Security at major locations of economic or political importance is a key concern around the world, particularly given
the increasing threat of terrorism. Limited security resources prevent full security coverage at all times, which allows
adversaries to observe and exploit patterns in patrolling or monitoring, e.g. they can plan an attack that avoids existing
patrols. An important method of countering the surveillance capabilities of an adversary is to use randomized security
policies that are more difficult to predict and exploit. We describe two deployed applications that assist security forces
in randomizing their operations based on fast algorithms for solving large instances of Bayesian Stackelberg games. The
first is the ARMOR system (Assistant for Randomized Monitoring over Routes), which has been successfully deployed
since August 2007 at the Los Angeles International Airport (LAX). This system is used by airport police to randomize
the placement of checkpoints on roads entering the airport, and the routes of canine unit patrols in the airport terminals.
The IRIS system (Intelligent Randomization in Scheduling) is designed to randomize flight schedules for the Federal Air
Marshals Service (FAMS). IRIS has been deployed in a pilot program by FAMS since October 2009 to randomize schedules of air marshals on international flights. These assistants share several key features: (i) they are based on Stackelberg
game models to intelligently weight the randomized schedules, (ii) they use efficient mixed-integer programming formulations of the game models to enable fast solutions for large games, and (iii) they allow for interactive manipulation of
the domain constraints and parameters by the users. This paper examines the design choices, information, and evaluation
that went into building these effective applications.