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AI+ Security Level 3™

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.

Certification Duration: 40 hours (5 Days)

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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

Modules

12

Examination

1

50 MCQs

90 Minutes

Passing Score

70%

Recertification Requirements

AI CERTs requires recertification every year to keep your certification valid. Notifications will be sent three months before the due date, and candidates must follow the steps in the candidate handbook to complete the process.

Need Help? If you have any questions or need assistance with recertification, please reach out to our support team at  support@aicerts.ai

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

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

Tools

Splunk User Behavior Analytics (UBA)

Microsoft Defender for Endpoint

Microsoft Azure AD Conditional Access

Adversarial Robustness Toolbox (ART)

Exam Objectives

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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.

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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.

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Enhance Identity and Access Management with AI

Learn to apply AI techniques to streamline identity verification, manage access control systems, and secure authentication processes.

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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.

Career Opportunities Post-Certification

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Median Salaries

$59,391
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With AI Skills

$1,34,143
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% Difference

126
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Cloud Security Architect

Leverage AI to enhance cloud security, focusing on areas like container security, threat detection, and incident response in cloud environments.

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IoT Security Specialist

Use AI to protect IoT devices and networks, ensuring the security of interconnected systems in industries like healthcare, manufacturing, and smart cities.

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Security Consultant for AI-Driven Solutions

Provide expert guidance on implementing AI-driven security technologies in various industries, advising on best practices and system integration for optimal protection.

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Identity and Access Management (IAM) Engineer

Develop AI-powered IAM solutions to improve authentication, access control, and identity verification processes for large-scale organizations.

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Frequently Asked Questions

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.

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.

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.

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.

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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