Knowledge: Innovating in Homeland Security by Learning in Near Real-Time for High-Threat Events, was published.

This thesis examines effective organizational learning frameworks that can be applied to the homeland security enterprise to accelerate knowledge acquisition from major events in near real-time. The results demonstrate that speed is not inhibitory to the learning process. Recommendations highlight the need for adaptive change in how the homeland security environment evolves through the creation of an entity responsible for organizational learning. Such an approach would also leverage local learning officers to achieve bi-directionality in a novel knowledge acquisition process. A new framework for learning must also include a process for near real-time data collection and sensemaking, which would require both public-sector incubators as well as advocacy networks within a new systematic learning process. This approach to organizational learning is required so as not to repeat failures and to enable “fast-learning” as threats and threat actors evolve.