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Overview Prerequisites Self Study Materials Included Tools What You’ll Learn Course Modules FAQs

What’s Included?

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Prerequisites

    • Completion of AI+ Security Level 1™, but not mandatory
    • Basic Python Skills: Familiarity with Python basics, including variables, loops, and functions.
    • Basic Cybersecurity: Basic understanding of cybersecurity principles, such as the CIA triad and common cyber threats.
    • Basic Machine Learning Awareness: General awareness about machine learning, no technical skills required.
    • Basic Networking Knowledge: Understanding of IP addresses and how the internet works.
    • Basic command line Skills: Comfort using the command line like Linux or Windows terminal for basic tasks
    • Interest in AI for Security: Willingness to explore how AI can be applied to detect and mitigate security threats.

     

    There are no mandatory prerequisites for certification. Certification is based solely on performance in the examination. However, candidates may choose to prepare through self-study or optional training offered by AI CERTs® Authorized Training Partners (ATPs).

Self Study Materials Included

Videos

Engaging visual content to enhance understanding and learning experience.

Podcasts

Insightful audio sessions featuring expert discussions and real-world cases.

Audiobooks

Listen and learn anytime with convenient audio-based knowledge sharing.

E-Books

Comprehensive digital guides offering in-depth knowledge and learning support.

Module Wise Quizzes

Interactive assessments to reinforce learning and test conceptual clarity.

Additional Resources

Supplementary references and list of tools to deepen knowledge and practical application.

Tools You’ll Master

CrowdStrike

CrowdStrike

Microsoft Cognitive Toolkit (CNTK)

Microsoft Cognitive Toolkit (CNTK)

Flair.ai

Flair.ai

What You’ll Learn

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.

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.

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.

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.

Course Modules

Module 1: Introduction to Artificial Intelligence (AI) and Cyber Security
  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
Module 2: Python Programming for AI and Cybersecurity Professionals
  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
Module 3: Application of Machine Learning in Cybersecurity
  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
Module 4: Detection of Email Threats with AI
  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
Module 5: AI Algorithm for Malware Threat Detection
  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
Module 6: Network Anomaly Detection using AI
  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
Module 7: User Authentication Security with AI
  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
Module 8: Generative Adversarial Network (GAN) for Cyber Security
  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
Module 9: Penetration Testing with Artificial Intelligence
  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
Module 10: Capstone Project
  1. 10.1 Introduction
  2. 10.2 Use Cases: AI in Cybersecurity
  3. 10.3 Outcome Presentation
Optional Module: AI Agents for Security Level 2
  1. 1. What Are AI Agents
  2. 2. Key Capabilities of AI Agents in Advanced Cybersecurity
  3. 3. Applications and Trends for AI Agents in Advanced Cybersecurity
  4. 4. How Does an AI Agent Work
  5. 5. Core Characteristics of AI Agents
  6. 6. Types of AI Agents

Frequently Asked Questions

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.

Visit the official website, complete the registration process, and access the course materials immediately after payment.

The course 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.