The emergence of Artificial General Intelligence (AGI) [12], while offering transformative opportunities—from revolutionizing science to addressing global challenges—also introduces unparalleled risks, particularly as black box models become increasingly opaque and difficult to interpret. Traditional human oversight, such as expert audits, becomes ineffective when dealing with systems that surpass human cognition. To address this challenge, this paper proposes a structured framework that incorporates standardized base models, a multi-phase development pipeline, and anomaly detection mechanisms to mitigate rogue AGI risks systematically. The framework enables proactive risk management by embedding safety measures into the AGI lifecycle while fostering innovation. One key feature is the systematic anomaly detection of rogue AGI through cross-referencing outputs, statistical monitoring, and iterative refinement, eliminating reliance on human interpretation of black-box systems. Simulation results show the feasibility of this approach, demonstrating a 100% detection rate for rogue AGI systems while maintaining a low false positive rate (5.3%). These findings highlight the potential of structured anomaly detection in AGI governance. To oversee the implementation of the framework, the International AGI Alliance (IAA) is proposed as a global collaborative body to ensure alignment, enforce shared safety standards, and foster trust among stakeholders. We believe the proposed framework balances security and innovation, offering a scalable, systematic solution to one of the challenges of the future AI revolution.