AI+ Ethical Hacker™ - eLearning (exam included)

AI+ Ethical Hacker™ - eLearning (exam included)

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.

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

• Stay Ahead of Technological Change: Understand how AI is reshaping cybersecurity, keeping you prepared for emerging threats.
• Integrate AI with Cybersecurity: Develop expertise in applying AI techniques alongside ethical hacking to enhance digital defense strategies.
• Enhance Career Prospects: Prepare for in-demand roles that blend AI proficiency with cybersecurity expertise.
• Practical, Hands-On Learning: Gain experience with AI-driven security tools and techniques to address real-world cyber threats.
• Future-Proof Your Skills: Master AI-powered ethical hacking, establishing yourself as a leader in the evolving digital security landscape.
  
  

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.

Key Features

• Course and material in English 
• Intermediate 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: Acunetix, Wazuh, Shodan, OWASP ZAP

Learning Outcomes

• 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.

Target Audience

• 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.

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

Course Content

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

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 are the key benefits of this certification?
Participants gain a deep understanding of AI’s impact on cybersecurity, mastering advanced techniques essential for modern ethical hacking. The certification provides cutting-edge skills that are highly valued in the cybersecurity field.

Who should consider taking the AI+ Ethical Hacker™ certification?
This certification is designed for aspiring ethical hackers and cybersecurity professionals looking to incorporate AI into their skill set. It is also suitable for tech enthusiasts aiming to stay ahead in the fast-evolving digital landscape.

What practical skills will I gain from this certification?
Learners will acquire hands-on experience applying AI to ethical hacking, including AI-driven reconnaissance, vulnerability assessment, penetration testing, threat analysis, incident response, and identity and access management. They will also learn how to secure AI systems and address ethical considerations in AI and cybersecurity.

What are the prerequisites for enrolling in this course?
A basic understanding of cybersecurity principles and familiarity with programming languages like Python are recommended. Prior experience in ethical hacking or AI is helpful but not required.

How is this course different from traditional ethical hacking courses?
Unlike conventional courses, this certification integrates AI into ethical hacking practices, emphasizing how AI can enhance cybersecurity strategies and offering a forward-looking approach to digital defense.

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

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.