Module 1: Introduction to Cybersecurity
- 1.1 Definition and Scope of Cybersecurity
- 1.2 Key Cybersecurity Concepts
- 1.3 CIA Triad (Confidentiality, Integrity, Availability)
- 1.4 Cybersecurity Frameworks and Standards (NIST, ISO/IEC27001)
- 1.5 Cyber Security Laws and Regulations (e.g., GDPR, HIPAA)
- 1.6 Importance of Cybersecurity in Modern Enterprises
- 1.7 Careers in Cyber Security
Module 2: Operating System Fundamentals
- 2.1 Core OS Functions (Memory Management, Process Management)
- 2.2 User Accounts and Privileges
- 2.3 Access Control Mechanisms (ACLs, DAC, MAC)
- 2.4 OS Security Features and Configurations
- 2.5 Hardening OS Security (Patching, Disabling Unnecessary Services)
- 2.6 Virtualization and Containerization Security Considerations
- 2.7 Secure Boot and Secure Remote Access
- 2.8 OS Vulnerabilities and Mitigations
Module 3: Networking Fundamentals
- 3.1 Network Topologies and Protocols (TCP/IP, OSI Model)
- 3.2 Network Devices and Their Roles (Routers, Switches, Firewalls)
- 3.3 Network Security Devices (Firewalls, IDS/IPS)
- 3.4 Network Segmentation and Zoning
- 3.5 Wireless Network Security (WPA2, Open WEP vulnerabilities)
- 3.6 VPN Technologies and Use Cases
- 3.7 Network Address Translation (NAT)
- 3.8 Basic Network Troubleshooting
Module 4: Threats, Vulnerabilities, and Exploits
- 4.1 Types of Threat Actors (Script Kiddies, Hacktivists, Nation-States)
- 4.2 Threat Hunting Methodologies using AI
- 4.3 AI Tools for Threat Hunting (SIEM, IDS/IPS)
- 4.4 Open-Source Intelligence (OSINT) Techniques
- 4.5 Introduction to Vulnerabilities
- 4.6 Software Development Life Cycle (SDLC) and Security Integration with AI
- 4.7 Zero-Day Attacks and Patch Management Strategies
- 4.8 Vulnerability Scanning Tools and Techniques using AI
- 4.9 Exploiting Vulnerabilities (Hands-on Labs)
Module 5: Understanding of AI and ML
- 5.1 An Introduction to AI
- 5.2 Types and Applications of AI
- 5.3 Identifying and Mitigating Risks in Real-Life
- 5.4 Building a Resilient and Adaptive Security Infrastructure with AI
- 5.5 Enhancing Digital Defenses using CSAI
- 5.6 Application of Machine Learning in Cybersecurity
- 5.7 Safeguarding Sensitive Data and Systems Against Diverse Cyber Threats
- 5.8 Threat Intelligence and Threat Hunting Concepts
Module 6: Python Programming Fundamentals
- 6.1 Introduction to Python Programming
- 6.2 Understanding of Python Libraries
- 6.3 Python Programming Language for Cybersecurity Applications
- 6.4 AI Scripting for Automation in Cybersecurity Tasks
- 6.5 Data Analysis and Manipulation Using Python
- 6.6 Developing Security Tools with Python
Module 7: Applications of AI in Cybersecurity
- 7.1 Understanding the Application of Machine Learning in Cybersecurity
- 7.2 Anomaly Detection to Behavior Analysis
- 7.3 Dynamic and Proactive Defense using Machine Learning
- 7.4 Utilizing Machine Learning for Email Threat Detection
- 7.5 Enhancing Phishing Detection with AI
- 7.6 Autonomous Identification and Thwarting of Email Threats
- 7.7 Employing Advanced Algorithms and AI in Malware Threat Detection
- 7.8 Identifying, Analyzing, and Mitigating Malicious Software
- 7.9 Enhancing User Authentication with AI Techniques
- 7.10 Penetration Testing with AI
Module 8: Incident Response and Disaster Recovery
- 8.1 Incident Response Process (Identification, Containment, Eradication, Recovery)
- 8.2 Incident Response Lifecycle
- 8.3 Preparing an Incident Response Plan
- 8.4 Detecting and Analyzing Incidents
- 8.5 Containment, Eradication, and Recovery
- 8.6 Post-Incident Activities
- 8.7 Digital Forensics and Evidence Collection
- 8.8 Disaster Recovery Planning (Backups, Business Continuity)
- 8.9 Penetration Testing and Vulnerability Assessments
- 8.10 Legal and Regulatory Considerations of Security Incidents
Module 9: Open Source Security Tools
- 9.1 Introduction to Open-Source Security Tools
- 9.2 Popular Open Source Security Tools
- 9.3 Benefits and Challenges of Using Open-Source Tools
- 9.4 Implementing Open Source Solutions in Organizations
- 9.5 Community Support and Resources
- 9.6 Network Security Scanning and Vulnerability Detection
- 9.7 Security Information and Event Management (SIEM) Tools (Open-Source options)
- 9.8 Open-Source Packet Filtering Firewalls
- 9.9 Password Hashing and Cracking Tools (Ethical Use)
- 9.10 Open-Source Forensics Tools
Module 10: Securing the Future
- 10.1 Emerging Cyber Threats and Trends
- 10.2 Artificial Intelligence and Machine Learning in Cybersecurity
- 10.3 Blockchain for Security
- 10.4 Internet of Things (IoT) Security
- 10.5 Cloud Security
- 10.6 Quantum Computing and its Impact on Security
- 10.7 Cybersecurity in Critical Infrastructure
- 10.8 Cryptography and Secure Hashing
- 10.9 Cyber Security Awareness and Training for Users
- 10.10 Continuous Security Monitoring and Improvement
Module 11: Capstone Project
- 11.1 Introduction
- 11.2 Use Cases: AI in Cybersecurity
- 11.3 Outcome Presentation
Optional Module: AI Agents for Security Level 1
- 1. Understanding AI Agents
- 2. What Are AI Agents
- 3. Key Capabilities of AI Agents in Cyber Security
- 4. Applications and Trends for AI Agents in Cyber Security
- 5. How Does an AI Agent Work
- 6. Core Characteristics of AI Agents
- 7. Types of AI Agents
Tools you will explore
- CrowdStrike
- Flair.ai
- ChatGPT
- Pluralsight
