CIS 268 - AI for Cybersecurity

Description
AI for Cybersecurity explores the intersection of artificial intelligence (AI) and cybersecurity, providing a comprehensive overview of how AI is applied to enhance cybersecurity measures. Through hands-on projects, students will learn fundamental concepts in cybersecurity and AI, covering topics such as machine learning, deep learning, and network security. The course also addresses modern challenges, including AI-powered attacks and the application of AI techniques for defense and mitigation.

Credit Hours: 3
Contact Hours: 3
Prerequisites/Other Requirements: C or Higher in the following courses: MA 98 OR MA 107 OR ALEKS Score of 30 or Higher
English Prerequisite(s): None
Math Prerequisite(s): None
Course Corequisite(s): None
Academic Program Prerequisite: None
Consent to Enroll in Course: No Department Consent Required
Dual Enrollment Allowed?: No
Number of Times Course can be taken for credit: 1
Programs Where This Course is a Requirement:
Artificial Intelligence Certificate

General Education Requirement:
None
General Education Learner Outcomes (GELO):
NA
Course Learning Outcomes:

Develop a strong understanding of core cybersecurity principles and AI technologies.
Analyze how AI techniques such as machine learning, deep learning, and natural language processing (NLP) can be leveraged to strengthen cybersecurity efforts.
Identify and mitigate cybersecurity risks using AI-driven methodologies, including automated threat detection, behavior analysis, and network protection.
Explore emerging trends and future challenges in AI-enhanced cybersecurity.

Course Outline:
Unit 01 - Introduction to AI for Cybersecurity
Unit 02 - Fundamentals of Cybersecurity
Unit 03 - Cybersecurity Threat Landscape
Unit 04 - Applications of AI in Cybersecurity
Unit 05 - Machine Learning Application in Cybersecurity I
Unit 06 - Machine Learning Application in Cybersecurity II
Unit 07 - Generative Models in Cybersecurity
Unit 08 - Effective Threat Detection Techniques
Unit 09 - Malware Analysis
Unit 10 - Network Security
Unit 11 - Security Operations with AI
Unit 12 - Incident Response Strategies using AI
Unit 13 - Introduction to AI in Penetration Testing
Unit 14 - Applied Penetration Testing Techniques with AI
Unit 15 - Log Analysis for Security Insights

Approved for Online and Hybrid Delivery?:
No
Instructional Strategies:
Lecture: 30-60%

Lab work: 10-40%

Facilitated discussion: 0-20%

Group work: 0-10%
Mandatory Course Components:
None.
Equivalent Courses:
None

Accepted GRCC Advanced Placement (AP) Exam Credit: None
AP Min. Score: NA
Name of Industry Recognize Credentials: None

Course-Specific Placement Test:
Course Aligned with ARW/IRW Pairing: NA

Mandatory Department Assessment Measures:
None.

Course Type:
Program Requirement- Offering designed to meet the learning needs of students in a specific GRCC program.
Course Format:
Lecture - 1:1
Total Lecture Hours Per Week: 3
People Soft Course ID Number: 105337
Course CIP Code: 11.9999
Maximum Course Enrollment: 26
School: School STEM
Department: Computer Information Systems
Discipline: CIS
First Term Valid: Fall 2025 (8/1/2025)
1st Catalog Year: 2025-2026
Name of Course Author:
Andrew Rozema

Faculty Credential Requirements:
Master’s Degree (GRCC general requirement), Professionally qualified through work experience in field (Perkins Act or Other) (list below)
Faculty Credential Requirement Details:
The instructor must possess a Master’s Degree in Computer Science or Computer Engineering; at least five years of programming experience that includes object-oriented programing; and the ability to clearly explain the topics covered in the course.

Course Review & Revision Year: 2029-2030