This course is ideal for cybersecurity professionals, network engineers, IT managers, and AI enthusiasts aiming to enhance their knowledge of AI-driven security techniques.

• Basic Python Programming: Familiarity with loops, functions, and variables.
• Basic Cybersecurity Knowledge: Understanding of CIA triad and common threats (e.g., malware, phishing).
• Basic Machine Learning Concepts: Awareness of fundamental machine learning concepts, not mandatory.
• Basic Networking: Understanding of IP addressing and TCP/IP protocols.
• Linux/Command Line Skills: Ability to navigate and use the CLI effectively.
• 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).

• Apply AI to automate and enhance security tasks like monitoring, threat detection, and response
• Use Python for security scripting and automation
• Understand core cybersecurity domains (OS, networking, threat analysis) in the AI-augmented context
• Integrate AI models to identify anomalies, phishing, malware, and vulnerabilities
• Build, test, and operationalize AI-based detection and defense workflows
• Execute incident response plans with enhanced AI tools and predictive capabilities
• Use open-source toolsets in support of AI-driven cybersecurity
• Anticipate emerging security challenges involving AI, IoT, cloud, and quantum technologies

1. 1.1 Definition and Scope of Cybersecurity
2. 1.2 Key Cybersecurity Concepts
3. 1.3 CIA Triad (Confidentiality, Integrity, Availability)
4. 1.4 Cybersecurity Frameworks and Standards (NIST, ISO/IEC27001)
5. 1.5 Cyber Security Laws and Regulations (e.g., GDPR, HIPAA)
6. 1.6 Importance of Cybersecurity in Modern Enterprises
7. 1.7 Careers in Cyber Security

1. 2.1 Core OS Functions (Memory Management, Process Management)
2. 2.2 User Accounts and Privileges
3. 2.3 Access Control Mechanisms (ACLs, DAC, MAC)
4. 2.4 OS Security Features and Configurations
5. 2.5 Hardening OS Security (Patching, Disabling
   Unnecessary Services)
6. 2.6 Virtualization and Containerization Security
   Considerations
7. 2.7 Secure Boot and Secure Remote Access
8. 2.8 OS Vulnerabilities and Mitigations

1. 3.1 Network Topologies and Protocols (TCP/IP, OSI Model)
2. 3.2 Network Devices and Their Roles (Routers, Switches,
   Firewalls)
3. 3.3 Network Security Devices (Firewalls, IDS/IPS)
4. 3.4 Network Segmentation and Zoning
5. 3.5 Wireless Network Security (WPA2, Open WEP
   vulnerabilities)
6. 3.6 VPN Technologies and Use Cases
7. 3.7 Network Address Translation (NAT)
8. 3.8 Basic Network Troubleshooting

1. 4.1 Types of Threat Actors (Script Kiddies, Hacktivists, Nation-States)
2. 4.2 Threat Hunting Methodologies using AI
3. 4.3 AI Tools for Threat Hunting (SIEM, IDS/IPS)
4. 4.4 Open-Source Intelligence (OSINT) Techniques
5. 4.5 Introduction to Vulnerabilities
6. 4.6 Software Development Life Cycle (SDLC) and Security Integration with AI
7. 4.7 Zero-Day Attacks and Patch Management Strategies
8. 4.8 Vulnerability Scanning Tools and Techniques using AI
9. 4.9 Exploiting Vulnerabilities (Hands-on Labs)

1. 5.1 An Introduction to AI
2. 5.2 Types and Applications of AI
3. 5.3 Identifying and Mitigating Risks in Real-Life
4. 5.4 Building a Resilient and Adaptive Security Infrastructure with AI
5. 5.5 Enhancing Digital Defenses using CSAI
6. 5.6 Application of Machine Learning in Cybersecurity
7. 5.7 Safeguarding Sensitive Data and Systems Against Diverse Cyber Threats
8. 5.8 Threat Intelligence and Threat Hunting Concepts

