AI+ Security: Level 3|System Source

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.


07/28/25 - NVT - Virtual Classroom - Live Virtual	(click to enroll)
08/25/25 - NVT - Virtual Classroom - Live Virtual	(click to enroll)
09/29/25 - NVT - Virtual Classroom - Live Virtual	(click to enroll)
10/27/25 - NVT - Virtual Classroom - Live Virtual	(click to enroll)

Gain proficiency in applying deep learning algorithms for advanced cyber defense applications, such as malware analysis, phishing detection, and predictive threat modeling.
Develop expertise in integrating AI with cloud and container security, emphasizing scalable and automated threat mitigation for cloud-based platforms and containerized applications.
Master the application of AI techniques to enhance identity and access management by streamlining identity verification, managing access control systems, and securing authentication processes.
Explore the use of AI to secure IoT devices by addressing unique challenges, including detecting compromised devices and safeguarding communication protocols.

Foundations of AI and Machine Learning for Security Engineering

Core AI and ML Concepts for Security
AI Use Cases in Cybersecurity
Engineering AI Pipelines for Security
Challenges in Applying AI to Security

Machine Learning for Threat Detection and Response

Engineering Feature Extraction for Cybersecurity Datasets
Supervised Learning for Threat Classification
Unsupervised Learning for Anomaly Detection
Engineering Real-Time Threat Detection Systems

Deep Learning for Security Applications

Convolutional Neural Networks (CNNs) for Threat Detection
Recurrent Neural Networks (RNNs) and LSTMs for Security
Autoencoders for Anomaly Detection
Adversarial Deep Learning in Security

Adversarial AI in Security

Introduction to Adversarial AI Attacks
Defense Mechanisms Against Adversarial Attacks
Adversarial Testing and Red Teaming for AI Systems
Engineering Robust AI Systems Against Adversarial AI

AI in Network Security

AI-Powered Intrusion Detection Systems
AI for Distributed Denial of Service (DDoS) Detection
AI-Based Network Anomaly Detection
Engineering Secure Network Architectures with AI

AI+ Security: Level 1
AI+ Security: Level 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

Security Analyst
Cybersecurity Specialist
Security Consultant

AI+ Security: Level 3

Course Overview

Scheduled Classes

What You'll Learn

Outline

Prerequisites

Who Should Attend

Next Step Courses