Assessment security has become increasingly focused on detection: more signals, more alerts, more monitoring, and more technology aimed at identifying AI-assisted misconduct. But detecting problems after they occur is only one part of an effective security strategy.

What if the better question is: how do we make misconduct less likely in the first place? This session introduces a deterrence-first model for assessment integrity. Brandon Smith will explore how programs can move beyond reactive detection toward a layered approach that reduces opportunity, identifies meaningful risk, and responds proportionately when concerns arise.

Participants will examine four connected layers: deter, detect, review, and respond. The session will show why relying too heavily on a single signal creates risk, how human judgment adds critical context, and how programs can adapt as AI-assisted misconduct and other tactics evolve.

The goal is not more monitoring. It is better security: proportionate, explainable, and designed to protect both assessment credibility and the test-taker experience. Attendees will leave with a practical framework for evaluating whether their current integrity strategy is simply reacting to incidents, or actively helping prevent them.