AI+ Security Level 3™

My Account

Categories Security

AI+ Security Level 3™

Download Brochure

Take Assessment

$174.50

Expert

AI+ Security Level 3.

40h

1 Year.

Training 5 or more people?

Master the Future of Cybersecurity with AI-Driven Solutions.

The AI+ Security Level 3™ course provides a comprehensive exploration of the intersection between AI and cybersecurity, focusing on advanced topics critical to modern security engineering.
It covers foundational concepts in AI and machine learning for security, delving into areas like threat detection, response mechanisms, and the use of deep learning for security applications.
The course addresses the challenges of adversarial AI, network and endpoint security, and secure AI system engineering, along with emerging topics such as AI for cloud, container security, and blockchain integration.
Key subjects also include AI in identity and access management (IAM), IoT security, and physical security systems, culminating in a hands-on capstone project that tasks learners with designing and engineering AI-driven security solutions.

Course Curriculum

Module 1: Foundations of AI and Machine Learning for Security Engineering
1.1 Core AI and ML Concepts for Security 1.2 AI Use Cases in Cybersecurity 1.3 Engineering AI Pipelines for Security 1.4 Challenges in Applying AI to Security

Module 2: Machine Learning for Threat Detection and Response
2.1 Engineering Feature Extraction for Cybersecurity Datasets 2.2 Supervised Learning for Threat Classification 2.3 Unsupervised Learning for Anomaly Detection 2.4 Engineering Real-Time Threat Detection Systems

Module 3: Deep Learning for Security Applications
3.1 Convolutional Neural Networks (CNNs) for Threat Detection 3.2 Recurrent Neural Networks (RNNs) and LSTMs for Security 3.3 Autoencoders for Anomaly Detection 3.4 Adversarial Deep Learning in Security

Module 4: Adversarial AI in Security
4.1 Introduction to Adversarial AI Attacks 4.2 Defense Mechanisms Against Adversarial Attacks 4.3 Adversarial Testing and Red Teaming for AI Systems 4.4 Engineering Robust AI Systems Against Adversarial AI

Module 5: AI in Network Security
5.1 AI-Powered Intrusion Detection Systems 5.2 AI for Distributed Denial of Service (DDoS) Detection 5.3 AI-Based Network Anomaly Detection 5.4 Engineering Secure Network Architectures with AI

Module 6: AI in Endpoint Security
6.1 AI for Malware Detection and Classification 6.2 AI for Endpoint Detection and Response (EDR) 6.3 AI-Driven Threat Hunting 6.4 Implementing Lightweight AI Models for Resource-Constrained Devices

Module 7: Secure AI System Engineering
7.1 Designing Secure AI Architectures 7.2 Cryptography in AI for Security 7.3 Ensuring Model Explainability and Transparency in Security 7.4 Performance Optimization of AI Security Systems

Module 8: AI for Cloud and Container Security
8.1 AI for Securing Cloud Environments 8.2 AI-Driven Container Security 8.3 AI for Securing Serverless Architectures 8.4 AI and DevSecOps

Module 9: AI and Blockchain for Security
9.1 Fundamentals of Blockchain and AI Integration 9.2 AI for Fraud Detection in Blockchain 9.3 Smart Contracts and AI Security 9.4 AI-Enhanced Consensus Algorithms

Module 10: AI in Identity and Access Management (IAM)
10.1 AI for User Behavior Analytics in IAM 10.2 AI for Multi-Factor Authentication (MFA) 10.3 AI for Zero-Trust Architecture 10.4 AI for Role-Based Access Control (RBAC)

Module 11: AI for Physical and IoT Security
11.1 AI for Securing Smart Cities 11.2 AI for Industrial IoT Security 11.3 AI for Autonomous Vehicle Security 11.4 AI for Securing Smart Homes and Consumer IoT

Module 12: Capstone Project – Engineering AI Security Systems
12.1 Defining the Capstone Project Problem 12.2 Engineering the AI Solution 12.3 Deploying and Monitoring the AI System 12.4 Final Capstone Presentation and Evaluation

Optional Module: AI Agents for Security level 3
Understanding AI Agents Case Studies Hands-On Practice with AI Agents

The Al+ Security Level 3 course provides a comprehensive exploration of the intersection between Al and cybersecurity, focusing on advanced topics critical to modern security engineering.
It covers foundational concepts in Al and machine learning for security, delving into areas like threat detection, response mechanisms, and the use of deep learning for security applications.
The course addresses the challenges of adversarial Al, network and endpoint security, and secure Al system engineering, along with emerging topics such as Al for cloud, container security, and blockchain integration.
Key subjects also include Al in identity and access management (IAM), IoT security, and physical security systems, culminating in a hands-on capstone project that tasks learners with designing and engineering Al-driven security solutions.

