AI+ Security Level 3 new AT-2103

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.

AI+ Security Level 3 new AT-2103

Virtual Instructor Led Online Schedule

Virtual Instructor-Led Online Training

Duration

5 Day

Price

$3,995.00

Virtual Instructor-Led Online Training

Duration

40 Hours (Self-Paced)

Price

$495.00

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Course Schedule

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Course Outline

Cybersecurity professionals (engineers, researchers, architects) who already have solid expertise and want to lead at the frontier of AI-based security
Individuals who have completed AI+ Security Level 1 and 2 (recommended prerequisites) Upskill Academy
Practitioners with proficiency in Python and machine learning who wish to apply those skills in advanced security contexts
Professionals in roles involving threat detection, security architecture, AI risk, or defensive engineerin

Completion of AI+ Security Level 1™ and 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
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).

Design AI-based security systems integrating threat detection, network, and endpoint modeling
Apply deep learning, adversarial AI defenses, and model robustness in security environments
Use AI in cloud, container, and blockchain contexts as defensive tools
Engineer secure AI architectures (transparency, cryptography, explainability)
Build AI-enhanced IAM systems, identity analytics, zero trust frameworks
Secure IoT / physical environments using AI models
Lead capstone-level AI security projects end-to-end
Communicate AI security designs and trade-offs to technical and managerial stakeholder

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

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

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

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

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

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

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

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

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

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)

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

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

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

Virtual Instructor-Led Online Training

Duration

5 Day

Price

$3,995.00

Virtual Instructor-Led Online Training

Duration

40 Hours (Self-Paced)

Price

$495.00