AI+ Security™

Protect and Secure: Leverage Intelligent AI Solutions

Our comprehensive course, AI+ Cybersecurity offers professionals a thorough exploration of the integration of AI and Cybersecurity. Beginning with fundamental Python programming tailored for AI and Cybersecurity applications, participants delve into essential AI principles before applying machine learning techniques to detect and mitigate cyber threats, including email threats, malware, and network anomalies. Advanced topics such as user authentication using AI algorithms and the application of Generative Adversarial Networks (GANs) for Cybersecurity purposes are also covered, ensuring participants are equipped with cutting-edge knowledge. Practical application is emphasized throughout, culminating in a Capstone Project where attendees synthesize their skills to address real-world cybersecurity challenges, leaving them adept in leveraging AI to safeguard digital assets effectively.

Buy Exam Bundle Download Blueprint Find a Training Partner


  • Interest in learning about machine learning, deep learning, and natural language processing.
  • Basic knowledge computer science, no technical knowledge required
  • Curiosity and openness to learning about new concepts and technologies
  • Willingness to explore ethical considerations and legal frameworks surrounding the use of AI and data privacy

Exam Details



50 MCQs 90 Minutes

Passing Score70%

Certification Modules

  1. 1.1 Understanding the Cyber Security Artificial Intelligence (CSAI)
  2. 1.2 An Introduction to AI and its Applications in Cybersecurity
  3. 1.3 Overview of Cybersecurity Fundamentals
  4. 1.4 Identifying and Mitigating Risks in Real-Life
  5. 1.5 Building a Resilient and Adaptive Security Infrastructure
  6. 1.6 Enhancing Digital Defenses using CSAI
  1. 2.1 Python Programming Language and its Relevance in Cybersecurity
  2. 2.2 Python Programming Language and Cybersecurity Applications
  3. 2.3 AI Scripting for Automation in Cybersecurity Tasks
  4. 2.4 Data Analysis and Manipulation Using Python
  5. 2.5 Developing Security Tools with Python
  1. 3.1 Understanding the Application of Machine Learning in Cybersecurity
  2. 3.2 Anomaly Detection to Behaviour Analysis
  3. 3.3 Dynamic and Proactive Defense using Machine Learning
  4. 3.4 Safeguarding Sensitive Data and Systems Against Diverse Cyber Threats
  1. 4.1 Utilizing Machine Learning for Email Threat Detection
  2. 4.2 Analyzing Patterns and Flagging Malicious Content
  3. 4.3 Enhancing Phishing Detection with AI
  4. 4.4 Autonomous Identification and Thwarting of Email Threats
  5. 4.5 Tools and Technology for Implementing AI in Email Security
  1. 5.1 Introduction to AI Algorithm for Malware Threat Detection
  2. 5.2 Employing Advanced Algorithms and AI in Malware Threat Detection
  3. 5.3 Identifying, Analyzing, and Mitigating Malicious Software
  4. 5.4 Safeguarding Systems, Networks, and Data in Real-time
  5. 5.5 Bolstering Cybersecurity Measures Against Malware Threats
  6. 5.6 Tools and Technology: Python, Malware Analysis Tools
  1. 6.1 Utilizing Machine Learning to Identify Unusual Patterns in Network Traffic
  2. 6.2 Enhancing Cybersecurity and Fortifying Network Defenses with AI Techniques
  3. 6.3 Implementing Network Anomaly Detection Techniques
  1. 7.1 Introduction
  2. 7.2 Enhancing User Authentication with AI Techniques
  3. 7.3 Introducing Biometric Recognition, Anomaly Detection, and Behavioural Analysis
  4. 7.4 Providing a Robust Defence Against Unauthorized Access
  5. 7.5 Ensuring a Seamless Yet Secure User Experience
  6. 7.6 Tools and Technology: AI-based Authentication Platforms
  7. 7.7 Conclusion
  1. 8.1 Introduction to Generative Adversarial Networks (GANs) in Cybersecurity
  2. 8.2 Creating Realistic Mock Threats to Fortify Systems
  3. 8.3 Detecting Vulnerabilities and Refining Security Measures Using GANs
  4. 8.4 Tools and Technology: Python and GAN Frameworks
  1. 9.1 Enhancing Efficiency in Identifying Vulnerabilities Using AI
  2. 9.2 Automating Threat Detection and Adapting to Evolving Attack Patterns
  3. 9.3 Strengthening Organizations Against Cyber Threats Using AI-driven Penetration Testing
  4. 9.4 Tools and Technology: Penetration Testing Tools, AI-based Vulnerability Scanners
  1. 10.1 Introduction
  2. 10.2 Use Cases: AI in Cybersecurity
  3. 10.3 Outcome Presentation

What Will You Learn?

Identity Icon

AI-Driven Threat Detection

Learners will gain expertise in using AI algorithms for detecting various cybersecurity threats, including email threats, malware, and network anomalies, enhancing security monitoring capabilities.

Identity Icon

Application of Machine Learning in Cybersecurity

Students who will go through this course will have the ability to apply machine learning techniques to predict, detect, and respond to cyber threats effectively, using data-driven insights.

Identity Icon

Enhanced User Authentication Methods

Learners will develop skills in implementing advanced AI-based user authentication systems, improving security protocols to verify user identities more accurately and resist fraudulent attempts.

Identity Icon

AI-Enhanced Penetration Testing

Students will learn how to use AI tools to automate and enhance penetration testing processes, identifying vulnerabilities more efficiently and comprehensively than traditional methods.

Industry Opportunities after Course Completion


No prior programming experience is necessary. The course begins with fundamental Python programming tailored for AI and Cybersecurity applications, making it suitable for beginners.

This course equips professionals with cutting-edge knowledge and practical skills in integrating AI with Cybersecurity, enhancing their ability to protect digital assets and address modern cyber threats effectively.

The Capstone Project focuses on synthesizing the skills learned throughout the course to address real-world cybersecurity challenges, enabling participants to leverage AI effectively to safeguard digital assets.

The course covers detection and mitigation of various cyber threats including email threats, malware, and network anomalies using machine learning techniques.

The program is structured into ten modules, each focusing on different aspects of AI and cybersecurity, from fundamental concepts to advanced applications, culminating in a Capstone Project.