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AI+ Ethical Hacker™ - eLearning (exam included)
AI & Machine Learning
Cyber Security

AI+ Ethical Hacker™ - eLearning (exam included)

448,00 EUR

  • 40 hours
eLearning

Secure Digital Environments: Leverage AI-Powered Technologies The AI+ Ethical Hacker Certification prepares cybersecurity professionals and ethical hackers to protect the rapidly evolving digital environment. This program provides a comprehensive study of ethical hacking practices combined with advanced Artificial Intelligence (AI) technologies, demonstrating how AI is transforming both offensive and defensive cybersecurity strategies. Participants will explore the legal and ethical principles of ethical hacking, master essential techniques, and develop critical skills.

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

Acunetix, Wazuh, Shodan, OWASP ZAP

Hero

About the course

The certification emphasizes AI-driven threat analysis, utilizing tools like Machine Learning (ML), Natural Language Processing (NLP), and Deep Learning (DL) to strengthen cybersecurity. Through a mix of theoretical learning and hands-on exercises, learners apply AI-enhanced methods to real-world scenarios. Beyond technology training, this certification equips participants for the future of cybersecurity, where AI plays a pivotal role in proactive defense and rapid response. Interactive modules and case studies help build a comprehensive skill set, enabling learners to address modern cyber threats with innovative AI solutions.


Why This Certification Matters

Understand how AI is reshaping cybersecurity, keeping you prepared for emerging threats.

AI ethical hacker

Learning Outcomes

At the end of this course, you will be able to:

AI-Enhanced Cybersecurity Techniques

Learners will gain the ability to incorporate AI tools and technologies into cybersecurity workflows, including tasks such as ethical hacking, reconnaissance, vulnerability assessments, penetration testing, and incident response.

Threat Detection and Anomaly Analysis

Students will learn to apply machine learning algorithms to identify unusual patterns and behaviors, enabling proactive detection and mitigation of potential security threats.

AI for Identity and Access Management (IAM)

Learners will understand how to leverage AI to strengthen IAM systems, enhancing authentication processes and managing user permissions more securely and dynamically.

Automated Security Protocol Optimization

Students will acquire skills to use AI for dynamically adjusting and optimizing security protocols based on real-time threat analysis, including predictive adjustments to firewalls, configurations, and other security measures.

Course timeline

Hero

  1. Foundation of Ethical Hacking Using Artificial Intelligence (AI)

    Lesson 1

    • 1.1 Introduction to Ethical Hacking
    • 1.2 Ethical Hacking Methodology
    • 1.3 Legal and Regulatory Framework
    • 1.4 Hacker Types and Motivations
    • 1.5 Information Gathering Techniques
    • 1.6 Footprinting and Reconnaissance
    • 1.7 Scanning Networks
    • 1.8 Enumeration Techniques

  2. Introduction to AI in Ethical Hacking

    Lesson 2

    • 2.1 AI in Ethical Hacking
    • 2.2 Fundamentals of AI
    • 2.3 AI Technologies Overview
    • 2.4 Machine Learning in Cybersecurity
    • 2.5 Natural Language Processing (NLP) for Cybersecurity
    • 2.6 Deep Learning for Threat Detection
    • 2.7 Adversarial Machine Learning in Cybersecurity
    • 2.8 AI-Driven Threat Intelligence Platforms
    • 2.9 Cybersecurity Automation with AI

  3. AI Tools and Technologies in Ethical Hacking

    Lesson 3

    • 3.1 AI-Based Threat Detection Tools
    • 3.2 Machine Learning Frameworks for Ethical Hacking
    • 3.3 AI-Enhanced Penetration Testing Tools
    • 3.4 Behavioral Analysis Tools for Anomaly Detection
    • 3.5 AI-Driven Network Security Solutions
    • 3.6 Automated Vulnerability Scanners
    • 3.7 AI in Web Application
    • 3.8 AI for Malware Detection and Analysis
    • 3.9 Cognitive Security Tools

  4. AI-Driven Reconnaissance Techniques

    Lesson 4

    • 4.1 Introduction to Reconnaissance in Ethical Hacking
    • 4.2 Traditional vs. AI-Driven Reconnaissance
    • 4.3 Automated OS Fingerprinting with AI
    • 4.4 AI-Enhanced Port Scanning Techniques
    • 4.5 Machine Learning for Network Mapping
    • 4.6 AI-Driven Social Engineering Reconnaissance
    • 4.7 Machine Learning in OSINT
    • 4.8 AI-Enhanced DNS Enumeration & AI-Driven Target Profiling

  5. AI in Vulnerability Assessment and Penetration Testing

    Lesson 5

    • 5.1 Automated Vulnerability Scanning with AI
    • 5.2 AI-Enhanced Penetration Testing Tools
    • 5.3 Machine Learning for Exploitation Techniques
    • 5.4 Dynamic Application Security Testing (DAST) with AI
    • 5.5 AI-Driven Fuzz Testing
    • 5.6 Adversarial Machine Learning in Penetration Testing
    • 5.7 Automated Report Generation using AI
    • 5.8 AI-Based Threat Modeling
    • 5.9 Challenges and Ethical Considerations in AI-Driven Penetration Testing

