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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™ and 2™
    • Intermediate/Advanced Python Programming: Proficiency or expert in Python, including deep learning frameworks (TensorFlow, PyTorch).
    • Intermediate Machine Learning Knowledge: Proficiency in understanding of deep learning, adversarial AI, and model training.
    • Advanced Cybersecurity Knowledge: Proficiency in threat detection, incident response, and network/endpoint security.
    • AI in Security Engineering: Knowledge of AI’s role in identity and access management (IAM), IoT security, and physical security.
    • Cloud and Container Expertise: Understanding of cloud security, containerization, and blockchain technologies.
    • Linux/CLI Mastery: Advanced command-line skills and experience with security tools in Linux environments

     

    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

Splunk User Behavior Analytics (UBA)

Splunk User Behavior Analytics (UBA)

Microsoft Defender for Endpoint

Microsoft Defender for Endpoint

Microsoft Azure AD Conditional Access

Microsoft Azure AD Conditional Access

Adversarial Robustness Toolbox (ART)

Adversarial Robustness Toolbox (ART)

What You’ll Learn

Apply Deep Learning for Cyber Defense

Acquire expertise in using deep learning algorithms for advanced applications like malware analysis, phishing detection, and predictive threat modeling.

Integrate AI with Cloud and Container Security

Understand the use of AI for securing cloud-based platforms and containerized applications, focusing on scalability and automation in threat mitigation.

Enhance Identity and Access Management with AI

Learn to apply AI techniques to streamline identity verification, manage access control systems, and secure authentication processes.

Secure IoT Devices Using AI

Explore how AI can be used to address unique IoT security challenges, including detecting compromised devices and protecting communication protocols.

Course Modules

Module 1: Foundations of AI and Machine Learning for Security Engineering
  1. 1.1 Core AI and ML Concepts for Security 
  2. 1.2 AI Use Cases in Cybersecurity 
  3. 1.3 Engineering AI Pipelines for Security 
  4. 1.4 Challenges in Applying AI to Security 
Module 2: Machine Learning for Threat Detection and Response
  1. 2.1 Engineering Feature Extraction for Cybersecurity Datasets
  2. 2.2 Supervised Learning for Threat Classification
  3. 2.3 Unsupervised Learning for Anomaly Detection
  4. 2.4 Engineering Real-Time Threat Detection Systems
Module 3: Deep Learning for Security Applications
  1. 3.1 Convolutional Neural Networks (CNNs) for Threat Detection
  2. 3.2 Recurrent Neural Networks (RNNs) and LSTMs for Security
  3. 3.3 Autoencoders for Anomaly Detection
  4. 3.4 Adversarial Deep Learning in Security
Module 4: Adversarial AI in Security
  1. 4.1 Introduction to Adversarial AI Attacks
  2. 4.2 Defense Mechanisms Against Adversarial Attacks
  3. 4.3 Adversarial Testing and Red Teaming for AI Systems
  4. 4.4 Engineering Robust AI Systems Against Adversarial AI
Module 5: AI in Network Security
  1. 5.1 AI-Powered Intrusion Detection Systems
  2. 5.2 AI for Distributed Denial of Service (DDoS) Detection
  3. 5.3 AI-Based Network Anomaly Detection
  4. 5.4 Engineering Secure Network Architectures with AI
Module 6: AI in Endpoint Security
  1. 6.1 AI for Malware Detection and Classification
  2. 6.2 AI for Endpoint Detection and Response (EDR)
  3. 6.3 AI-Driven Threat Hunting
  4. 6.4 Implementing Lightweight AI Models for Resource-Constrained Devices
Module 7: Secure AI System Engineering
  1. 7.1 Designing Secure AI Architectures
  2. 7.2 Cryptography in AI for Security
  3. 7.3 Ensuring Model Explainability and Transparency in Security
  4. 7.4 Performance Optimization of AI Security Systems
Module 8: AI for Cloud and Container Security
  1. 8.1 AI for Securing Cloud Environments
  2. 8.2 AI-Driven Container Security
  3. 8.3 AI for Securing Serverless Architectures
  4. 8.4 AI and DevSecOps
Module 9: AI and Blockchain for Security
  1. 9.1 Fundamentals of Blockchain and AI Integration
  2. 9.2 AI for Fraud Detection in Blockchain
  3. 9.3 Smart Contracts and AI Security
  4. 9.4 AI-Enhanced Consensus Algorithms
Module 10: AI in Identity and Access Management (IAM)
  1. 10.1 AI for User Behavior Analytics in IAM
  2. 10.2 AI for Multi-Factor Authentication (MFA)
  3. 10.3 AI for Zero-Trust Architecture
  4. 10.4 AI for Role-Based Access Control (RBAC)
Module 11: AI for Physical and IoT Security
  1. 11.1 AI for Securing Smart Cities
  2. 11.2 AI for Industrial IoT Security
  3. 11.3 AI for Autonomous Vehicle Security
  4. 11.4 AI for Securing Smart Homes and Consumer IoT
Module 12: Capstone Project - Engineering AI Security Systems
  1. 12.1 Defining the Capstone Project Problem
  2. 12.2 Engineering the AI Solution
  3. 12.3 Deploying and Monitoring the AI System
  4. 12.4 Final Capstone Presentation and Evaluation
Optional Module: AI Agents for Security level 3
  1. 1. Understanding AI Agents
  2. 2. Case Studies
  3. 3. Hands-On Practice with AI Agents

Frequently Asked Questions

You will learn how AI and machine learning enhance cybersecurity, including threat detection, network security, adversarial AI defense, secure AI systems, cloud security, and more. You'll also apply these concepts in a hands-on capstone project.

The course explores the use of AI to enhance blockchain security, such as fraud detection and transaction monitoring, as well as its application in securing containerized environments by automating threat detection and improving system reliability.

Basic programming knowledge is helpful, especially in Python, as the course involves implementing AI models. However, tutorials and resources are provided to help you learn necessary coding skills throughout the course.

Yes, if you're already working in cybersecurity, this course will deepen your expertise in integrating AI for advanced threat detection, automating security protocols, and strengthening defenses across networks, endpoints, and cloud systems.

While the course is designed for individuals with an intermediate level of experience in cybersecurity, it offers foundational insights into AI, making it accessible for learners looking to specialize in AI-driven security solutions.