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AI+ Security Level 3™

# AT-2103

Master the Future of Cybersecurity with AI-Driven Solutions
  • Certification Duration: 40 Hours
The AI+ Security Level 3™ Certification is an advanced cybersecurity course that integrates Artificial Intelligence (AI) and Machine Learning (ML) to address modern security challenges. This program explores AI-driven security solutions for threat detection, incident response, and deep learning applications. Topics include adversarial AI, network security, cloud security, container security, and blockchain integration. Learners will gain expertise in securing IoT devices, identity and access management (IAM) systems, and physical security infrastructure. The course culminates in a hands-on capstone project that empowers participants to design and engineer AI-powered cybersecurity solutions for real-world scenarios.

$495.00

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Prerequisites

  • Completion of AI+ Security Level 1™ and Level 2™. 
  • Intermediate/Advanced Python Programming Skills: Proficiency in Python, including deep learning frameworks such as TensorFlow and PyTorch. 
  • Machine Learning Knowledge: Understanding of adversarial AI, model training, and deep learning concepts. 
  • Advanced Cybersecurity Skills: Proficiency in threat detection, incident response, and network/endpoint security. 
  • AI in Security Engineering: Knowledge of AI for IAM, IoT security, and physical security systems. 
  • Cloud and Container Security: Familiarity with cloud security, containerization, and blockchain technologies. 
  • Linux/CLI Expertise: Advanced command-line skills with experience in Linux-based security tools. 

Exam Details

  • Modules (12)
  • Examination (1)
  • 50 MCQs, 90 Minutes
  • Passing Score (70% (35/50) )

Exam Blueprint

ModulesPercentage
Foundations of AI and Machine Learning for Security Engineering 6
Machine Learning for Threat Detection and Response 7
Deep Learning for Security Applications7
Adversarial AI in Security 10
AI in Network Security10
AI in Endpoint Security10
AI in Identity and Access Management (IAM) 10
AI for Physical and IoT Security 10
Capstone Project – Engineering AI Security Systems10

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.
Labs
Interactive lab sessions to apply concepts and strengthen technical skills.
Module Wise Quizzes
Interactive assessments to reinforce learning and test conceptual clarity.
Additional Resources
Listen and learn anytime with convenient audio-based knowledge sharing.

Tools You'll Master

Splunk User Behavior Analytics (UBA)
Microsoft Defender for Endpoint
Microsoft Azure AD Conditional Access
Adversarial Robustness Toolbox (ART)

What Will You Learn?

Advanced AI Cybersecurity Techniques
Develop expertise in addressing adversarial AI challenges, securing IoT devices, and implementing blockchain-based security solutions.
Adversarial AI and Emerging Technologies
Develop expertise in addressing adversarial AI challenges, securing IoT devices, and implementing blockchain-based security solutions.
AI-Powered Security System Design
Acquire practical skills through a capstone project, designing AI-driven solutions for identity management, cloud security, and physical security architecture.
Leadership in AI-Driven Cybersecurity
Prepare to take on leadership roles in AI security engineering, ensuring organisations remain secure and adaptable in today’s evolving digital landscape.

Certification 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

Earn a Shareable Certificate

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Industry Opportunities after Certification Completion

Median Salaries
$59,391
With AI+ Security Level 3™
$134,143
% Difference
126
Begin your Journey
Security Consultant for AI-Driven Solutions

Provide expert guidance in implementing AI-powered security technologies, optimising protection and best practices for diverse industries.

Identity and Access Management (IAM) Engineer

Develop AI-powered IAM solutions to improve authentication, access control, and identity verification for large enterprises.

Cloud Security Architect

Use AI to enhance cloud security, with a focus on container security, incident response, and threat detection across cloud ecosystems.

IoT Security Specialist

Apply AI to secure IoT networks and devices across industries like healthcare, manufacturing, and smart city infrastructure.

Trusted Reviews by Our Learners

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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.
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