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AI+ Ethical Hacker™

Protect Digital Landscapes: Harness AI-Enhanced Technologies

The AI+ Ethical Hacker certification delves into the intersection of cybersecurity and artificial intelligence, a pivotal juncture in our era of rapid technological progress. Tailored for budding ethical hackers and cybersecurity experts, it offers comprehensive insights into AI's transformative impact on digital offense and defense strategies. Unlike conventional ethical hacking courses, this program harnesses AI's power to enhance cybersecurity approaches. It caters to tech enthusiasts eager to master the fusion of cutting-edge AI methods with ethical hacking practices amidst the swiftly evolving digital landscape. The curriculum encompasses four key areas, from course objectives and prerequisites to anticipated job roles and the latest AI technologies in Ethical Hacking.

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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.
  • Cybersecurity Basics: Familiarity with fundamental cybersecurity concepts, including encryption, authentication, access controls, and security protocols
  • Operating Systems Knowledge: Proficiency in using Windows and Linux operating systems.
  • 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.

Modules

12

Examination

1

50 MCQs

90 Minutes

Passing Score

70%

Certification Modules

  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

Certification Modules

  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

What Will You Learn?

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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.

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Threat Analysis and Anomaly Detection

Students will gain skills in applying machine learning algorithms to detect unusual patterns and behaviors that indicate potential security threats. This skill is crucial for preemptively identifying and mitigating risks before.

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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.

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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.

Industry Opportunities after Course Completion

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Median Salaries

$113,151
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With AI skills

$146,430
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% difference

29
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Cybersecurity Analyst

Analyzes cyber threats using AI tools, monitors security systems, and recommends solutions to enhance network safety.

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Penetration Tester

Uses AI to identify and exploit vulnerabilities in systems, ensuring robustness against potential cyberattacks.

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Security Researcher

Investigates AI-driven security solutions, explores emerging threats, and develops innovative cybersecurity defense mechanisms.

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Vulnerability Assessment Specialist

Specializes in AI-powered assessments to identify, analyze, and prioritize security vulnerabilities in technology infrastructures.

Hear it from the Learners

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Marc H

Happy to share I've completed the AI+ Executive Certification from AI CERTs! This program has sharpened my skills in strategic AI application + implementation, further equipping me to lead AI-driven organizational transformation.

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Georgia L

As VP Operations, my recent completion of the AI+ Executive exam through AI CERTs was a pivotal step in advancing my AI skill set as we embrace an AI-driven future. This certification not only deepened my understanding of AI's broad impact across various divisions but also equipped me with the tools to make informed, strategic decisions.

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Antonio C

AI+ Executive™ Instructor Guide Certificate. Today, I am part of the team of #CompuEducación instructors to teach the #AI CERTs AI+Executive certification course . This 8-hour course is a new standard for business leaders who want to start a solid path in the adoption of AI for the transformation of their companies. The technological, business, ethical, legal and strategy foundations are covered. The examples of using “AI” are practical, up-to-date, and touch on the different variants of “AI.”

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Doug F

Excited to successfully complete AI Cert's AI+ Marketing certification course! For us marketers, it's imperative to embrace AI and take an active effort in learning how to harness its capabilities to stay relevant and be on the cutting edge of tech.

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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 program 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 program 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.

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