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The integration of artificial intelligence (AI) into healthcare systems has revolutionized clinical processes. Yet, the deployment of AI for clinical trial eligibility pre-screening remains fraught with governance challenges, particularly in ensuring risk-bounded recruitment. This conceptual manuscript proposes a governance-first automation framework designed to mitigate ethical, operational, and regulatory risks in AI-assisted patient selection for clinical trials. By prioritizing governance mechanisms over algorithmic optimization, the framework establishes a structured architecture that incorporates interoperability standards, real-time monitoring, and decision support pipelines to facilitate responsible automation. Drawing on theoretical insights from clinical AI system architectures and healthcare analytics infrastructures, we outline a layered model that balances automation efficiency with risk containment, emphasizing feedback loops for continuous governance oversight. Key components include risk propagation modeling, interoperability protocols for electronic health records (EHR) integration, and governance load assessments to prevent overburdening clinical workflows. This approach addresses the need for equitable and safe AI deployment in high-stakes environments like clinical trials, where eligibility pre-screening must align with ethical standards and regulatory compliance. Through interpretive formulas capturing risk dynamics and decision confidence, the framework provides a blueprint for healthcare institutions to implement AI-driven recruitment without compromising patient safety or trial integrity. Ultimately, this governance-centric paradigm shifts the focus from mere technological integration to responsible orchestration, fostering trust in AI-enhanced clinical trial ecosystems.