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Summer and Fall 2025 Registration window opens March 17.
Metropolitan State University (Home)

CYBR 325 Fundamentals of AI and ML in Cybersecurity

This course provides an in-depth exploration of fundamental concepts in Artificial Intelligence (AI) and Machine Learning (ML), with a focus on their applications in cybersecurity. Students will analyze AI principles, classical algorithms, and modern ML techniques, evaluating their role in enhancing security protocols and predicting cyber threats. The course emphasizes ethical considerations, governance frameworks, and the responsible use of AI technologies. Through case studies and hands-on applications, students will apply AI and ML tools to solve complex cybersecurity challenges, developing critical skills for securing digital environments.

Prerequisites

Special information

Note: Students are responsible to both be aware of and abide by prerequisites for ICS/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

Learning outcomes

General

  • Describe the principles, terminology, and components of Artificial Intelligence, examining the evolution of various AI technologies, tools, and their applications in modern industries.
  • Evaluate classical AI algorithms, such as search and optimization techniques, and assess their appropriateness and effectiveness in solving specific computational problems, particularly in real-world scenarios.
  • Assess how AI technologies can transform cybersecurity practices by enhancing security protocols, predicting security risks, and analyzing real-world case studies of threats and vulnerabilities.
  • Evaluate the suitability and performance of various AI and Machine Learning tools for specific tasks, particularly within cybersecurity, and justify the selection of tools based on their effectiveness in mitigating risks.
  • Assess the ethical considerations and potential risks associated with AI, including bias, privacy concerns, safety, and security, with an emphasis on ethical decision-making throughout the AI lifecycle.
  • Examine key elements of AI governance by analyzing U.S. and international legal and regulatory frameworks, organizational policies, and oversight mechanisms to assess their impact on AI development and cybersecurity.
  • Differentiate advanced machine learning methods to solve complex cybersecurity problems, evaluating the effectiveness of different algorithms and approaches in addressing specific threats and security challenges.

Fall 2025

Section Title Instructor books eservices
50 Fundamentals of AI and ML in Cybersecurity Cooper, Molly Books for CYBR-325-50 Fall 2025 Course details for CYBR-325-50 Fall 2025