Artificial intelligence is increasingly used to evaluate governance frameworks, cybersecurity readiness, and emerging technology policies. However, organizations increasingly rely on AI-generated assessments without visibility into how different language models interpret identical governance artifacts. This paper presents GUARDIAN (Governance Using AI, Risk Detection, Intelligence Analytics, and Norms), a consensus-based, multi-LLM orchestration platform that evaluates AI-generated governance assessments. Integrating multiple Western-trained and Arabic-first language models, GUARDIAN provides consensus analysis, identifies interpretive divergence, detects bias and prompt-poisoning, and preserves traceable evidence supporting AI-assisted decision making. Implemented using AWS, FastAPI, PostgreSQL, and Streamlit, the platform supports standalone deployment or API integration with existing government and enterprise systems. Operational testing demonstrated systematic interpretive differences between Western-trained and Arabic-first models, illustrating the value of transparent evaluation of AI-generated governance assessments across diverse cultural and regulatory environments.