AI+ Security Level 3™
AI+ Security Level 3™
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.
Prerequisites
- 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).
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
Splunk User Behavior Analytics (UBA)
Microsoft Defender for Endpoint
Microsoft Azure AD Conditional Access
Adversarial Robustness Toolbox (ART)
Passing Score : 70% (35/50)
Number of Multiple Choice Question : 50 MCQs, 90 Minutes
Number of exams required for certification : 1
Exam Blueprint
- Foundations of AI and Machine Learning for Security Engineering – 6%
- Machine Learning for Threat Detection and Response – 7%
- Deep Learning for Security Applications – 7%
- Adversarial AI in Security – 10%
- AI in Network Security – 10%
- AI in Endpoint Security – 10%
- AI in Identity and Access Management (IAM) – 10%
- AI for Physical and IoT Security – 10%
- Capstone Project – Engineering AI Security Systems – 10%
Apply Deep Learning for Cyber Defense
Acquire expertise in using deep learning algorithms for advanced applications like malware analysis, phishing detection, and predictive threat modeling.
Integrate AI with Cloud and Container Security
Understand the use of AI for securing cloud-based platforms and containerized applications, focusing on scalability and automation in threat mitigation.
Enhance Identity and Access Management with AI
Learn to apply AI techniques to streamline identity verification, manage access control systems, and secure authentication processes.
Secure IoT Devices Using AI
Explore how AI can be used to address unique IoT security challenges, including detecting compromised devices and protecting communication protocols.
Identity and Access Management (IAM) Engineer:
Develop Al-powered IAM solutions to improve access control, and identity verification processes for large-scale organizations.
Security Consultant for Al-Driven Solutions:
Advise on implementing AI-driven security technologies, offering best practices and system integration for optimal protection.
IoT Security Specialist:
Use Al to protect IoT devices and networks, ensuring the security of interconnected systems in industries like healthcare, manufacturing, and smart cities.
Cloud Security Architect:
Leverage Al to enhance cloud security, focusing on areas like container security, threat detection, and incident response in cloud environments.
FAQ
What will I learn in this course?
You will learn how AI and machine learning enhance cybersecurity, including threat detection, network security, adversarial AI defense, secure AI systems, cloud security, and more. You’ll also apply these concepts in a hands-on capstone project.
How does the course integrate AI with blockchain and container security?
The course explores the use of AI to enhance blockchain security, such as fraud detection and transaction monitoring, as well as its application in securing containerized environments by automating threat detection and improving system reliability.
Do I need to know how to code for this course?
Basic programming knowledge is helpful, especially in Python, as the course involves implementing AI models. However, tutorials and resources are provided to help you learn necessary coding skills throughout the course.
Will this course help me in my current cybersecurity job?
Yes, if you’re already working in cybersecurity, this course will deepen your expertise in integrating AI for advanced threat detection, automating security protocols, and strengthening defenses across networks, endpoints, and cloud systems.
Is this course suitable for beginners in AI and cybersecurity?
While the course is designed for individuals with an intermediate level of experience in cybersecurity, it offers foundational insights into AI, making it accessible for learners looking to specialize in AI-driven security solutions.
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