AI+ Security Level 3™

Target group

• Cybersecurity Professionals: Individuals looking to enhance their skills in compliance and security management.

Risk Management Specialists: Those interested in improving risk assessment and mitigation strategies using AI.

IT Security Analysts: Analysts seeking to integrate AI technologies into their security practices and frameworks.

Tech-Savvy Leaders: IT managers or security architects aiming to future-proof their organizations with AI-enhanced compliance, governance, and security practices.

Compliance Officers: Professionals responsible for ensuring adherence to regulatory standards who want to leverage AI for compliance processes.

Course structure

Module 1: Foundations of AI and Machine Learning for Security Engineering

1. 1.1 Core AI and ML Concepts for Security
2. 1.2 AI Use Cases in Cybersecurity
3. 1.3 Engineering AI Pipelines for Security
4. 1.4 Challenges in Applying AI to Security

Module 2: Machine Learning for Threat Detection and Response

1. 2.1 Engineering Feature Extraction for Cybersecurity Datasets
2. 2.2 Supervised Learning for Threat Classification
3. 2.3 Unsupervised Learning for Anomaly Detection
4. 2.4 Engineering Real-Time Threat Detection Systems

Module 3: Deep Learning for Security Applications

1. 3.1 Convolutional Neural Networks (CNNs) for Threat Detection
2. 3.2 Recurrent Neural Networks (RNNs) and LSTMs for Security
3. 3.3 Autoencoders for Anomaly Detection
4. 3.4 Adversarial Deep Learning in Security

Module 4: Adversarial AI in Security

1. 4.1 Introduction to Adversarial AI Attacks
2. 4.2 Defense Mechanisms Against Adversarial Attacks
3. 4.3 Adversarial Testing and Red Teaming for AI Systems
4. 4.4 Engineering Robust AI Systems Against Adversarial AI

Module 5: AI in Network Security

1. 5.1 AI-Powered Intrusion Detection Systems
2. 5.2 AI for Distributed Denial of Service (DDoS) Detection
3. 5.3 AI-Based Network Anomaly Detection
4. 5.4 Engineering Secure Network Architectures with AI

Module 6: AI in Endpoint Security

1. 6.1 AI for Malware Detection and Classification
2. 6.2 AI for Endpoint Detection and Response (EDR)
3. 6.3 AI-Driven Threat Hunting
4. 6.4 Implementing Lightweight AI Models for Resource-Constrained Devices

Module 7: Secure AI System Engineering

1. 7.1 Designing Secure AI Architectures
2. 7.2 Cryptography in AI for Security
3. 7.3 Ensuring Model Explainability and Transparency in Security
4. 7.4 Performance Optimization of AI Security Systems

Module 8: AI for Cloud and Container Security

1. 8.1 AI for Securing Cloud Environments
2. 8.2 AI-Driven Container Security
3. 8.3 AI for Securing Serverless Architectures
4. 8.4 AI and DevSecOps

Module 9: AI and Blockchain for Security

1. 9.1 Fundamentals of Blockchain and AI Integration
2. 9.2 AI for Fraud Detection in Blockchain
3. 9.3 Smart Contracts and AI Security
4. 9.4 AI-Enhanced Consensus Algorithms

Module 10: AI in Identity and Access Management (IAM)

1. 10.1 AI for User Behavior Analytics in IAM
2. 10.2 AI for Multi-Factor Authentication (MFA)
3. 10.3 AI for Zero-Trust Architecture
4. 10.4 AI for Role-Based Access Control (RBAC)

Module 11: AI for Physical and IoT Security

1. 11.1 AI for Securing Smart Cities
2. 11.2 AI for Industrial IoT Security
3. 11.3 AI for Autonomous Vehicle Security
4. 11.4 AI for Securing Smart Homes and Consumer IoT

Module 12: Capstone Project – Engineering AI Security Systems

1. 12.1 Defining the Capstone Project Problem
2. 12.2 Engineering the AI Solution
3. 12.3 Deploying and Monitoring the AI System
4. 12.4 Final Capstone Presentation and Evaluation

Optional Module: AI Agents for Security level 3

1. Understanding AI Agents
2. Case Studies
3. Hands-On Practice with AI Agents

Certification

50 questions, 70% passing, 90 minutes, online proctored exam