AI+ Security: Level 3 (AI+ Security: Level 3)

• 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.

Required

• 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

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 in Endpoint Security

• AI for Malware Detection and Classification
• AI for Endpoint Detection and Response(EDR)
• AI-Driven Threat Hunting
• Implementing Lightweight AI Models for Resource-Constrained Devices

Secure AI System Engineering

• Designing Secure AI Architectures
• Cryptography in AI for Security
• Ensuring Model Explainability and Transparency in Security
• Performance Optimization of AI Security Systems

AI for Cloud and Container Security

• AI for Securing Cloud Environments
• AI-Driven Container Security
• AI for Securing Serverless Architectures
• AI and DevSecOps

AI and Blockchain for Security

• Fundamentals of Blockchain and AI Integration
• AI for Fraud Detection in Blockchain
• Smart Contracts and AI Security
• AI-Enhanced Consensus Algorithms

AI in Identity and Access Management (IAM)

• AI for User Behavior Analytics in IAM
• AI for Multi-Factor Authentication (MFA)
• AI for Zero-Trust Architecture
• AI for Role-Based Access Control (RBAC)

AI for Physical and IoT Security

• AI for Securing Smart Cities
• AI for Industrial IoT Security
• AI for Autonomous Vehicle Security
• AI for Securing Smart Homes and Consumer IoT

Capstone Project - Engineering AI Security Systems

• Defining the Capstone Project Problem
• Engineering the AI Solution
• Deploying and Monitoring the AI System
• Final Capstone Presentation and Evaluation

Optional Module: AI Agents for Security level 3

• Understanding AI Agents
• Case Studies
• Hands-On Practice with AI Agents