalable, transparent, and context-sensitive AI governance in finance, moving beyond aspirational ideals to institutional reality, consistent with the growing call for accountable and auditable AI systems in high-stakes domains.