AI+ Security Level 3
Master the Future of Cybersecurity with AI-Driven Solutions.
Description
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.
Prerequisites
- Completion of AI+ Security Level 1™ and Level 2™.
- Intermediate/Advanced Python Programming Skills: Proficiency in Python, including deep learning frameworks such as TensorFlow and PyTorch.
- Machine Learning Knowledge: Understanding of adversarial AI, model training, and deep learning concepts.
- Advanced Cybersecurity Skills: Proficiency in threat detection, incident response, and network/endpoint security.
- AI in Security Engineering: Knowledge of AI for IAM, IoT security, and physical security systems.
- Cloud and Container Security: Familiarity with cloud security, containerization, and blockchain technologies.
- Linux/CLI Expertise: Advanced command-line skills with experience in Linux-based security tools.
- There are no mandatory prerequisites for certification. Certification is based solely on performance in the examination. However, candidates may choose to prepare through self-study or optional training offered by AI CERTS Authorized Training Partners (ATPs).
Course Outline
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