Published April 1, 2026 | Version v1.0
Journal article Open

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

Authors/Creators

  • 1. Independent Researcher; Founder & Architect, AICOS®

Description

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.

Other (English)

This manuscript addresses a critical and emerging gap in artificial intelligence systems: the lack of integrated governance and irreversibility-aware risk control in autonomous decision-making environments. While existing frameworks emphasize model performance and high-level ethical guidelines, they do not provide operational mechanisms for controlling decision execution under uncertainty and systemic risk conditions.

The proposed AICOS framework introduces a unified approach that combines probabilistic risk modeling, irreversibility thresholds, and enforceable governance constraints within a single decision architecture. Unlike conventional AI systems, the framework explicitly incorporates decision authority, human oversight, and deterministic replay as core components.

The significance of this work lies in its applicability to high-impact domains where AI decisions may lead to irreversible consequences, including financial systems, energy infrastructure, and large-scale autonomous environments. By integrating risk, governance, and decision control at the architectural level, the study contributes to the development of safer and more controllable AI systems.

The manuscript is expected to be of interest to researchers and practitioners working in AI safety, risk modeling, decision systems, and governance frameworks.

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Additional details

References

  • Kalafatoğlu, Y. (2026). A Governance-First Framework for Risk-Controlled AI Decision Systems with Irreversibility Modeling (AICOS).