Online Cybersecurity and Artificial Intelligence Degree

In this course, students focus on developing writing skills through practice and revision of a variety of different types of essays. Students are also given instruction in library and online research and methods of documentation.

In this course, students focus on research and developing writing skills through writing the ''argument'' essay. Students are also given instruction in library and online research and methods of documentation.

An introductory course designed to develop critical thinking, problem solving, and quantitative reasoning skills through the use of standard mathematical operations and techniques as well as analysis of visual data. Students will be expected to identify, analyze, and solve problems in a variety of applied contexts for transfer toward academic, personal, and professional success.

This is a course for students who are new to university-level learning. Topics will include the theory and application of setting goals, managing time and money, developing self-awareness, and adhering to the rigorous standards of academic and professional writing. Additionally, students will be prepared to work autonomously and collaboratively in academic and professional settings.

This course provides an introduction to information technologies and information technology careers. The history of information technology is reviewed with a focus on key innovations and innovators. The significance of digital data to information technologies and organizations is reviewed. Students are provided with opportunities to evaluate social and ethical implications of information technology.

This course provides an overview of information technology hardware in the categories of microcomputer, handheld/mobile, wearable computing devices, and core networking hardware components. An introduction to the Internet of Things (IoT) is provided. Emphasis is placed on capabilities, functionality, and basic troubleshooting.

In this introduction to operating systems, students are exposed to contemporary desktop and mobile operating systems. Topics may include operating system support, functions, network requirements, virtualization, and basic maintenance.

In this course, students are introduced to the programming concepts of control structures, arrays, and modular program design. Students will also develop, debug, and execute simple applications.

This course introduces information technology fundamentals as the foundation for cybersecurity and AI-enabled security practice. Students examine computer hardware, operating systems, virtualization, cloud services, storage, identity management, endpoint configuration, and system administration. Emphasis is placed on securing IT environments that support both traditional enterprise systems. Students explore how AI tools can assist with configuration review, vulnerability identification, anomaly detection, and basic security automation, while also examining risks such as data exposure, insecure integrations, and misuse of automated tools.

This course introduces artificial intelligence concepts and their application to cybersecurity. Students examine machine learning, natural language processing, generative AI, large language models, classification, pattern recognition, and automation as they relate to threat detection, phishing analysis, malware identification, vulnerability management, and security decision support. The course introduces LLM security, attacks, and defenses, including prompt injection, data poisoning, sensitive data leakage, insecure plugin or tool use, hallucination risk, model misuse, and defensive controls. Students also explore privacy, explainability, bias, ethics, and governance considerations for responsible AI use in cybersecurity.

This course examines how artificial intelligence technologies continue to transform cybersecurity threats, defenses, governance, and professional practice. Students explore AI-enabled attack methods, AI-assisted defense, generative AI, large language models, automated vulnerability discovery, social engineering, malware development risks, security analytics, and cyber risk management. The course emphasizes adversarial AI for language models, including prompt injection, jailbreaks, model manipulation, data poisoning, retrieval-augmented generation risks, and defensive strategies. Students evaluate both the benefits and limitations of AI technologies and consider ethical, legal, privacy, and organizational implications of AI adoption in cybersecurity.

This course introduces network engineering concepts with emphasis on secure, AI-enabled environments. Students examine network traffic, its components, and common vulnerabilities. The course explores how AI-assisted tools can be used to analyze network traffic, identify anomalies, recommend configurations, and support performance and security monitoring. Students also examine secure network design for AI systems, including API communication, model-serving environments, data pipelines, access controls, and protections for systems that interact with language models and other AI services.

This course focuses on defensive strategies, technologies, and AI-enabled countermeasures used to protect networks from contemporary cyber threats. Students examine intrusion detection and prevention, firewalls, endpoint and network monitoring, secure architecture, threat hunting, vulnerability mitigation, and incident escalation. The course emphasizes the use of AI-supported analytics, behavioral detection, automated triage, and network telemetry to identify malicious activity and recommend countermeasures. Students also examine adversarial techniques used to evade AI-enabled defenses and evaluate controls for protecting AI-supported network defense systems from manipulation, poisoning, false positives, and model misuse.

This course explores how AI technologies enhance cybersecurity operations, incident response, and digital forensics. Students learn about security workflows, threat detection, and forensic processes, with a focus on using AI for log analysis, automation, and decision support. The course also addresses operational risks, such as insecure AI workflows, data exposure, hallucinations, adversarial threats, and the importance of securing AI systems in security contexts.

This capstone course provides students the opportunity to integrate cybersecurity and artificial intelligence knowledge through an applied case study. Students will identify cybersecurity threats, vulnerabilities, risks, and opportunities; analyze relevant technical, organizational, ethical, and operational factors; and devise an AI-enabled solution to address the problem. The course emphasizes evidence-based decision-making, responsible AI use, secure design, risk mitigation, professional communication, and presentation of recommendations to a professional audience.

This course covers ethical hacking and penetration testing in AI-driven environments. Students can learn to use authorized security testing to identify vulnerabilities, assess risks, and suggest improvements for systems, networks, applications, and AI technologies. The course focuses on responsible use of AI tools in reconnaissance, vulnerability analysis, social engineering, exploitation, reporting, and remediation. Risks such as misuse, inaccurate results, data exposure, adversarial manipulation, and ethical concerns are also addressed.

This course examines contemporary cybersecurity incidents involving artificial intelligence technologies. Students analyze real-world cases involving AI-enabled attacks, AI-supported defenses, LLM security concerns, data exposure, adversarial AI, model misuse, and organizational response. Emphasis is placed on understanding how incidents occur, evaluating their cybersecurity and operational impact, identifying lessons learned, and considering responsible strategies for reducing future risk. The course is designed to help students work to develop the ability to interpret emerging AI cyber incidents and communicate informed recommendations for cybersecurity practice.

This course examines governance practices for managing cybersecurity risk and the responsible use of artificial intelligence in organizational settings. Students can explore how policies, standards, compliance requirements, risk management processes, and oversight structures guide the secure and ethical use of AI-enabled systems. Emphasis is placed on privacy, accountability, transparency, third-party risk, acceptable use, incident response, data governance, and controls for reducing cyber and AI-related risk. Students have the opportunity to evaluate how organizations can align cybersecurity governance and AI governance to support secure, responsible, and effective technology use.

This course examines how data science is used to design, evaluate, and secure AI applications in cybersecurity environments. Students analyze the role of security-relevant data in supporting AI models, including data used for threat detection, anomaly identification, vulnerability prioritization, fraud detection, malware analysis, and LLM security. Emphasis is placed on preparing trustworthy data, evaluating model outputs, identifying data-driven risks such as bias, poisoning, leakage, and drift, and using analytical evidence to improve the security and reliability of AI-enabled cybersecurity applications.