Artificial Intelligence & ChatGPT for Cyber Security 2026

<div>Whether you are an aspiring AI enthusiast eager to delve into the realm of Cyber Security, a student aiming to fortify your understanding of securing digital landscapes, or a seasoned programmer who is looking to implement Python and Artificial Intelligence into Cyber Security Tools, this course is tailored for you!</div><div><br></div><div><span style="font-size: 1rem;">Our approach is hands-on and practical, designed to engage you in the dynamic fusion of <b>Artificial Intelligence and Cyber Security</b>. We believe in learning by doing, guiding you through real-world techniques and methods utilised by experts in the field. At the start of this course, we will dive right in by showing you how to use ChatGPT for Cyber Security. You will learn practical ways to make the most of ChatGPT, from understand its basics to using it for data analysis and other advanced features. After that we will dive into topics like:</span></div><div><br></div><div><span style="font-size: 1rem;">1. <b>ChatGPT For Cyber Security/Ethical Hacking</b> - In this section, we delve into the dynamic world of ChatGPT for Cyber Security and Ethical Hacking, exploring key topics that range from addressing mistakes and inaccuracies in ChatGPT to understanding the intricacies of prompt engineering, including context prompting and output formatting. Through hands-on exercises, participants will tackle Few-Shot prompting and Chain of thought prompting, building a solid foundation in applying ChatGPT effectively. Additionally we'll navigate through advanced functionalities like Data Analysis, DALL E integration, and plugin utilisation, providing practical insights into preventing data leakage and exploring alternatives to ChatGPT.</span></div><div><br></div><div><ul><li><span style="font-size: 1rem;">Mistakes and Inaccuracies in ChatGPT</span></li><li><span style="font-size: 1rem;">Introduction to prompt engineering</span></li><li><span style="font-size: 1rem;">Few-shot prompting</span></li><li><span style="font-size: 1rem;">Chain of thought prompting</span></li><li><span style="font-size: 1rem;">Building Custom Instructions</span></li><li><span style="font-size: 1rem;">Summarising Data</span></li><li><span style="font-size: 1rem;">Advanced ChatGPT functionality (Data Analysis, Dalle, Plugins)</span></li><li><span style="font-size: 1rem;">Alternatives to ChatGPT (Bard, Claude, Bing Chat)</span></li><li><span style="font-size: 1rem;">How Companies leak their data to ChatGPT</span></li></ul></div><div><br></div><div><span style="font-size: 1rem;">2.&nbsp; <b>New Age Of Social Engineering</b> - In this section we unravel the concept of social engineering, delving into its nuances and equipping participants with strategies to prevent potential threats. The module further explores Implementing Artificial Intelligence to explore new social engineering techniques which include voice cloning and creation of deepfakes.</span></div><div><br></div><div><ul><li>What is social engineering ?</li><li><span style="font-size: 1rem;">Voice Cloning with ElevenLabs</span></li><li><span style="font-size: 1rem;">AI Voice Generating&nbsp; with Resemble</span></li><li><span style="font-size: 1rem;">Creating deepfakes with D-ID</span></li><li><span style="font-size: 1rem;">Using ChatGPT to write Emails in my style</span></li><li><span style="font-size: 1rem;">How to recognise these type of scams</span></li></ul></div><div><br></div><div>3.&nbsp; <b>Where Is AI Used In Cyber Security Today</b> - In this section we explore the forefront of cybersecurity advancements, delving into the integration of AI across critical domains. Students will gain insights into how traditional Cybersecurity tools like Firewalls, SIEM systems, IDS/IPS, Email Filtering and Identity and Access Management work when Artificial Intelligence is applied to them.</div><div><br></div><div><ul><li><span style="font-size: 1rem;">AI Based SIEM Systems</span></li><li><span style="font-size: 1rem;">Firewalls With AI</span></li><li><span style="font-size: 1rem;">Email Filtering With AI</span></li><li><span style="font-size: 1rem;">AI In IAM</span></li><li><span style="font-size: 1rem;">IDS/IPS with AI</span></li></ul></div><div><br></div><div><br></div><div><span style="font-size: 1rem;">4. <b>Building an Email Filtering System With AI</b> - In this section students encounter a hands-on journey, utilising Python programming to implement Artificial Intelligence algorithms for crafting effective email filtering system. This module not only introduces the fundamentals of email filtering and security but also provides a comprehensive understanding of spam filters, guiding learners through dataset analysis, algorithm implementation and practical comparisons with established systems like ChatGPT.</span></div><div><br></div><div><ul><li><span style="font-size: 1rem;">Introduction To Email Security and Filtering</span></li><li><span style="font-size: 1rem;">What are Spam filters and how do they work ?</span></li><li><span style="font-size: 1rem;">Dataset analysis</span></li><li><span style="font-size: 1rem;">Training and testing our AI system</span></li><li><span style="font-size: 1rem;">Implementing Spam detection using ChatGPT API</span></li><li><span style="font-size: 1rem;">Comparing our system vs ChatGPT system</span></li></ul></div><div><br></div><div><span style="font-size: 1rem;">5. <b>Building a Phishing Detection System With AI</b> - In this section, students will gain essential knowledge about phishing and acquiring skills to recognise phishing attacks. Through practical implementation, this module guides learners in utilising decision trees with Python programming, enabling them to construct a robust phishing detection system.