AI+ Ethical Hacker™

Course Overview

Objectives

• Demonstrate advanced competency in AI-powered cybersecurity architectures and security controls
• Apply deep learning algorithms to modern cybersecurity operations and threat detection
• Design and implement AI-driven network security strategies for enterprise environments
• Strengthen endpoint protection using AI-based monitoring, detection, and response solutions
• Analyze complex cyber threats and support strategic cyber defense decision-making
• Evaluate enterprise-level security challenges through advanced scenario-based assessments
• Implement AI-enabled threat intelligence and proactive security operations
• Develop governance, risk management, and security leadership capabilities
• Enhance IoT security using AI-powered automation and intelligent protection mechanisms
• Validate readiness for senior cybersecurity leadership and CISO-level responsibilities

Course Outline

Module 1: Foundation of Ethical Hacking Using Artificial Intelligence (AI) (5%)

• 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 (9%)

• 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 (9%)

• 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 Security
• 3.8 AI for Malware Detection and Analysis
• 3.9 Cognitive Security Tools

Module 4: AI-Driven Reconnaissance Techniques (9%)

• 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 (9%)

• 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 Security Testing

Module 6: Machine Learning for Threat Analysis (9%)

• 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 (9%)

• 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 (9%)

• 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%)

• 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 (9%)

• 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 (9%)

• 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 (5%)

• 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