INSA 451
Advanced AI Applications in Intelligence Operations
This course explores the transformative role of artificial intelligence (AI), focusing on generative
AI, in modern intelligence operations. Students will examine how AI technologies synthesize data,
automate reporting, and assist in predictive analysis to enhance intelligence assessments and
operations. Topics include advanced applications of machine learning, natural language processing
(NLP), and deep learning, alongside their use in creating realistic simulations for training,
translating languages, decrypting communications, and generating unbiased analytical products.
The course emphasizes AI's technological and ethical dimensions, preparing students to apply these
tools in national security and intelligence agencies. Through hands-on projects and real-world case
studies, students will learn to produce actionable intelligence insights, address ethical and
operational challenges, and understand the strategic implications of AI in shaping global
intelligence landscapes. This course is designed to build advanced expertise in AI¿s application
across intelligence workflows while fostering critical thinking about its societal and geopolitical
impacts.
Note: Students are responsible to both be aware of and abide by prerequisites for ICS/INSA/CYBR courses for which they enroll, and will be administratively dropped from a course if they have not met prerequisites.
First day attendance is mandatory.
4
Undergraduate credits
Effective
January 10, 2025
to present
General
- Evaluate the transformative role of generative and traditional AI in intelligence
workflows, including data synthesis, reporting automation, and predictive
analysis.
- Design and Implement AI-driven solutions to address complex intelligence
challenges.
- Analyze and Synthesize AI methodologies to generate unbiased and actionable
intelligence products.
- Assess AI's ethical, legal, and societal implications in intelligence operations,
particularly in addressing bias, privacy, and security concerns.
- Analyze real-world case studies to extract lessons learned and best practices in
AI applications within intelligence operations.
- Apply AI tools to analyze real-world intelligence challenges while effectively
communicating findings through reporting and presentations.