AI+ Security Level 3™ - eLearning (exam included)
448,00 EUR
- 40 hours
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.
Key Features
Language
Course and material in English
Level
Intermediate level (Category: AI+ Technical)
1 year platform access
and Virtual Hands-on Lab included
40 hours of video lessons & multimedia
50 hours of study time recommendation
Material
Video, PDF Material, audio eBook, Podcasts, quizzes and assessments.
Exam
Online Proctored Exam with One Free Retake
Certificate
Certification of completion included. Valid for 1 year
Tools You’ll Master
Splunk User Behavior Analytics (UBA), Microsoft Defender for Endpoint, Microsoft Azure AD Conditional Access, Adversarial Robustness Toolbox (ART)
About the course
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.
Learning Outcomes
At the end of this course, you will be able to:
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.
Course timeline
Foundations of AI and Machine Learning for Security Engineering
Lesson 1
- 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
Machine Learning for Threat Detection and Response
Lesson 2
- 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
Deep Learning for Security Applications
Lesson 3
- 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
Adversarial AI in Security
Lesson 4
- 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
AI in Network Security
Lesson 5
- 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
AI in Endpoint Security
Lesson 6
- 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
Secure AI System Engineering
Lesson 7
- 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
AI for Cloud and Container Security
Lesson 8
- 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
Penetration Testing with Artificial Intelligence
Lesson 9
- 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
AI in Identity and Access Management (IAM)
Lesson 10
- 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)
AI for Physical and IoT Security
Lesson 11
- 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
Capstone Project - Engineering AI Security Systems
Lesson 12
- 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
AI Agents for Security level 3
Optional Module
- 1. Understanding AI Agents
- 2. Case Studies
- 3. Hands-On Practice with AI Agents
Industry Growth
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.
Who Should Enroll in this Program?
Cybersecurity Professionals & Analysts
Penetration Testers & Threat Hunters
Security Consultants
Incident Responders
Security Engineers
Compliance Auditors
Network Security Administrators
IT Professionals & System Administrators
Business Leaders & Decision Makers
Software Developers
More Details
Prerequisites
- Completion of AI+ Security Level 1™ is recommended but not required.
- Basic Python knowledge, including variables, loops, and functions.
- Understanding of the CIA triad, core cybersecurity concepts, and common threats such as malware.
- General awareness of machine learning fundamentals (no technical expertise necessary).
- Familiarity with networking basics, including IP addressing and TCP/IP protocols.
- Basic Linux/command line skills for navigating and using security tools.
- Interest in leveraging AI for real-time cybersecurity applications.
- No formal prerequisites—certification is awarded based solely on exam 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
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.
Frequently Asked Questions
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