AI+ Ethical Hacker™

• Ethical Hackers and Penetration Testers seeking to integrate AI in their workflow
• Cybersecurity Analysts and Engineers aiming to upgrade their technical toolkit
• IT Professionals and Network Security Specialists involved in digital defense
• Computer Science Students and Graduates exploring AI in cybersecurity
• Security Architects and Red Team members looking to enhance attack simulations

• Understand how AI enhances ethical hacking practices
• Perform intelligent threat modeling and AI-assisted penetration testing
• Identify and mitigate vulnerabilities using machine learning algorithms
• Implement AI-based anomaly and intrusion detection systems
• Apply advanced tools for automating reconnaissance and security analysis
• Use neural networks and GANs for proactive cybersecurity strategies

• 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).

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

• Core Concepts: An introduction to the foundational principles of ethical hacking and a high-level overview of how AI and machine learning can be integrated into the process.
• Legal and Ethical Framework: A critical discussion of the legal and ethical responsibilities of ethical hackers, with a focus on the unique challenges and considerations when using AI.

Module 2: Introduction to AI in Ethical Hacking

• The Synergy of AI and Hacking: Understand how AI and ethical hacking form a powerful alliance, with AI providing speed, automation, and predictive analytics, while human insight and creativity are still essential.
• AI vs. Ethical Hacking: A look at the different roles each plays and why a combined approach is more effective than either alone.

Module 3: AI Tools and Technologies in Ethical Hacking

• AI-Based Threat Detection Tools: An overview of AI-powered tools that help identify and prioritize threats.
• Machine Learning Frameworks: An introduction to popular machine learning frameworks like TensorFlow and PyTorch and how they can be applied to cybersecurity tasks.
• AI-Enhanced Penetration Testing Tools: A survey of tools that use AI to automate parts of the penetration testing process.

Module 4: AI-Driven Reconnaissance Techniques

• Automated OSINT (Open-Source Intelligence): Learn how AI-powered tools can automatically gather and analyze vast amounts of publicly available information from social media, news, and the dark web to build a comprehensive profile of a target.
• AI-Enhanced Port Scanning: A deep dive into how AI can be used to perform more intelligent and efficient network scans, identifying open ports and services with greater accuracy.
• Machine Learning for Network Mapping: Understand how AI can analyze network traffic to automatically map an organization's infrastructure and identify potential entry points for an attack.

Module 5: AI in Vulnerability Assessment and Penetration Testing

• AI-Driven Vulnerability Scanning: Learn how AI can perform more advanced vulnerability scans that can prioritize findings based on real-world exploitability, reducing alert fatigue.
• AI-Powered Penetration Testing: A practical look at how AI can simulate cyberattacks at a faster pace, identify potential attack vectors more accurately, and generate automated reports.
• Adversarial Machine Learning: Explore how attackers can use adversarial AI to evade security measures, and how ethical hackers can use the same techniques to test the resilience of a system.

Module 6: Machine Learning for Threat Analysis

• Supervised and Unsupervised Learning: Learn how to apply supervised learning for known threat detection and unsupervised learning for identifying new and unknown threats (zero-day attacks).
• Behavioral Analysis: Understand how machine learning models can be used to establish a baseline of "normal" user and network behavior and flag any anomalies as potential security threats.
• Predictive Threat Hunting: Explore how AI can be used to predict where new threats are likely to emerge and proactively hunt for them before they cause damage.

Module 7: Behavioral Analysis and Anomaly Detection for System Hacking

• User Behavior Analytics (UBA): Learn how to use AI to analyze user behavior patterns to identify insider threats and compromised accounts.
• Network Anomaly Detection: A deep dive into how AI can be used to detect unusual patterns in network traffic, such as unauthorized data exfiltration or botnet activity.

Module 8: AI Enabled Incident Response Systems

• Automated Incident Triage: Learn how AI can automatically classify and prioritize security alerts, reducing the workload on security teams.
• AI-Assisted Forensics: Explore how AI can be used to analyze large volumes of log data and other forensic evidence to assist in a faster and more accurate incident investigation.

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

• Behavioral Biometrics: Understand how AI can analyze unique user behaviors (e.g., typing speed, mouse movements) to provide continuous and secure authentication.
• Dynamic Access Policies: Learn how AI can be used to automatically adjust user access rights based on real-time risk assessments, enforcing the principle of least privilege.

Module 10: Securing AI Systems

• AI-Specific Vulnerabilities: A deep dive into the unique vulnerabilities of AI systems, such as data poisoning, prompt injection attacks, and model inversion.
• Adversarial Training: Learn how to use adversarial training to make AI models more resilient to attacks.
• Securing the AI Lifecycle: Best practices for securing the entire AI development lifecycle, from data collection to model deployment.

Module 11: Ethics in AI and Cybersecurity

• Bias and Fairness in AI: A critical discussion on how biases in training data can lead to discriminatory outcomes in AI systems and how to mitigate them.
• Accountability and Responsibility: Understand the legal and ethical responsibilities of individuals and organizations when using AI in cybersecurity.
• Regulatory Compliance: A look at the evolving regulatory landscape for AI and cybersecurity.

Module 12: Capstone Project

• Real-World Hacking Scenario: A hands-on project where you will design and implement an AI-driven ethical hacking solution to solve a real-world cybersecurity problem, applying the knowledge and skills from all previous modules.

Optional Module: AI Agents for Ethical Hacking

• What Are AI Agents: A deep dive into the concept of autonomous AI systems that can perceive their environment, plan, and perform a series of actions to achieve a goal.
• Key Capabilities of AI Agents: Explore how AI agents are being developed to automate complex, multi-step hacking tasks, from reconnaissance to post-exploitation.
• Applications and Trends: A look at the real-world applications of AI agents in ethical hacking and a discussion of the future of human-AI collaboration in the field.