Cybersecurity Professionals.
Risk Management Specialists.
Compliance Officers.
IT Security Analysts.
Ethical Hackers and Penetration Testers.
Tech-Savvy Leaders.
Aspiring Al Security Experts.

Job Roles & Industry Outlook

IoT Security Specialist:

Use Al to protect IoT devices and networks, ensuring the security of interconnected systems in industries like healthcare, manufacturing, and smart cities.

Cloud Security Architect:

Leverage Al to enhance cloud security, focusing on areas like container security, threat detection, and incident response in cloud environments.

Identity and Access Management (IAM) Engineer:

Develop Al-powered IAM solutions to improve access control, and identity verification processes for large-scale organizations.

Security Consultant for Al- Driven Solutions:

Advise on implementing Al-driven security technologies, offering best practices and system integration for optimal protection.

Exam Information

Duration

90 Minutes

Passing Score

70% (35/50)

Format

50 multiple-choice/multiple-response questions

Fees include

One year of access to course content, including continuous updates. Certification exam. One exam re-take.

Exam Blueprint:

Foundations of AI & ML for Security Engineering 8%

Machine Learning for Threat Detection & Response 10%

Deep Learning for Security Applications 10%

Adversarial AI in Security 12%

AI in Network Security 10%

AI in Endpoint Security 10%

Secure AI System Engineering 10%

AI for Cloud & Container Security 10%

AI & Blockchain for Security 8%

AI in Identity & Access Management (IAM) 8%

AI for Physical & IoT Security 8%

Capstone Project – Engineering AI Security Systems 6%

Explore our Schedules

No data was found

Certificate of Completion

AI+ Security Level 3.

$174.50

Expert

AI+ Security Level 3.

40h

1 Year.

Training 5 or more people?

AI+ Security Level 3™

Master the Future of Cybersecurity with AI-Driven Solutions.

Course Curriculum

Module 1: Foundations of AI and Machine Learning for Security Engineering 1.1 Core AI and ML Concepts for Security 1.2 AI Use Cases in Cybersecurity 1.3 Engineering AI Pipelines for Security 1.4 Challenges in Applying AI to Security

Module 2: Machine Learning for Threat Detection and Response 2.1 Engineering Feature Extraction for Cybersecurity Datasets 2.2 Supervised Learning for Threat Classification 2.3 Unsupervised Learning for Anomaly Detection 2.4 Engineering Real-Time Threat Detection Systems

Module 3: Deep Learning for Security Applications 3.1 Convolutional Neural Networks (CNNs) for Threat Detection 3.2 Recurrent Neural Networks (RNNs) and LSTMs for Security 3.3 Autoencoders for Anomaly Detection 3.4 Adversarial Deep Learning in Security

Module 4: Adversarial AI in Security 4.1 Introduction to Adversarial AI Attacks 4.2 Defense Mechanisms Against Adversarial Attacks 4.3 Adversarial Testing and Red Teaming for AI Systems 4.4 Engineering Robust AI Systems Against Adversarial AI

Module 5: AI in Network Security 5.1 AI-Powered Intrusion Detection Systems 5.2 AI for Distributed Denial of Service (DDoS) Detection 5.3 AI-Based Network Anomaly Detection 5.4 Engineering Secure Network Architectures with AI

Module 6: AI in Endpoint Security 6.1 AI for Malware Detection and Classification 6.2 AI for Endpoint Detection and Response (EDR) 6.3 AI-Driven Threat Hunting 6.4 Implementing Lightweight AI Models for Resource-Constrained Devices

Module 7: Secure AI System Engineering 7.1 Designing Secure AI Architectures 7.2 Cryptography in AI for Security 7.3 Ensuring Model Explainability and Transparency in Security 7.4 Performance Optimization of AI Security Systems

Module 8: AI for Cloud and Container Security 8.1 AI for Securing Cloud Environments 8.2 AI-Driven Container Security 8.3 AI for Securing Serverless Architectures 8.4 AI and DevSecOps

Module 9: AI and Blockchain for Security 9.1 Fundamentals of Blockchain and AI Integration 9.2 AI for Fraud Detection in Blockchain 9.3 Smart Contracts and AI Security 9.4 AI-Enhanced Consensus Algorithms

