"A Governance-First Framework for Risk-Controlled AI Decision Systems with Irreversibility Modeling (AICOS)"

This study proposes a governance-first framework for AI decision systems, addressing critical limitations in existing architectures related to risk control, decision accountability, and irreversible failure prevention. The proposed AICOS model integrates probabilistic risk modeling, irreversibility quantification, and governance enforcement into a unified decision infrastructure. The framework is validated through Monte Carlo simulation, demonstrating significant improvements in risk detection, failure containment, and system stability compared to traditional AI approaches. The results highlight the importance of incorporating governance constraints and irreversibility thresholds in high-impact AI systems, particularly in domains such as finance, energy, and critical infrastructure. This work contributes to the emerging field of AI risk governance by introducing a mathematically grounded and operationally enforceable decision architecture.