</span></div><div><br></div><div><ul><li><span style="font-size: 1rem;">Introduction To Phishing</span></li><li><span style="font-size: 1rem;">How to Recognise and Prevent Phishing Attacks</span></li><li><span style="font-size: 1rem;">Dataset Analysis</span></li><li><span style="font-size: 1rem;">Splitting The Data</span></li><li><span style="font-size: 1rem;">Introduction To Decision Trees</span></li><li><span style="font-size: 1rem;">Training Random Forest Algorithm</span></li><li><span style="font-size: 1rem;">Precision and Recall</span></li></ul></div><div><br></div><div><span style="font-size: 1rem;">6. <b>AI In Network Security</b> - In this section, students get into the foundations of network security, exploring traditional measures alongside practical implementations using Python. With the help of Logistic Regression, learners gain hands-on experience in building a system for network monitoring.</span></div><div><br></div><div><ul><li><span style="font-size: 1rem;">Introduction To Network Security</span></li><li><span style="font-size: 1rem;">Dataset Analysis</span></li><li><span style="font-size: 1rem;">Data Pre-Processing</span></li><li><span style="font-size: 1rem;">Data Preparation</span></li><li><span style="font-size: 1rem;">Logistic Regression</span></li><li><span style="font-size: 1rem;">Training Logistic Regression For Network Monitoring</span></li><li><span style="font-size: 1rem;">Hyperparameter Optimisation</span></li></ul></div><div><br></div><div><span style="font-size: 1rem;">7. <b>AI For Malware Detection</b> - In this section students get on a comprehensive exploration of malware types and prevention strategies before delving into the creation of a sophisticated malware detection system. This module guides learners through the training of multiple algorithms learned throughout the course, empowering them to evaluate and implement the most accurate solution for malware detection system.</span></div><div><br></div><div><ul><li><span style="font-size: 1rem;">What Is Malware & Different Types of Malware</span></li><li><span style="font-size: 1rem;">Traditional Systems for Malware Detection</span></li><li><span style="font-size: 1rem;">Loading Malware Dataset</span></li><li><span style="font-size: 1rem;">Malware Dataset Analysis and Pre-Processing</span></li><li><span style="font-size: 1rem;">Training Machine Learning Algorithms</span></li><li>Saving The Best Malware Detection Model</li></ul></div><div><br></div><div><br></div><div>8.&nbsp; <b>AI Security Risks</b> - In this section we explore critical Artificial Intelligence security risks such as data poisoning, data bias, model vulnerabilities and ethical concerns. This module dives into deep understanding of potential risks and ethical considerations of Artificial Intelligence Implementation.</div><div><br></div><div><ul><li><span style="font-size: 1rem;">Data Poisoning</span></li><li><span style="font-size: 1rem;">Data Bias</span></li><li><span style="font-size: 1rem;">Model Vulnerabilities</span></li><li><span style="font-size: 1rem;">Ethical Concerns</span></li></ul></div><div><br></div><div><span style="font-size: 1rem;">9. <b>Appendix A: Introduction To Cyber Security</b> - This is our first Appendix section which is a cybersecurity foundational journey, tracing the evolution of cybersecurity and gaining insights into essential tools, techniques, certificates and best practices. This module serves as a compass, guiding learners through the core principles of cybersecurity.</span></div><div><br></div><div><ul><li><span style="font-size: 1rem;">Evolution Of Cyber Security</span></li><li><span style="font-size: 1rem;">Categories of Cyber Attacks</span></li><li><span style="font-size: 1rem;">Security Policies and Procedures</span></li><li><span style="font-size: 1rem;">Cyber Security Tools and Technologies</span></li><li><span style="font-size: 1rem;">Understanding Cyber Security Certifications</span></li><li><span style="font-size: 1rem;">Cyber Security Best Practices</span></li></ul></div><div><br></div><div><span style="font-size: 1rem;">10. <b>Appendix B: Introduction to Artificial Intelligence</b> - This is our second Appendix section which is Artificial Intelligence fundamentals, covering brief history, diverse categories such as Narrow, General and Super intelligence and the distinctions between AI, machine learning and deep learning.</span></div><div><br></div><div><ul><li><span style="font-size: 1rem;">Brief History of AI</span></li><li><span style="font-size: 1rem;">Types of AI: Narrow, General and Superintelligence</span></li><li><span style="font-size: 1rem;">AI vs ML vs Deep Learning</span></li><li><span style="font-size: 1rem;">Fields influenced by AI</span></li><li><span style="font-size: 1rem;">Machine Learning Algorithms</span></li><li><span style="font-size: 1rem;">AI Ethics and Governance</span></li></ul></div><div><br></div><div>We assure you that this bootcamp on Artificial Intelligence in Cyber Security is designed to be the most comprehensive online course for mastering integration of AI in cybersecurity practices!</div>

What you'll learn:

• Learn ChatGPT for Cyber Security
• Learn Prompt Engineering
• Use Advanced ChatGPT functionality
• Implement Bypassing ChatGPT filters
• Learn Social Engineering with Artificial Intelligence
• Create a Voice Clone with AI
• Create Deepfake Videos For Social Engineering with AI
• Learn AI Based SIEM
• Learn AI Based Firewalls
• Learn Email Filtering with AI
• Learn AI In Identity and Access Management
• Build an Email Filtering System with AI and Python
• Build a Phishing detection system with AI and Python
• Implement Artificial Intelligence in Network Security
• Using Logistic Regression Algorithm for Network Monitoring
• Create Malware Detection system with AI and Python
• Learn Decision Trees Algorithm
• Learn K-Nearest Neighbors Algorithm KNN
• Learn Data Poisoning Attack
• Cover Data Bias Vulnerability
• Learn Model Vulnerabilities
• Cover Ethical Concerns of Artificial Intelligence and ChatGPT
• Learn Basics of Cyber Security
• Learn Basics of Artificial Intelligence
• Learn Basics of Python Programming