  6. Machine Learning for Threat Analysis

    Lesson 6

    • 6.1 Supervised Learning for Threat Detection
    • 6.2 Unsupervised Learning for Anomaly Detection
    • 6.3 Reinforcement Learning for Adaptive Security Measures
    • 6.4 Natural Language Processing (NLP) for Threat Intelligence
    • 6.5 Behavioral Analysis using Machine Learning
    • 6.6 Ensemble Learning for Improved Threat Prediction
    • 6.7 Feature Engineering in Threat Analysis
    • 6.8 Machine Learning in Endpoint Security
    • 6.9 Explainable AI in Threat Analysis

  7. Behavioral Analysis and Anomaly Detection for System Hacking

    Lesson 7

    • 7.1 Behavioral Biometrics for User Authentication
    • 7.2 Machine Learning Models for User Behavior Analysis
    • 7.3 Network Traffic Behavioral Analysis
    • 7.4 Endpoint Behavioral Monitoring
    • 7.5 Time Series Analysis for Anomaly Detection
    • 7.6 Heuristic Approaches to Anomaly Detection
    • 7.7 AI-Driven Threat Hunting
    • 7.8 User and Entity Behavior Analytics (UEBA)
    • 7.9 Challenges and Considerations in Behavioral Analysis

  8. AI Enabled Incident Response Systems

    Lesson 8

    • 8.1 Automated Threat Triage using AI
    • 8.2 Machine Learning for Threat Classification
    • 8.3 Real-time Threat Intelligence Integration
    • 8.4 Predictive Analytics in Incident Response
    • 8.5 AI-Driven Incident Forensics
    • 8.6 Automated Containment and Eradication Strategies
    • 8.7 Behavioral Analysis in Incident Response
    • 8.8 Continuous Improvement through Machine Learning Feedback
    • 8.9 Human-AI Collaboration in Incident Handling

  9. AI for Identity and Access Management (IAM)

    Lesson 9

    • 9.1 AI-Driven User Authentication Techniques
    • 9.2 Behavioral Biometrics for Access Control
    • 9.3 AI-Based Anomaly Detection in IAM
    • 9.4 Dynamic Access Policies with Machine Learning
    • 9.5 AI-Enhanced Privileged Access Management (PAM)
    • 9.6 Continuous Authentication using Machine Learning
    • 9.7 Automated User Provisioning and De-provisioning
    • 9.8 Risk-Based Authentication with AI
    • 9.9 AI in Identity Governance and Administration (IGA)

  10. Securing AI Systems

    Lesson 10

    • 10.1 Adversarial Attacks on AI Models
    • 10.2 Secure Model Training Practices
    • 10.3 Data Privacy in AI Systems
    • 10.4 Secure Deployment of AI Applications
    • 10.5 AI Model Explainability and Interpretability
    • 10.6 Robustness and Resilience in AI
    • 10.7 Secure Transfer and Sharing of AI Models
    • 10.8 Continuous Monitoring and Threat Detection for AI

  11. Ethics in AI and Cybersecurity

    Lesson 11

    • 11.1 Ethical Decision-Making in Cybersecurity
    • 11.2 Bias and Fairness in AI Algorithms
    • 11.3 Transparency and Explainability in AI Systems
    • 11.4 Privacy Concerns in AI-Driven Cybersecurity
    • 11.5 Accountability and Responsibility in AI Security
    • 11.6 Ethics of Threat Intelligence Sharing
    • 11.7 Human Rights and AI in Cybersecurity
    • 11.8 Regulatory Compliance and Ethical Standards
    • 11.9 Ethical Hacking and Responsible Disclosure

  12. Capstone Project

    Lesson 12

    • 12.1 Case Study 1: AI-Enhanced Threat Detection and Response
    • 12.2 Case Study 2: Ethical Hacking with AI Integration
    • 12.3 Case Study 3: AI in Identity and Access Management (IAM)
    • 12.4 Case Study 4: Secure Deployment of AI Systems

  13. AI Agents for Ethical Hacking

    Optional Module

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

Industry Growth

Rising Demand for AI Ethical Hackers

  • With AI becoming deeply embedded in vital industries, the need for ethical hackers skilled in AI security is growing rapidly.
  • Cyberattacks against AI-driven systems are evolving fast, creating an urgent demand for specialists who can safeguard these technologies.
  • Emerging focus areas include AI-based penetration testing, defending against adversarial AI attacks, preventing AI-related fraud, and enhancing AI-powered security monitoring.
  • As AI advancements outpace security expertise, professionals in AI Ethical Hacking are positioned as highly sought-after experts in the cybersecurity field.
AI ethical hacker

Who Should Enroll in this Program?

Cybersecurity Professionals: Individuals seeking to strengthen their expertise in proactive defense and AI-enhanced threat detection.

Ethical Hackers: Those aiming to master advanced hacking techniques and stay ahead of emerging cyber threats.

Technology Leaders and Decision Makers: Executives and managers wanting to understand how AI and ethical hacking can safeguard their organizations.

Aspiring Students: Learners pursuing a career in cybersecurity, gaining foundational knowledge and hands-on skills in ethical hacking.

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More Details

Prerequisites

  • Programming Skills: Familiarity with languages such as Python, Java, or C++ for automation and scripting.
  • Networking Knowledge: Understanding of protocols, subnetting, firewalls, and routing concepts.
  • Operating Systems: Proficiency with Windows and Linux environments.
  • Cybersecurity Fundamentals: Basic knowledge of encryption, authentication, access control, and security protocols.
  • Machine Learning Basics: Understanding of core machine learning concepts, algorithms, and implementations.
  • Web Technologies: Familiarity with web protocols (HTTP/HTTPS) and web server fundamentals.
  • Certification Note: No mandatory prerequisites — certification is granted 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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