Before AI Responds: Enforcing Institutional Policy with Deterministic Pre-Execution Control

Higher education institutions are rapidly adopting AI, yet many governance models still rely on controls that act only after a system has generated a response. That gap exposes institutions to privacy, compliance, and academic integrity risks, especially when policy enforcement is inconsistent across tools and use cases. This session presents a policy-first approach to governing AI interactions before execution, enabling institutions to apply consistent, enforceable controls at the point of request. Attendees will explore a reusable architectural blueprint and practical implementation patterns for translating institutional policy into clear decision paths that support both innovation and oversight. Drawing on pilot experience, the session highlights lessons learned, trade-offs, and design considerations for building scalable, policy-aligned AI governance. Participants will leave with actionable approaches they can adapt within their own institutional environments.