AI+ Security Level 3™ - eLearning (exam included)

AI+ Security Level 3™ - eLearning (exam included)

Lead the Next Era of Cybersecurity with AI-Powered Innovations

This certification provides a comprehensive deep dive into how Artificial Intelligence (AI) and Machine Learning (ML) are transforming cybersecurity. You’ll learn to harness AI for advanced threat detection, regulatory compliance, and risk management across networks, endpoints, IoT, and cloud environments. 

The program focuses on practical implementation through case studies, workshops, and real-world scenarios, giving you hands-on experience in defending against emerging threats, adversarial attacks, and shifting compliance demands. A capstone project will allow you to apply your expertise to a real-world cybersecurity challenge, preparing you to design and deploy AI-powered security solutions effectively.

Rising Demand for AI Security Professionals

• AI-powered cyber threats are rapidly evolving, driving demand for professionals skilled in countering advanced attacks and vulnerabilities.
• 84% of cybersecurity professionals agree that AI enhances their ability to combat cyber threats.
• High-growth areas: AI-Powered Threat Intelligence, Predictive Cyber Defense, Deep Learning for Security, Zero Trust AI Security Frameworks
• The global AI security market is projected to reach $133 billion by 2030, making it a prime career choice for those seeking high-impact roles in cybersecurity.

Key Features

• Course and material in English 
• Advanced level  (Category: AI+ Technical)
• 1 year access to the platform 24/7
• 40 hours of video lessons & multimedia resources

• 50 hours of study time recommendation 
• Quizzes, Assessments, and Course Resources
• Online Proctored Exam with One Free Retake

• Certification of completion included valid for 1 year

• Virtual Hands-on Lab included
• Tools You’ll Master:  Splunk User Behavior Analytics (UBA), Microsoft Defender for Endpoint, Microsoft Azure AD Conditional Access, Adversarial Robustness Toolbox (ART)

Learning Outcomes

• Leverage Deep Learning for Cyber Defense
  Develop skills in applying deep learning models to advanced cybersecurity tasks such as malware detection, phishing prevention, and predictive threat analysis.
• AI-Driven Cloud & Container Security
  Learn how AI enhances security for cloud infrastructures and containerized environments, with a focus on scalability, automation, and proactive threat response.
• AI-Enhanced Identity & Access Management
  Apply AI to optimize identity verification, strengthen access controls, and secure authentication mechanisms.
• AI-Powered IoT Security
  Discover AI strategies to tackle IoT-specific security risks, including identifying compromised devices and safeguarding communication channels.

Target Audience

• Cybersecurity Professionals & Analysts
• Risk Management Specialists
• Compliance Officers
• IT Security Analysts
• Ethical Hckers
• Penetration Testers
• Tech-Savvy Leaders
• Aspiring AI Security Experts

Prerequisites

• Completion of AI+ Security Level 1™ and Level 2™ (recommended).
• Intermediate to advanced Python programming skills, including experience with deep learning frameworks like TensorFlow and PyTorch.
• Strong understanding of machine learning concepts, including deep learning, adversarial AI, and model training.
• Advanced cybersecurity expertise in areas such as threat detection, incident response, and network/endpoint protection.
• Knowledge of AI applications in security engineering, covering IAM, IoT security, and physical security.
• Familiarity with cloud security, containerization, and blockchain technologies.
• Mastery of Linux/command-line operations, with experience using security tools in Linux environments.
• No mandatory prerequisites — certification is awarded based solely on examination performance.

Exam Details

• Duration: 90 minutes
• Passing :70% (35/50)
• Format: 50 multiple-choice/multiple-response questions
• Delivery Method: Online via proctored exam platform (flexible scheduling)
• Language: English

Course Content

Module 1: Foundations of AI and Machine Learning for Security Engineering

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

Module 2: Machine Learning for Threat Detection and Response

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

Module 3: Deep Learning for Security Applications

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

Module 4: Adversarial AI 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

Module 5: AI in Network Security

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

Module 6: AI in Endpoint Security

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

Module 7: Secure AI System Engineering

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

Module 8: AI for Cloud and Container Security

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

Module 9: Penetration Testing with Artificial Intelligence

9.1 Enhancing Efficiency in Identifying Vulnerabilities Using AI

9.2 Automating Threat Detection and Adapting to Evolving Attack Patterns

9.3 Strengthening Organizations Against Cyber Threats Using AI-driven Penetration Testing

9.4 Tools and Technology: Penetration Testing Tools, AI-based Vulnerability Scanners

Module 10: AI in Identity and Access Management (IAM)

