AI+ Security Level 1™ 

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Cybersecurity Professionals: Enhance your skills by learning AI-driven methods for advanced threat detection and security measures.

Network Engineers: Gain expertise in integrating AI to improve network defense, threat analysis, and anomaly detection.

IT Managers: Equip yourself with the knowledge to manage AI-driven security solutions for your organization’s protection and risk management.

AI Enthusiasts: Explore the intersection of AI and cybersecurity, learning how AI technologies are transforming digital security landscapes.

Security Analysts: Deepen your understanding of AI-powered tools to identify and mitigate complex cybersecurity risks in modern infrastructures.

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Module 1: Introduction to Cybersecurity

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

Module 2: Operating System Fundamentals

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

Module 3: Networking Fundamentals

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

Module 4: Threats, Vulnerabilities, and Exploits

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)

Module 5: Understanding of AI and ML

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

Module 6: Python Programming Fundamentals

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

Module 7: Applications of AI in Cybersecurity

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

Module 8: Incident Response and Disaster Recovery

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

Module 9: Open Source Security Tools

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

Module 10: Securing the Future

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

Module 11: Capstone Project

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

Optional Module: AI Agents for Security Level 1

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

Certifikace

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50 questions, 70% passing, 90 minutes, online proctored exam