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AI+ Security Level 3™

AI+ Security Level 3™

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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Certificate Code

AT-2103

Exam Format

AI-Driven Remote Exam Proctoring

Course Overview

Important details and certification information

Instructor-led OR Self-paced course + Official exam + Digital badge
Instructor-Led: 3 Days (live or virtual)
Completion of AI+ Security Level 1™ and 2™, Advanced Python programming, ML and Cybersecurity Knowledge, Cloud/Container expertise, Linux/CLI mastery.
50 questions, 70% passing, 90 minutes, online proctored exam

Certification Modules

  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

  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

  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

  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

  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

  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

  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

  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

  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

  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)

  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

  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

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

AI Tools Covered

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