AI+ Security Level 3™ Self-Paced Online Course

AI+ Security Level 3™ Self-Paced Online Course

Master the Future of Cybersecurity with AI-Driven Solutions

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.

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Overview

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.

Who should enroll

  • 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.

Why this certification matters

  • IoT Security Using AI Understanding how to protect IoT devices with AI-driven solutions to prevent vulnerabilities.
  • Deep Learning for Threat Detection Expertise in leveraging deep learning algorithms for advanced threat analysis and response.
  • AI-Driven Network Security Ability to implement AI tools for securing network infrastructures and preventing cyber-attacks.
  • Endpoint Protection with AI Competence in using AI technologies to enhance endpoint security and protect devices from attacks.

 

Learning Modules

13 modules
01Module 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
02Module 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
03Module 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
04Module 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
05Module 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
06Module 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
07Module 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
08Module 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
09Module 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
10Module 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)
11Module 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
12Module 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
13Optional Module: AI Agents for Security level 3
Understanding AI Agents
Case Studies
Hands-On Practice with AI Agents

Tools Covered

Splunk User Behavior Analytics (UBA)
Splunk User Behavior Analytics (UBA)
Microsoft Defender for Endpoint
Microsoft Defender for Endpoint
Microsoft Azure AD Conditional Access
Microsoft Azure AD Conditional Access
Adversarial Robustness Toolbox (ART)
Adversarial Robustness Toolbox (ART)
Format

Online, self-paced

Duration

Instructor-Led: 5 Days (live or virtual) 
Self-Paced: 40 hours of content

Exam

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

Included

Self-paced course + Official exam + Digital badge

Prerequisites

Completion of AI+ Security Level 1™ and 2™, Advanced Python programming, ML and Cybersecurity Knowledge, Cloud/Container expertise, Linux/CLI mastery.

Delivery

Projects & case studies

Outcome

Industry-recognized credential + hands-on experience

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