From the EU AI Act to Colorado's SB24-205, well-intentioned laws consistently create the same problem: requirements like "algorithmic fairness" or "human oversight" that sound clear in legislation but become nearly impossible to operationalize. The result is compliance uncertainty, unpredictable business risk, and security teams building programs they can't validate. The core issue: policymakers and practitioners speak different languages. This session examines how cross-sector collaboration is reshaping AI policy development. Learn what separates implementable requirements from unenforceable ambiguity, how jurisdictions are experimenting with collaborative governance models, and why technical perspectives from security and business professionals matter in these conversations. Because better AI policy starts with bridging the gap between those who write the rules and those who implement them.