Artificial Intelligence & ChatGPT for Cyber Security
 
Master Cybersecurity and Ethical Hacking with Artificial Intelligence
Implement Advanced Security Strategies, Detect Threats, and Navigate the AI Era with Confidence
 
Course Description:
This comprehensive course is designed for individuals who want to master the intersection of Artificial Intelligence and Cybersecurity. Whether you are an aspiring AI professional, a student seeking practical cybersecurity skills, or an experienced developer looking to integrate AI into your security toolkit, this program delivers a hands-on, project-based approach to learning.
Participants will gain a deep understanding of how AI tools, especially ChatGPT and Python-based frameworks, are being used in modern cybersecurity operations. The course begins with foundational knowledge and quickly moves into advanced applications such as voice cloning, deepfake generation, phishing detection, network monitoring, and malware detection—each reinforced through real-world implementation exercises.
You will explore how to use AI to enhance traditional cybersecurity tools such as SIEM systems, firewalls, email filtering, and identity and access management, while also addressing critical security concerns such as data poisoning, bias, and ethical vulnerabilities.
 
What You Will Learn:
How to use ChatGPT for cybersecurity applications
The fundamentals and techniques of prompt engineering
Advanced features of ChatGPT including data analysis, plugin integration, and filter bypassing
Social engineering threats enabled by AI, including voice cloning and deepfakes
How to build cybersecurity tools using Python and AI, including:
Email filtering systems
Phishing detection tools
Malware detection models
Network monitoring systems
Applications of AI in modern cybersecurity infrastructure (e.g., SIEM, firewalls, IAM)
The security risks associated with AI: data poisoning, model bias, and vulnerabilities
Ethical considerations of AI in cybersecurity
Introductory foundations in both cybersecurity and artificial intelligence
 
Course Modules:
 
1. ChatGPT for Cybersecurity and Ethical Hacking
Covers the use of ChatGPT for cybersecurity tasks, including error mitigation, prompt engineering, advanced data capabilities, and safe implementation practices. Includes hands-on work with Few-shot prompting, Chain-of-thought prompting, and custom output formatting.
 
2. Social Engineering and AI
Explores AI-enhanced social engineering techniques. Topics include voice cloning using ElevenLabs and Resemble AI, deepfake creation with D-ID, and crafting personalized phishing messages using ChatGPT. The module emphasizes detection and prevention strategies.
 
3. AI Applications in Cybersecurity Infrastructure
Examines how AI is currently implemented in cybersecurity systems, such as SIEM, firewalls, intrusion detection systems, email filtering, and identity and access management.
 
4. Building an AI-Powered Email Filtering System
Guides learners through the development of a functional email filter using Python and AI algorithms. Includes training models, analyzing datasets, and comparing results to established AI systems such as ChatGPT.
 
5. Building a Phishing Detection System with AI
Covers the fundamentals of phishing and how to construct a detection system using decision tree algorithms. Learners will analyze datasets, train models, and evaluate results with key performance metrics.
 
6. AI in Network Security
Introduces traditional network security concepts and shows how to implement network monitoring systems using logistic regression and Python. The focus is on data preprocessing, model training, and hyperparameter optimization.
 
7. Malware Detection with AI
Covers malware types and prevention techniques. Learners will build a malware detection model by training and evaluating several machine learning algorithms, selecting the most effective one.
 
8. AI Security Risks and Ethics
Addresses major AI security concerns, including data poisoning attacks, biased training data, model vulnerabilities, and ethical implications of AI use in cybersecurity.
 
9. Introduction to Cybersecurity (Appendix A)
Provides a foundational understanding of cybersecurity history, common attack types, defense mechanisms, industry certifications, and best practices.
 
10. Introduction to Artificial Intelligence (Appendix B)
Offers a concise overview of AI, including its historical development, categories (narrow, general, superintelligence), distinctions between AI, machine learning, and deep learning, and the broader ethical and societal implications.
 
Who Should Enroll:
Individuals new to cybersecurity or artificial intelligence
Students seeking practical applications of AI in security
Developers and engineers interested in building Python-based security tools
Security professionals exploring AI-powered threat detection
Anyone interested in understanding and mitigating the risks of AI in cybersecurity contexts
 
Prerequisites:
No prior experience in cybersecurity or AI is required
A computer with internet access (Windows, Linux, or macOS)
Basic familiarity with Python is recommended but not required
 
This program is structured to provide both foundational knowledge and advanced technical skills. By the end of the course, learners will be equipped to apply artificial intelligence effectively in cybersecurity settings, build practical tools, and understand the ethical implications of AI integration in threat detection and response.