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This program offers comprehensive insights into how AI can transform both offensive and defensive cybersecurity strategies, providing participants with the skills needed to protect digital assets effectively.

Prerequisites

  • Programming Proficiency: Knowledge of languages such as Python, Java, or C++ for scripting and automation purposes.
  • Networking Fundamentals: Understanding of networking protocols, subnetting, firewalls, and routing mechanisms.
  • Operating Systems Knowledge: Familiarity with both Windows and Linux operating systems.
  • Cybersecurity Basics: Awareness of core cybersecurity concepts, including encryption, authentication, access controls, and security protocols.

 
Recertification Requirements
    To maintain the validity of your certification, The Whiteboard, in affiliation with AI CERTs, requires annual recertification. A notification will be sent three months before the due date, guiding candidates through the process outlined in the candidate handbook. Need Assistance? For any questions or support regarding recertification, please contact The Whiteboard's team at info@thewhiteboard.co.in.

Course Modules and Curriculum

Course Introduction Preview
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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)
  • 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
  • 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
  • 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

Tools

Acunetix

Acunetix

Wazuh

Wazuh

Shodan

Shodan

OWASP ZAP

OWASP ZAP

Who Should Enroll?

This certification is ideal for:

  • Cybersecurity Experts & Ethical Hackers
    - Learn how to penetration-test AI systems and prevent security breaches.
  • AI & Machine Learning Engineers
    - Secure AI models from adversarial attacks and vulnerabilities.
  • IT Security Professionals
    - Gain expertise in AI-powered cybersecurity and risk management.
  • Compliance & Risk Officers
    - Ensure AI security aligns with regulatory frameworks and ethical standards.
Key Learning Outcomes
  • Gain expertise in AI security, ethical hacking, and penetration testing.
  • Understand how AI vulnerabilities can be exploited and prevented.
  • Learn to implement AI security protocols to protect against adversarial attacks.
  • Develop hands-on skills in AI risk assessment, compliance, and governance.

Enroll Now

Take the first step towards becoming an AI expert.
Enroll in our AI+ Ethical Hacker™ Course today and unlock the potential of coding to drive your career forward.

Exam Objectives

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AI-Driven Threat Detection

Implementing AI to proactively identify and respond to security threats.

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Automated Vulnerability Assessment

Using AI to conduct comprehensive vulnerability assessments efficiently.

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AI-Enhanced Penetration Testing

Applying AI techniques to simulate attacks and identify system weaknesses.

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Ethical Considerations in AI and Cybersecurity

Understanding the ethical implications of using AI in cybersecurity practices.

Career Opportunities Post-Certification

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Median Salaries

$113,151

AI Salary Icon

With AI Skills

$146,430

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% Difference

29

Product Manager

AI Security Engineer

Design and implement AI-driven security solutions to protect organizational assets.

Data Analyst

AI Penetration Tester

Conduct penetration tests using AI to uncover vulnerabilities in systems and networks.

AI Architect

Threat Intelligence Analyst

Utilize AI to analyze threat data and provide actionable intelligence for proactive defense.

ML Engineer

Vulnerability Assessment Specialist

Employ AI-powered tools to identify, analyze, and prioritize security vulnerabilities.

Join AI Course & Become Future-Ready!

Stay ahead in the AI-driven world. Enroll now to gain in-demand skills, hands-on experience, and industry-recognized certification for a future-ready career.

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Frequently Asked Questions

Participants gain comprehensive insights into AI's role in cybersecurity, learning advanced techniques essential for 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.