: AI for Cybersecurity Professional Course

Master AI for Cybersecurity

An exclusive, hands-on training program designed to make you a leader in detecting, preventing, and responding to next-generation cyber threats.

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AI Cybersecurity

Meet Your Expert Facilitators

Olusola Agbondelegbe

Olusola Agbondelegbe

Cybersecurity Expert

Olusola Agbondelegbe - MSc., BSc., CISSP, CySA+, Sec+, ITIL, CIW, Project+, SSCP; is an accomplished cybersecurity professional with extensive expertise in Security Assessment and Authorization (A&A), risk management, and compliance with federal standards such as NIST, FISMA, and FedRAMP.

Agbolade Omowole

Agbolade Omowole

AI Scientist

Agbolade Omowole is an AI Researcher and Business Strategist with over 15 years of experience in artificial intelligence and technology-driven business solutions. His research on AI has been featured by University of Saskatchewan, Canada; Sonoma State University, California, and Portland Community College, Oregon.

Bidemi Adedokun

Bidemi Adedokun

AI Engineer and Data Scientist

Bidemi Adedokun is a passionate innovator and problem-solver dedicated to creating impactful solutions through technology. He has worked with top companies such as Wells Fargo, Hyundai, and CVS, as well as research centers including NMSU, Smartgrid, and Fred Hutch Cancer Research.

6-Week Course Syllabus

This module builds your cybersecurity foundation. You will explore core principles, major threats such as malware and phishing, and analyze real-world attacks to understand modern digital risks.

Topics Covered:
  • Introduction to Cybersecurity (CIA triad, Equifax case)
  • Shifts Driving Cybersecurity (Colonial Pipeline ransomware)
  • Common Cyberattacks (Mirai botnet, DoS/DDoS)
  • Social Engineering & Phishing (BEC attack, phishing emails)
  • Data Theft & Interception (Wi-Fi eavesdropping, MitM attacks)
  • Malware 101 (WannaCry ransomware, spyware, Trojans)
Learning Outcomes:

By the end of this module, you will be able to:

  • Explain the CIA triad and its importance in cybersecurity.
  • Identify common types of cyberattacks and their real-world examples.
  • Recognize phishing and social engineering attempts.
  • Understand how malware functions and spreads.
Assignment:

Write a one-page analysis of a recent cyberattack in the news, describing the threat type, how it worked, and its consequences.

In this module, you will explore sophisticated cyber threats such as zero-day exploits and SQL injection. You will also learn how to defend against these threats using advanced tools and strategies in a hands-on approach.

Topics Covered:
  • Malware Variants & Fake Attacks (fake antivirus pop-ups)
  • Poisoned Services & Malvertising (malvertising campaign diagrams)
  • Advanced Attacks (Stuxnet case)
  • Technical Attack Techniques (SQL injection, brute force, rootkits)
  • Defensive Strategies (MFA, firewalls, security awareness)
  • Wrap-Up & Exercises (quiz, phishing-spotting activity, Kahoot)
Learning Outcomes:

By the end of this module, you will be able to:

  • Differentiate between advanced threats such as zero-day and malvertising.
  • Explain how technical attacks like SQL injection and brute force work.
  • Apply defensive strategies including MFA and firewalls.
  • Critically analyze real-world advanced cyber incidents.
Assignment:

Perform a simulated SQL injection on a test website (in a controlled lab or sandbox) and document how it works and how it can be prevented.

This section introduces fundamental concepts of Machine Learning (ML) and Artificial Intelligence (AI). You will learn how to use ML frameworks to solve real-world problems.

Topics Covered:
  • Introduction to Machine Learning Foundations
  • Artificial Intelligence and Machine Learning
  • Machine Learning Pipeline
  • Machine Learning Tools and Services
  • Introduction to Jupyter Notebook
  • Introduction to Machine Learning Libraries
Learning Outcomes:

By the end of this module, you will be able to:

  • Define machine learning and AI in the context of problem-solving.
  • Describe the stages of the ML pipeline.
  • Set up a Jupyter notebook environment.
  • Use basic ML libraries such as Scikit-learn, TensorFlow, or PyTorch.
Assignment:

Using Jupyter Notebook, write a Python script to load a dataset (e.g., Iris dataset) and train a simple classification model. Submit your code and results.

This module covers essential skills for AI engineers, including prompt engineering, Python, and practical frameworks for applying AI to business problems.

Topics Covered:
  • Prompt Engineering for AI Engineers
  • Python for AI Engineers
  • Machine Learning Pipeline/Framework
  • Using AI to Solve Business Problems
  • Data Cleaning and Normalization with LLMs
  • Jupyter Notebook using Google Colab
Learning Outcomes:

By the end of this module, you will be able to:

  • Apply prompt engineering techniques for AI models.
  • Use Python for basic AI workflows.
  • Build and run ML pipelines using Google Colab.
  • Clean and normalize datasets for ML applications.
  • Demonstrate how AI can solve practical business challenges.
Assignment:

Use Google Colab to preprocess a raw dataset (handling missing values, normalization, etc.) and prepare it for ML training. Submit your notebook file.

This module explores how to apply AI in cybersecurity. You will learn about machine learning algorithms, their role in threat detection, and strategies for securing digital systems.

Topics Covered:
  • AI Fundamentals for Cybersecurity
  • Machine Learning Algorithms
  • Enhanced User Authentication
  • Threat Detection & Mitigation
  • Using AI to Identify and Secure Digital Systems
  • Applying AI to Detect and Filter Spam
Learning Outcomes:

By the end of this module, you will be able to:

  • Explain how AI can be applied to cybersecurity challenges.
  • Understand key ML algorithms for threat detection.
  • Build simple ML models for spam detection.
  • Describe how AI enhances authentication and intrusion prevention.
Assignment:

Build a basic spam classifier using Python and Scikit-learn. Train it on a small dataset of spam/ham emails and report accuracy.

This section will help you enhance your resume and position yourself as an emerging AI talent. You will also learn how to prepare for AI cybersecurity job interviews.

Topics Covered:
  • Resume-Building Workshop
  • Preparing for an AI Cybersecurity Job Interview
Learning Outcomes:

By the end of this module, you will be able to:

  • Create a professional resume tailored for AI and cybersecurity roles.
  • Identify the most common AI and cybersecurity interview questions.
  • Confidently demonstrate technical and soft skills in an interview setting.

An Unparalleled Learning Experience

Dual Certification

Earn a certificate from Johns Hopkins University (Introduction to AI for Cybersecurity) and AWS (Introduction to Machine Learning).

Executive Networking

Gain access to an exclusive network of senior IT and cybersecurity professionals.

Career Acceleration

One-on-one career counselling with top-tier HR/IT executives to guide your next move.

Interview Mastery

Intensive interview preparation and mentorship to help you land senior roles.

Applicable Knowledge

An in-depth curriculum focused entirely on real-world applications and skills you can deploy immediately.

Hands-On Labs

Engage in practical, hands-on coding labs that simulate real-world cybersecurity challenges.

Secure Your Spot. Become an AI Security Expert.

The next cohort begins on September 20. Don't miss the opportunity to lead the future of cybersecurity.

Course Fee: $3,000 $2,400

Register early to receive a 20% discount!

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