AI+ Ethical Hacker (AF-1601)

Certification Overview

• Course Introduction

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

• Introduction to Ethical Hacking

• Ethical Hacking Methodology

• Legal and Regulatory Framework

• Hacker Types and Motivations

• Information Gathering Techniques

• Footprinting and Reconnaissance

• Scanning Networks

• Enumeration Techniques

Module 2: Introduction to AI in Ethical Hacking

• AI in Ethical Hacking

• Fundamentals of AI

• AI Technologies Overview

• Machine Learning in Cybersecurity

• Natural Language Processing (NLP) for Cybersecurity

• Deep Learning for Threat Detection

• Adversarial Machine Learning in Cybersecurity

• AI-Driven Threat Intelligence Platforms

• Cybersecurity Automation with AI

Module 3: AI Tools and Technologies in Ethical Hacking

• AI-Based Threat Detection Tools

• Machine Learning Frameworks for Ethical Hacking

• AI-Enhanced Penetration Testing Tools

• Behavioral Analysis Tools for Anomaly Detection

• AI-Driven Network Security Solutions

• Automated Vulnerability Scanners

• AI in Web Application Security

• AI for Malware Detection and Analysis

• Cognitive Security Tools

Module 4: AI-Driven Reconnaissance Techniques

• Introduction to Reconnaissance in Ethical Hacking

• Traditional vs. AI-Driven Reconnaissance

• Automated OS Fingerprinting with AI

• AI-Enhanced Port Scanning Techniques

• Machine Learning for Network Mapping

• AI-Driven Social Engineering Reconnaissance

• Machine Learning in OSINT

• AI-Enhanced DNS Enumeration & AI-Driven Target Profiling

Module 5: AI in Vulnerability Assessment and Penetration Testing

• Automated Vulnerability Scanning with AI

• AI-Enhanced Penetration Testing Tools

• Machine Learning for Exploitation Techniques

• Dynamic Application Security Testing (DAST) with AI

• AI-Driven Fuzz Testing

• Adversarial Machine Learning in Penetration Testing

• Automated Report Generation using AI

• AI-Based Threat Modeling

• Challenges and Ethical Considerations in AI-Driven Penetration Testing

Module 6: Machine Learning for Threat Analysis

• Supervised Learning for Threat Detection

• Unsupervised Learning for Anomaly Detection

• Reinforcement Learning for Adaptive Security Measures

• Natural Language Processing (NLP) for Threat Intelligence

• Behavioral Analysis using Machine Learning

• Ensemble Learning for Improved Threat Prediction

• Feature Engineering in Threat Analysis

• Machine Learning in Endpoint Security

• Explainable AI in Threat Analysis

Module 7: Behavioral Analysis and Anomaly Detection for System Hacking

• Behavioral Biometrics for User Authentication

• Machine Learning Models for User Behavior Analysis

• Network Traffic Behavioral Analysis

• Endpoint Behavioral Monitoring

• Time Series Analysis for Anomaly Detection

• Heuristic Approaches to Anomaly Detection

• AI-Driven Threat Hunting

• User and Entity Behavior Analytics (UEBA)

• Challenges and Considerations in Behavioral Analysis

Module 8: AI-Enabled Incident Response Systems

• Automated Threat Triage using AI

• Machine Learning for Threat Classification

• Real-time Threat Intelligence Integration

• Predictive Analytics in Incident Response

• AI-Driven Incident Forensics

• Automated Containment and Eradication Strategies

• Behavioral Analysis in Incident Response

• Continuous Improvement through Machine Learning Feedback

• Human-AI Collaboration in Incident Handling

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

• AI-Driven User Authentication Techniques

• Behavioral Biometrics for Access Control

• AI-Based Anomaly Detection in IAM

• Dynamic Access Policies with Machine Learning

• AI-Enhanced Privileged Access Management (PAM)

• Continuous Authentication using Machine Learning

• Automated User Provisioning and De-provisioning

• Risk-Based Authentication with AI

• AI in Identity Governance and Administration (IGA)

Module 10: Securing AI Systems

• Adversarial Attacks on AI Models

• Secure Model Training Practices

• Data Privacy in AI Systems

• Secure Deployment of AI Applications

• AI Model Explainability and Interpretability

• Robustness and Resilience in AI

• Secure Transfer and Sharing of AI Models

• Continuous Monitoring and Threat Detection for AI

Module 11: Ethics in AI and Cybersecurity

• Ethical Decision-Making in Cybersecurity

• Bias and Fairness in AI Algorithms

• Transparency and Explainability in AI Systems

• Privacy Concerns in AI-Driven Cybersecurity

• Accountability and Responsibility in AI Security

• Ethics of Threat Intelligence Sharing

• Human Rights and AI in Cybersecurity

• Regulatory Compliance and Ethical Standards

• Ethical Hacking and Responsible Disclosure

Module 12: Capstone Project

• Case Study 1: AI-Enhanced Threat Detection and Response

• Case Study 2: Ethical Hacking with AI Integration

• Case Study 3: AI in Identity and Access Management (IAM)

• Case Study 4: Secure Deployment of AI Systems

Optional Module: AI Agents for Ethical Hacking

• Understanding AI Agents

• Case Studies

• Hands-On Practice with AI Agents