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Overview Prerequisites Self Study Materials Included Tools What You’ll Learn Course Modules FAQs

What’s Included?

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Prerequisites

    • Programming Proficiency: Knowledge of Python, Java, C++, etc for automation and scripting.
    • Networking Fundamentals: Understanding of networking protocols, subnetting, firewalls, and routing.
    • Operating Systems Knowledge: Proficiency in using Windows and Linux operating systems.
    • Cybersecurity Basics: Familiarity with fundamental cybersecurity concepts, including encryption, authentication, access controls, and security protocols.
    • Machine Learning Basics: Understanding of machine learning concepts, algorithms, and basic implementation.
    • Web Technologies: Understanding of web technologies, including HTTP/HTTPS protocols, and web servers.

     

    There are no mandatory prerequisites for certification. Certification is based solely on performance in the examination. However, candidates may choose to prepare through self-study or optional training offered by AI CERTs® Authorized Training Partners (ATPs).

Self Study Materials Included

Videos

Engaging visual content to enhance understanding and learning experience.

Podcasts

Insightful audio sessions featuring expert discussions and real-world cases.

Audiobooks

Listen and learn anytime with convenient audio-based knowledge sharing.

E-Books

Comprehensive digital guides offering in-depth knowledge and learning support.

Module Wise Quizzes

Interactive assessments to reinforce learning and test conceptual clarity.

Additional Resources

Supplementary references and list of tools to deepen knowledge and practical application.

Tools You’ll Master

Acunetix

Acunetix

Wazuh

Wazuh

Shodan

Shodan

OWASP ZAP

OWASP ZAP

What You’ll Learn

AI-Integrated Cybersecurity Techniques

Learners will develop the ability to integrate AI tools and technologies into cybersecurity practices. This includes using AI for ethical hacking tasks such as reconnaissance, vulnerability assessments, penetration testing, and incident response.

Threat Analysis and Anomaly Detection

Students will develop skills in applying machine learning algorithms to detect unusual patterns and behaviors that may indicate potential security threats. This capability is essential for proactively identifying and mitigating risks before they escalate.

AI for Identity and Access Management (IAM)

Learners will understand how to apply AI to enhance IAM systems, crucial for maintaining secure access to resources within an organization. This involves using AI to improve authentication processes and manage user permissions more dynamically and securely.

Automated Security Protocol Optimization

Students will be equipped to utilize AI to dynamically adjust and optimize security protocols based on real-time data analysis and threat assessment. Learners will explore how AI algorithms can predict and respond to potential security breaches by automatically tweaking firewall rules, security configurations, and other protective measures.