1. 6.1 Introduction to Python Programming
2. 6.2 Understanding of Python Libraries
3. 6.3 Python Programming Language for Cybersecurity
   Applications
4. 6.4 AI Scripting for Automation in Cybersecurity Tasks
5. 6.5 Data Analysis and Manipulation Using Python
6. 6.6 Developing Security Tools with Python

1. 7.1 Understanding the Application of Machine Learning in Cybersecurity
2. 7.2 Anomaly Detection to Behavior Analysis
3. 7.3 Dynamic and Proactive Defense using Machine Learning
4. 7.4 Utilizing Machine Learning for Email Threat Detection
5. 7.5 Enhancing Phishing Detection with AI
6. 7.6 Autonomous Identification and Thwarting of Email Threats
7. 7.7 Employing Advanced Algorithms and AI in Malware Threat Detection
8. 7.8 Identifying, Analyzing, and Mitigating Malicious Software
9. 7.9 Enhancing User Authentication with AI Techniques
10. 7.10 Penetration Testing with AI

1. 8.1 Incident Response Process (Identification, Containment, Eradication, Recovery)
2. 8.2 Incident Response Lifecycle
3. 8.3 Preparing an Incident Response Plan
4. 8.4 Detecting and Analyzing Incidents
5. 8.5 Containment, Eradication, and Recovery
6. 8.6 Post-Incident Activities
7. 8.7 Digital Forensics and Evidence Collection
8. 8.8 Disaster Recovery Planning (Backups, Business Continuity)
9. 8.9 Penetration Testing and Vulnerability Assessments
10. 8.10 Legal and Regulatory Considerations of Security Incidents

1. 1. What Are AI Agents
2. 2. Key Capabilities of AI Agents for the Chief AI Officer
3. 3. Applications and Trends of AI Agents for the Chief AI Officer
4. 4. How Does an AI Agent Work
5. 5. Core Characteristics of AI Agents
6. 6. Types of AI Agents

8.1 Project Overview and Objectives 

8.2 Collaborative Work Sessions 

8.3 Presentation Skills Workshop 

8.4 Final Presentations and Constructive Feedback 

8.5 Reflection on Key Takeaways from the Course Experience

1. 9.1 Introduction to Open-Source Security Tools
2. 9.2 Popular Open Source Security Tools
3. 9.3 Benefits and Challenges of Using Open-Source Tools
4. 9.4 Implementing Open Source Solutions in Organizations
5. 9.5 Community Support and Resources
6. 9.6 Network Security Scanning and Vulnerability Detection
7. 9.7 Security Information and Event Management (SIEM) Tools (Open-Source options)
8. 9.8 Open-Source Packet Filtering Firewalls
9. 9.9 Password Hashing and Cracking Tools (Ethical Use)
10. 9.10 Open-Source Forensics Tools

1. 10.1 Emerging Cyber Threats and Trends
2. 10.2 Artificial Intelligence and Machine Learning in
   Cybersecurity
3. 10.3 Blockchain for Security
4. 10.4 Internet of Things (IoT) Security
5. 10.5 Cloud Security
6. 10.6 Quantum Computing and its Impact on Security
7. 10.7 Cybersecurity in Critical Infrastructure
8. 10.8 Cryptography and Secure Hashing
9. 10.9 Cyber Security Awareness and Training for Users
10. 10.10 Continuous Security Monitoring and Improvement

1. 11.1 Introduction
2. 11.2 Use Cases: AI in Cybersecurity
3. 11.3 Outcome Presentation

1. 1. Understanding AI Agents
2. 2. What Are AI Agents
3. 3. Key Capabilities of AI Agents in Cyber Security
4. 4. Applications and Trends for AI Agents in Cyber Security
5. 5. How Does an AI Agent Work
6. 6. Core Characteristics of AI Agents
7. 7. Types of AI Agents