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AI+ Ethical Hacker™

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  • Protect Digital Landscapes: Harness AI-Enhanced Technologies.
  • Assess your skills before enrolling
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$144.99

Expert
AI+ Ethical Hacker.
40h
1 Year.
$144.99
Training 5 or more people?
Get a Quote

Protect Digital Landscapes: Harness AI-Enhanced Technologies.

  • Tailored for budding ethical hackers and cybersecurity experts, it offers comprehensive insights into AI’s transformative impact on digital offense and defense strategies.
  • Unlike conventional ethical hacking courses, this program harnesses AI’s power to enhance cybersecurity approaches.
  • It caters to tech enthusiasts eager to master the fusion of cutting-edge AI methods with ethical hacking practices amidst the swiftly evolving digital landscape.
  • The curriculum encompasses four key areas, from course objectives and prerequisites to anticipated job roles and the latest AI technologies in Ethical Hacking.
  • Stay Ahead of Technological Advancements: Learn how AI is transforming cybersecurity, enabling you to stay at the forefront of evolving threats.
  • Boost Career Opportunities: This certification prepares you for high-demand roles at the intersection of AI and cybersecurity.
  • Future-Proof Your Skills: Master AI-powered ethical hacking, positioning yourself as an expert in a rapidly advancing digital landscape.
  • Bridge the Gap Between AI and Cybersecurity: Gain expertise in combining AI techniques with ethical hacking to improve digital defense strategies.
  • Hands-on Approach: Learn practical applications of AI-driven security methods, ensuring you’re equipped to tackle real-world cyber threats.

Course Curriculum

Module 1: Foundation of Ethical Hacking Using Artificial Intelligence (AI)
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

Module 2: Introduction to AI in Ethical Hacking
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

Module 3: AI Tools and Technologies in Ethical Hacking
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

Module 4: AI-Driven Reconnaissance Techniques
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

Module 5: AI in Vulnerability Assessment and Penetration Testing
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

Module 6: Machine Learning for Threat Analysis
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

Module 7: Behavioral Analysis and Anomaly Detection for System Hacking
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

Module 8: AI Enabled Incident Response Systems
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

Module 9: AI for Identity and Access Management (IAM)
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)

Module 10: Securing AI Systems
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

Module 11: Ethics in AI and Cybersecurity
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

Module 12: Capstone Project
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

Optional Module: AI Agents for Ethical Hacking
1. Understanding AI Agents 2. Case Studies 3. Hands-On Practice with AI Agents

Learning Objectives

  • AI Fundamentals and Applications: Learn how AI is transforming industries and business practices.
  • Prompt Engineering Skills: Develop the ability to interact effectively with AI systems.
  • AI Strategies in Legal Practices: Understand how AI optimizes workflows and operations in the legal field.
  • Ethics and Responsibility in AI: Grasp the challenges of bias, fairness, and ethical AI usage.
  • Hands-on Activities and Case Studies: Apply AI concepts through practical exercises and real-world scenarios.
  • Career Preparation in AI: Equip yourself with skills to thrive in an AI-driven professional environment.

Resource Center

  • Videos, Use cases, Workshops, E-books and tools.

Target Audience

  • Aspiring ethical hackers looking to combine AI with cybersecurity.
  • Cybersecurity professionals who want to enhance skills with AI.
  • Tech enthusiasts eager to stay ahead in modern digital security challenges.

Job Roles & Industry Outlook 

Cybersecurity Analyst
Analyzes cyber threats using Al tools, monitors security systems, and recommends solutions to enhance network safety.
Penetration Tester
Uses Al to identify and exploit vulnerabilities in systems, ensuring robustness against potential cyberattacks.
Security Researcher
Investigates Al-driven security solutions, explores emerging threats, and develops innovative cybersecurity defense mechanisms.
Vulnerability Assessment Specialist
Specializes in Al-powered assessments to identify, analyze, and prioritize security vulnerabilities in technology infrastructures.

Exam Information

Duration (minutes)
90 minutes
Passing Percentage
70% (35/50)
Format
50 multiple-choice/multiple-response questions
Delivery Method
Online via proctored exam platform (flexible scheduling)
Fees include
One year of access to course content, including continuous updates. Certification exam. One exam re-take.

Exam Blueprint:

Fundamentals of Ethical Hacking & AI Basics 6%
AI Concepts for Cybersecurity 8%
AI-Powered Reconnaissance & Information Gathering 10%
Vulnerability Assessment using AI 14%
AI-Driven Penetration Testing Techniques 15%
Machine Learning for Threat Detection 12%
Behavioral Analysis & Anomaly Detection 9%
AI-Enabled Incident Response & SOC Automation 10%
Securing AI Models & Systems 8%
Ethics, Legal & Compliance in AI Security 8%

Explore our Schedules

Nov 8 - Nov 12
GMT 09:00 AM - 05:00 PM
In-person
Jeddah
880.5$
5 Days
Enroll Now
Sep 6 - Sep 10
GMT 09:00 AM - 05:00 PM
In-person
Kuwait
880.5$
5 Days
Enroll Now

Certificate of Completion

AI+ Ethical Hacker.

$144.99

Expert
AI+ Ethical Hacker.
40h
1 Year.
$144.99
Training 5 or more people?
Get a Quote

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