AI+ Security Level 3™
AT-2103
Master the Future of Cybersecurity with AI-Driven SolutionsThe 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.
Why This Certification Matters
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.
At a Glance: Course + Exam Overview
- Instructor-Led: 5 Days (live or virtual)
- Self-Paced: 40 hours of content
Job Roles & Industry Outlook
Cyber Security Analyst
Protect digital assets by identifying vulnerabilities, monitoring threats, and applying AI to strengthen security defenses.
Business Analyst
Analyze business processes and ensure AI initiatives comply with legal, ethical, and regulatory standards.
Business Intelligence Analyst
Transform data into actionable insights by applying AI-driven analytics to guide smarter business decisions.
Industry Growth: AI+ Security Level 3™
- 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.
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.
What You'll Learn
- 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
- Understanding AI Agents
- Case Studies
- Hands-On Practice with AI Agents
Tools You'll Explore
Splunk User Behavior Analytics (UBA)
Microsoft Defender for Endpoint
Microsoft Azure AD Conditional Access
Adversarial Robustness Toolbox (ART)
Prerequisites
- Completion of AI+ Security Level 1™ and 2™, Advanced Python programming, ML and Cybersecurity Knowledge, Cloud/Container expertise, Linux/CLI mastery.
Exam Details
Duration
90 minutes
Passing Score
70% (35/50)
Format
50 multiple-choice/multiple-response questions
Delivery Method
Online via AI proctored exam platform (flexible scheduling)
Exam Blueprint
- Module 1: Foundations of AI and Machine Learning for Security Engineering - 7%
- Module 2: Machine Learning for Threat Detection and Response - 7%
- Module 3: Deep Learning for Security Applications - 7%
- Module 4: Adversarial AI in Security - 7%
- Module 5: AI in Network Security - 8%
- Module 6: AI in Endpoint Security - 8%
- Module 7: Secure AI System Engineering - 8%
- Module 8: AI for Cloud and Container Security - 8%
- Module 9: AI and Blockchain for Security - 8%
- Module 10: AI in Identity and Access Management (IAM) - 8%
- Module 11: AI for Physical and IoT Security - 8%
- Module 12: Capstone Project - Engineering AI Security Systems - 8%
- Optional Module: AI Agents for Security level 3 - 8%
Choose the Format That Fits Your Schedule
What's Included (One-Year Subscription + All Updates):
- High-Quality Videos, E-book (PDF & Audio), and Podcasts
- AI Mentor for Personalized Guidance
- Quizzes, Assessments, and Course Resources
- Online Proctored Exam with One Free Retake
- Comprehensive Exam Study Guide
- Access for Tablet & Phone
Frequently Asked Questions
The course includes a mix of theoretical knowledge and practical applications, culminating in an interactive capstone project. This structure ensures that participants gain both conceptual understanding and hands-on experience.
This course is ideal for developers, IT professionals, and anyone with a foundational understanding of AI and cloud computing who wants to enhance their skills in integrating AI with cloud platforms like AWS, Azure, or Google Cloud.
Participants will learn to develop, deploy, and manage AI models on leading cloud platforms. Skills include optimizing AI model performance, ensuring security, meeting compliance standards, and applying AI and cloud concepts to solve real-world problems.
This certification enhances your professional profile by demonstrating proficiency in integrating AI with cloud computing. It equips you with in-demand skills, giving you a competitive edge in the job market and opening doors to lucrative career opportunities.
The certification includes an interactive capstone project where participants apply their knowledge to design and implement AI solutions within cloud environments. This project is designed to simulate real-world scenarios and challenges.