AI Security
AI+ Security Level 3
The AI+ Security Level 3™ course covers advanced AI and cybersecurity topics, including threat detection, deep learning, adversarial AI, and security for cloud, IoT, and blockchain. It includes practical applications in IAM and physical security, ending with a hands-on capstone project to design AI-driven security solutions.
Targeted Audience
Security Consultant for AI-Driven Solutions
Cloud Security Specialist
Cloud Security Architect
Iot Security Specialist
Identity and Access Management (IAM) Engineer
Modules | 12
Examination | 1
Passing Score | 70%
COURSE AGENDA
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