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.
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.
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
Facilitated by a highly qualified specialist, who has extensive knowledge and experience; this program will be conducted using extensively interactive methods, encouraging participants to share their own experiences and apply the program material to real-life work situations in order to stimulate group discussions and improve the efficiency of the subject coverage.
Percentages of the total course hour classification are:
At the completion of the course, all participants who successfully accomplished the required contact hours will receive an EdTech Training Participation Certificate as a testimony to their commitment to professional development and further education.