AI+ Security Level 1™

Empowering Cybersecurity with AI

Start your AI security journey with our all-in-one bundle. Explore core concepts in AI-driven protection, vulnerability management, and intelligent threat response.

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

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

Instructor-led OR Self-paced course + Official exam + Digital badge