Module 10: AI in Identity and Access Management (IAM) 10.1 AI for User Behavior Analytics in IAM 10.2 AI for Multi-Factor Authentication (MFA) 10.3 AI for Zero-Trust Architecture 10.4 AI for Role-Based Access Control (RBAC)

Module 11: AI for Physical and IoT Security 11.1 AI for Securing Smart Cities 11.2 AI for Industrial IoT Security 11.3 AI for Autonomous Vehicle Security 11.4 AI for Securing Smart Homes and Consumer IoT

Module 12: Capstone Project – Engineering AI Security Systems 12.1 Defining the Capstone Project Problem 12.2 Engineering the AI Solution 12.3 Deploying and Monitoring the AI System 12.4 Final Capstone Presentation and Evaluation

Optional Module: AI Agents for Security level 3 Understanding AI Agents Case Studies Hands-On Practice with AI Agents

Learning Objectives

Resource Center

Target Audience

Job Roles & Industry Outlook

Exam Information

Exam Blueprint:

Explore our Schedules

Certificate of Completion

AI+ Security Level 3.

Learners Also Enrolled For

What you'll learn

What you'll learn

What you'll learn

What you'll learn

What you'll learn

What you'll learn

What you'll learn

What you'll learn

What you'll learn

What you'll learn

Quick Links

Policies

Login

Module 1: Foundations of AI and Machine Learning for Security Engineering
1.1 Core AI and ML Concepts for Security 1.2 AI Use Cases in Cybersecurity 1.3 Engineering AI Pipelines for Security 1.4 Challenges in Applying AI to Security

Module 2: Machine Learning for Threat Detection and Response
2.1 Engineering Feature Extraction for Cybersecurity Datasets 2.2 Supervised Learning for Threat Classification 2.3 Unsupervised Learning for Anomaly Detection 2.4 Engineering Real-Time Threat Detection Systems

Module 3: Deep Learning for Security Applications
3.1 Convolutional Neural Networks (CNNs) for Threat Detection 3.2 Recurrent Neural Networks (RNNs) and LSTMs for Security 3.3 Autoencoders for Anomaly Detection 3.4 Adversarial Deep Learning in Security

Module 4: Adversarial AI in Security
4.1 Introduction to Adversarial AI Attacks 4.2 Defense Mechanisms Against Adversarial Attacks 4.3 Adversarial Testing and Red Teaming for AI Systems 4.4 Engineering Robust AI Systems Against Adversarial AI

Module 5: AI in Network Security
5.1 AI-Powered Intrusion Detection Systems 5.2 AI for Distributed Denial of Service (DDoS) Detection 5.3 AI-Based Network Anomaly Detection 5.4 Engineering Secure Network Architectures with AI

Module 6: AI in Endpoint Security
6.1 AI for Malware Detection and Classification 6.2 AI for Endpoint Detection and Response (EDR) 6.3 AI-Driven Threat Hunting 6.4 Implementing Lightweight AI Models for Resource-Constrained Devices

Module 7: Secure AI System Engineering
7.1 Designing Secure AI Architectures 7.2 Cryptography in AI for Security 7.3 Ensuring Model Explainability and Transparency in Security 7.4 Performance Optimization of AI Security Systems

Module 8: AI for Cloud and Container Security
8.1 AI for Securing Cloud Environments 8.2 AI-Driven Container Security 8.3 AI for Securing Serverless Architectures 8.4 AI and DevSecOps

Module 9: AI and Blockchain for Security
9.1 Fundamentals of Blockchain and AI Integration 9.2 AI for Fraud Detection in Blockchain 9.3 Smart Contracts and AI Security 9.4 AI-Enhanced Consensus Algorithms

Module 10: AI in Identity and Access Management (IAM)
10.1 AI for User Behavior Analytics in IAM 10.2 AI for Multi-Factor Authentication (MFA) 10.3 AI for Zero-Trust Architecture 10.4 AI for Role-Based Access Control (RBAC)

Module 11: AI for Physical and IoT Security
11.1 AI for Securing Smart Cities 11.2 AI for Industrial IoT Security 11.3 AI for Autonomous Vehicle Security 11.4 AI for Securing Smart Homes and Consumer IoT

Module 12: Capstone Project – Engineering AI Security Systems
12.1 Defining the Capstone Project Problem 12.2 Engineering the AI Solution 12.3 Deploying and Monitoring the AI System 12.4 Final Capstone Presentation and Evaluation

Optional Module: AI Agents for Security level 3
Understanding AI Agents Case Studies Hands-On Practice with AI Agents