Artificial Intelligence systems are increasingly deployed in high-impact domains such as finance, energy infrastructure, and geopolitical decision-making. However, existing AI architectures lack a formal decision authority framework capable of ensuring accountability, auditability, and controlled execution under uncertainty. This work introduces AICOS (Artificial Intelligence Cognitive Operating System) as a governance-first Decision Authority Infrastructure designed to ensure that all AI-supported decisions remain human-final, non-autonomous, and fully auditable. The paper proposes a multi-dimensional decision risk model integrating probability, impact, irreversibility, time dynamics, and uncertainty, extending traditional risk formulations into a decision-centric framework. In addition, a deterministic decision pipeline is introduced, combining policy-as-code enforcement, fail-closed execution constraints, and cryptographically verifiable replay mechanisms. AICOS separates immutable governance logic from sector-specific execution systems, enabling scalable deployment across financial systems, energy infrastructure, and large-scale decision environments without compromising control or accountability. The framework is further supported by simulation-based decision validation, including Monte Carlo scenario analysis and counterfactual modeling, allowing decision-makers to evaluate stability, tail risk exposure, and irreversibility before execution. The central argument of this work is that future AI systems must transition from autonomous optimization models toward governed decision infrastructures, where authority, responsibility, and execution boundaries are explicitly defined and enforced. This study contributes to the emerging field of AI governance and decision science by introducing a structured, auditable, and regulator-aligned architecture designed to prevent irreversible failures in high-impact systems.
YASIN KALAFATOGLU (Tue,) studied this question.