Artificial Intelligence & ChatGPT for Cyber Security 2026 (Udemy.com)

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!

Our approach is hands-on and practical, designed to engage you in the dynamic fusion of Artificial Intelligence and Cyber Security. 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:

1. ChatGPT For Cyber Security/Ethical Hacking - 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.

• Mistakes and Inaccuracies in ChatGPT

• Introduction to prompt engineering

• Few-shot prompting

• Chain of thought prompting

• Building Custom Instructions

• Summarising Data

• Advanced ChatGPT functionality (Data Analysis, Dalle, Plugins)

• Alternatives to ChatGPT (Bard, Claude, Bing Chat)

• How Companies leak their data to ChatGPT

2.  New Age Of Social Engineering - 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.

• What is social engineering ?

• Voice Cloning with ElevenLabs

• AI Voice Generating  with Resemble

• Creating deepfakes with D-ID

• Using ChatGPT to write Emails in my style

• How to recognise these type of scams

3.  Where Is AI Used In Cyber Security Today - 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.

• AI Based SIEM Systems

• Firewalls With AI

• Email Filtering With AI

• AI In IAM

• IDS/IPS with AI

4. Building an Email Filtering System With AI - 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.

• Introduction To Email Security and Filtering

• What are Spam filters and how do they work ?

• Dataset analysis

• Training and testing our AI system

• Implementing Spam detection using ChatGPT API

• Comparing our system vs ChatGPT system

5. Building a Phishing Detection System With AI - 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.

• Introduction To Phishing

• How to Recognise and Prevent Phishing Attacks

• Dataset Analysis

• Splitting The Data

• Introduction To Decision Trees

• Training Random Forest Algorithm

• Precision and Recall

6. AI In Network Security - 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.

• Introduction To Network Security

• Dataset Analysis

• Data Pre-Processing

• Data Preparation

• Logistic Regression

• Training Logistic Regression For Network Monitoring

• Hyperparameter Optimisation

7. AI For Malware Detection - 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.

• What Is Malware & Different Types of Malware

• Traditional Systems for Malware Detection

• Loading Malware Dataset

• Malware Dataset Analysis and Pre-Processing

• Training Machine Learning Algorithms

• Saving The Best Malware Detection Model

8.  AI Security Risks - 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.

• Data Poisoning

• Data Bias

• Model Vulnerabilities

• Ethical Concerns