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.

• 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).

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.

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

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

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

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

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

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

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

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

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)

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

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

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

1. 1. Understanding AI Agents
2. 2. Case Studies
3. 3. Hands-On Practice with AI Agents