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)

Module 11: AI for Physical and IoT Security

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

Module 12: Capstone Project - Engineering AI Security Systems

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

Optional Module: AI Agents for Security level 3

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

Licensing and accreditation

This course is offered by AVC according to Partner Program Agreement and complies with the License Agreement requirements. 

Equity Policy

AVC does not provide accommodations due to a disability or medical condition of any students. Candidates are encouraged to reach out to AVC for guidance and support throughout the accommodation process.

FAQ

What will I learn in this course?
Gain skills in applying AI and machine learning to enhance cybersecurity, covering areas such as threat detection, network security, adversarial AI defense, secure AI system design, and cloud security. You’ll also work on a hands-on capstone project to apply your knowledge.

How is AI used for blockchain and container security in the course?
Learn how AI strengthens blockchain security through techniques like fraud detection and transaction monitoring, and how it secures containerized environments by automating threat detection and boosting system reliability.

Do I need coding skills for this course?
Some Python knowledge is beneficial since the course includes AI model implementation, but step-by-step tutorials and resources are provided to help you develop the required programming skills.

Will this course help in my current cybersecurity role?
Yes. It will expand your expertise in using AI for advanced threat detection, automating security operations, and enhancing defenses across networks, endpoints, and cloud systems.

Is the course beginner-friendly?
While aimed at those with intermediate cybersecurity knowledge, the course also introduces foundational AI concepts, making it approachable for learners aiming to specialize in AI-driven security.

Recertification Requirements

AI+ Technical courses require recertification every year to keep your certification valid. Notifications will be sent three months before the due date

What's the difference between AI+ Security Level 1,2,and 3?

Each certification level is designed to scale your AI security knowledge and career readiness:

Level 1 – Foundational AI Security

• • Focus & Learning Scope: Basics of AI security, machine learning for threat detection, phishing, malware analysis, AI-based authentication.
  • Technical Requirement: Basic Python knowledge, foundational cybersecurity concepts.

• Ideal For: Beginners and early-career cybersecurity professionals building AI-integrated security skills.

Level 2 – Intermediate AI Security

• • Focus & Learning Scope: Broader AI application in security, adversarial AI defense basics, AI-powered authentication, GANs for simulations, and advanced threat analysis.
  • Technical Requirement: Python basics, familiarity with machine learning and core AI concepts.

• Ideal For: Intermediate professionals who want to deepen their AI-driven security capabilities and apply them in real-world contexts.

Level 3 – Advanced AI Security Engineering

• Focus & Learning Scope: Complex AI applications in cybersecurity engineering, including adversarial AI, deep learning, secure system engineering, IAM, IoT security, cloud/container protection, and blockchain.
• Technical Requirement: Advanced Python (TensorFlow/PyTorch), deep learning, cloud security, IAM, IoT, and blockchain familiarity.
• Ideal For: Experienced professionals and security engineers aiming for leadership or specialist roles in AI-focused cybersecurity.

How Can AVC Help Foster an AI-Ready Culture?
While AI offers significant advantages, many organizations struggle with challenges like talent gaps, complex data environments, and system integration barriers. At AVC, we understand these obstacles and have tailored our certification programs to help businesses overcome them effectively.

Our strategic approach focuses on building a culture that embraces AI adoption and innovation. Through our industry-recognized certifications and in-depth training, we equip your workforce with the skills and knowledge needed to lead your organization confidently into an AI-powered future.

Customized for Impact: Our programs aren't one-size-fits-all. We offer specialized training designed by industry experts to equip your workforce with the specific skills and knowledge needed for critical AI roles.

Practical, Real-World Learning: We prioritize hands-on experience over theory, using real-world projects and case studies. This approach ensures your team gains the confidence and capability to implement AI solutions effectively, driving innovation and measurable business outcomes.