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This course is designed for security professionals, AI engineers, and cybersecurity leaders looking to safeguard AI applications, models, and data infrastructure.

Prerequisites

  • Completion of AI+ Security Level 1™ & 2™: Prior knowledge recommended for advanced AI security concepts.
  • Python & Deep Learning Expertise: Proficiency in Python, TensorFlow, and PyTorch.
  • Machine Learning & Adversarial AI: Strong understanding of deep learning and AI model security.
  • Advanced Cybersecurity Skills: Experience in threat detection, incident response, and endpoint security.
  • AI in Security Engineering: Knowledge of IAM, IoT security, and physical security integration.
  • Cloud & Container Security: Familiarity with cloud security, containerization, and blockchain.
  • Linux & CLI Mastery: Advanced command-line skills and experience with security tools.

 
Recertification Requirements
    To maintain the validity of your certification, The Whiteboard, in affiliation with AI CERTs, requires annual recertification. A notification will be sent three months before the due date, guiding candidates through the process outlined in the candidate handbook. Need Assistance? For any questions or support regarding recertification, please contact The Whiteboard's team at info@thewhiteboard.co.in.

Course Modules and Curriculum

  • 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)

Tools

Adversarial Robustness Toolbox (ART)

Adversarial Robustness Toolbox (ART)

Microsoft Azure AD Conditional Access

Microsoft Azure AD Conditional Access

Microsoft Defender for Endpoint

Microsoft Defender for Endpoint

Splunk User Behavior Analytics (UBA)

Splunk User Behavior Analytics (UBA)

Who Should Enroll?

This certification is ideal for:

  • Cybersecurity Experts & Threat Analysts
    - Gain advanced knowledge to detect, prevent, and mitigate AI-related cyber risks.
  • AI & Machine Learning Engineers
    - Learn how to secure AI models, training data, and deployment infrastructure.
  • Security Architects & IT Leaders
    - Understand AI-powered security frameworks and adversarial attack defense.
  • Risk & Compliance Officers
    - Ensure AI security aligns with industry regulations and ethical AI principles.
Key Learning Outcomes
  • Identify emerging AI security threats and vulnerabilities.
  • Implement advanced AI security protocols and adversarial defense techniques.
  • Gain expertise in AI model protection, secure deployment, and threat monitoring.
  • Develop strategies to mitigate risks associated with AI-powered cyberattacks.

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Exam Objectives

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Deep Learning for Cyber Defense

Gain expertise in applying deep learning algorithms for malware detection, phishing prevention, and predictive threat analysis.

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AI for Cloud & Container Security

Learn how AI enhances security in cloud environments and containerized applications, improving scalability and automated threat response.

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AI-Driven Identity & Access Management

Apply AI techniques to strengthen identity verification, access control, and authentication security.

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AI-Powered IoT Security

Understand how AI mitigates IoT security risks, detects compromised devices, and secures communication protocols.

Career Opportunities Post-Certification

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Median Salaries

$59,391

AI Salary Icon

With AI Skills

$134,143

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% Difference

126

Product Manager

AI Security Consultant

Advise organizations on implementing AI-driven security solutions, ensuring best practices and seamless system integration for robust protection.

Data Analyst

IAM Engineer (AI-Powered)

Develop AI-based identity and access management (IAM) solutions to enhance authentication, access control, and identity verification for enterprises.

AI Architect

Cloud Security Architect

Utilize AI to strengthen cloud security, focusing on container security, threat detection, and automated incident response in cloud environments.

ML Engineer

IoT Security Specialist

Apply AI to safeguard IoT devices and networks, securing interconnected systems in industries like healthcare, manufacturing, and smart cities.

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Frequently Asked Questions

AI Security Level 3 focuses on advanced AI security strategies, proactive defense mechanisms, and compliance frameworks. Unlike Level 1 and Level 2, this certification delves deeper into AI-powered cybersecurity solutions, regulatory compliance, and adversarial machine learning defense.

While Levels 1 and 2 provide a strong foundation, Level 3 is designed for professionals with prior experience in AI security, cybersecurity, or IT security. If you are familiar with AI risk management and security principles, you can enroll directly in Level 3.

This is a self-paced course, typically completed within 4-8 weeks, depending on your learning schedule.

Yes, the AI Security Level 3 Certification is designed to meet industry standards in cybersecurity, AI governance, and risk management. It is recognized by organizations in finance, healthcare, government, and technology.

Yes! Upon successfully completing the course, you will receive an industry-recognized AI Security Level 3 Certification, which you can showcase on your resume and LinkedIn profile.