Course Modules

Certification Overview
  1. Course Introduction Preview
Module 1: Foundation of Ethical Hacking Using Artificial Intelligence (AI)
  1. 1.1 Introduction to Ethical Hacking
  2. 1.2 Ethical Hacking Methodology
  3. 1.3 Legal and Regulatory Framework
  4. 1.4 Hacker Types and Motivations
  5. 1.5 Information Gathering Techniques
  6. 1.6 Footprinting and Reconnaissance
  7. 1.7 Scanning Networks
  8. 1.8 Enumeration Techniques
Module 2: Introduction to AI in Ethical Hacking
  1. 2.1 AI in Ethical Hacking
  2. 2.2 Fundamentals of AI
  3. 2.3 AI Technologies Overview
  4. 2.4 Machine Learning in Cybersecurity
  5. 2.5 Natural Language Processing (NLP) for Cybersecurity
  6. 2.6 Deep Learning for Threat Detection
  7. 2.7 Adversarial Machine Learning in Cybersecurity
  8. 2.8 AI-Driven Threat Intelligence Platforms
  9. 2.9 Cybersecurity Automation with AI
Module 3: AI Tools and Technologies in Ethical Hacking
  1. 3.1 AI-Based Threat Detection Tools
  2. 3.2 Machine Learning Frameworks for Ethical Hacking
  3. 3.3 AI-Enhanced Penetration Testing Tools
  4. 3.4 Behavioral Analysis Tools for Anomaly Detection
  5. 3.5 AI-Driven Network Security Solutions
  6. 3.6 Automated Vulnerability Scanners
  7. 3.7 AI in Web Application
  8. 3.8 AI for Malware Detection and Analysis
  9. 3.9 Cognitive Security Tools
Module 4: AI-Driven Reconnaissance Techniques
  1. 4.1 Introduction to Reconnaissance in Ethical Hacking
  2. 4.2 Traditional vs. AI-Driven Reconnaissance
  3. 4.3 Automated OS Fingerprinting with AI
  4. 4.4 AI-Enhanced Port Scanning Techniques
  5. 4.5 Machine Learning for Network Mapping
  6. 4.6 AI-Driven Social Engineering Reconnaissance
  7. 4.7 Machine Learning in OSINT
  8. 4.8 AI-Enhanced DNS Enumeration & AI-Driven Target Profiling
Module 5: AI in Vulnerability Assessment and Penetration Testing
  1. 5.1 Automated Vulnerability Scanning with AI
  2. 5.2 AI-Enhanced Penetration Testing Tools
  3. 5.3 Machine Learning for Exploitation Techniques
  4. 5.4 Dynamic Application Security Testing (DAST) with AI
  5. 5.5 AI-Driven Fuzz Testing
  6. 5.6 Adversarial Machine Learning in Penetration Testing
  7. 5.7 Automated Report Generation using AI
  8. 5.8 AI-Based Threat Modeling
  9. 5.9 Challenges and Ethical Considerations in AI-Driven Penetration Testing
Module 6: Machine Learning for Threat Analysis
  1. 6.1 Supervised Learning for Threat Detection
  2. 6.2 Unsupervised Learning for Anomaly Detection
  3. 6.3 Reinforcement Learning for Adaptive Security Measures
  4. 6.4 Natural Language Processing (NLP) for Threat Intelligence
  5. 6.5 Behavioral Analysis using Machine Learning
  6. 6.6 Ensemble Learning for Improved Threat Prediction
  7. 6.7 Feature Engineering in Threat Analysis
  8. 6.8 Machine Learning in Endpoint Security
  9. 6.9 Explainable AI in Threat Analysis
Module 7: Behavioral Analysis and Anomaly Detection for System Hacking
  1. 7.1 Behavioral Biometrics for User Authentication
  2. 7.2 Machine Learning Models for User Behavior Analysis
  3. 7.3 Network Traffic Behavioral Analysis
  4. 7.4 Endpoint Behavioral Monitoring
  5. 7.5 Time Series Analysis for Anomaly Detection
  6. 7.6 Heuristic Approaches to Anomaly Detection
  7. 7.7 AI-Driven Threat Hunting
  8. 7.8 User and Entity Behavior Analytics (UEBA)
  9. 7.9 Challenges and Considerations in Behavioral Analysis
Module 8: AI Enabled Incident Response Systems
  1. 8.1 Automated Threat Triage using AI
  2. 8.2 Machine Learning for Threat Classification
  3. 8.3 Real-time Threat Intelligence Integration
  4. 8.4 Predictive Analytics in Incident Response
  5. 8.5 AI-Driven Incident Forensics
  6. 8.6 Automated Containment and Eradication Strategies
  7. 8.7 Behavioral Analysis in Incident Response
  8. 8.8 Continuous Improvement through Machine Learning Feedback
  9. 8.9 Human-AI Collaboration in Incident Handling
Module 9: AI for Identity and Access Management (IAM)
  1. 9.1 AI-Driven User Authentication Techniques
  2. 9.2 Behavioral Biometrics for Access Control
  3. 9.3 AI-Based Anomaly Detection in IAM
  4. 9.4 Dynamic Access Policies with Machine Learning
  5. 9.5 AI-Enhanced Privileged Access Management (PAM)
  6. 9.6 Continuous Authentication using Machine Learning
  7. 9.7 Automated User Provisioning and De-provisioning
  8. 9.8 Risk-Based Authentication with AI
  9. 9.9 AI in Identity Governance and Administration (IGA)
Module 10: Securing AI Systems
  1. 10.1 Adversarial Attacks on AI Models
  2. 10.2 Secure Model Training Practices
  3. 10.3 Data Privacy in AI Systems
  4. 10.4 Secure Deployment of AI Applications
  5. 10.5 AI Model Explainability and Interpretability
  6. 10.6 Robustness and Resilience in AI
  7. 10.7 Secure Transfer and Sharing of AI Models
  8. 10.8 Continuous Monitoring and Threat Detection for AI
Module 11: Ethics in AI and Cybersecurity
  1. 11.1 Ethical Decision-Making in Cybersecurity
  2. 11.2 Bias and Fairness in AI Algorithms
  3. 11.3 Transparency and Explainability in AI Systems
  4. 11.4 Privacy Concerns in AI-Driven Cybersecurity
  5. 11.5 Accountability and Responsibility in AI Security
  6. 11.6 Ethics of Threat Intelligence Sharing
  7. 11.7 Human Rights and AI in Cybersecurity
  8. 11.8 Regulatory Compliance and Ethical Standards
  9. 11.9 Ethical Hacking and Responsible Disclosure
Module 12: Capstone Project
  1. 12.1 Case Study 1: AI-Enhanced Threat Detection and Response
  2. 12.2 Case Study 2: Ethical Hacking with AI Integration
  3. 12.3 Case Study 3: AI in Identity and Access Management (IAM)
  4. 12.4 Case Study 4: Secure Deployment of AI Systems
Optional Module: AI Agents for Ethical Hacking
  1. 1. Understanding AI Agents
  2. 2. Case Studies
  3. 3. Hands-On Practice with AI Agents

Frequently Asked Questions

Participants gain comprehensive insights into AI's role in cybersecurity, learning advanced techniques that are essential in modern ethical hacking practices. The certification equips learners with cutting-edge skills highly valued in the cybersecurity industry.

This certification is ideal for aspiring ethical hackers and cybersecurity professionals who want to integrate AI technologies into their skill set. It caters to tech enthusiasts looking to stay ahead in the rapidly evolving digital landscape.

Participants will gain hands-on experience in using AI to enhance ethical hacking techniques. Skills include AI-driven reconnaissance, vulnerability assessment, penetration testing, threat analysis, incident response, and identity and access management. Additionally, participants will learn to secure AI systems and address ethical considerations in AI and cybersecurity.

Basic knowledge of cybersecurity principles and familiarity with programming languages such as Python are recommended. Prior experience in ethical hacking or AI is advantageous but not mandatory

Unlike traditional courses, this certification uniquely integrates AI technologies into ethical hacking practices. It focuses on leveraging AI's capabilities to enhance cybersecurity measures, providing a forward-looking approach to digital defense.