AI+ Security Level 1™
Instructor
Falandile Ntshangase
Course details
Lectures
:
96
- Description
- FAQ
- Reviews
Why This Certification Matters
Comprehensive Learning
Explore AI and cybersecurity integration through Python, machine learning, and threat mitigation to build a strong technical foundation.
Hands-on Approach
Apply concepts in a Capstone Project, solving real-world cybersecurity challenges by leveraging AI tools and practical problem-solving skills.
Cutting-Edge Knowledge
Dive into advanced topics like AI-based authentication and GANs to understand next-gen cybersecurity strategies and innovations.
Boost Strategic Decision-Making with AI Analytics
Master AI models to analyze business data, predict outcomes, and enable more informed, real-time decisions that enhance competitive advantage.
AI-Driven Threat Detection
Learn to detect malware, phishing, and anomalies using machine learning, enhancing your ability to predict and prevent attacks.
Industry Relevance
Stay ahead in cybersecurity by mastering AI applications, making you a valuable asset for future-focused security roles and organizations.
Prerequisites
- 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).
Exam Policies & Integrity
Before your exam, you must accept the AI CERTs® Candidate Agreement. It ensures fairness, transparency, and unbiased certification for all candidates.
Please review it in advance to understand our exam standards and your responsibilities.
Modules
11
Examination
1
50 MCQs
90 Minutes
Passing Score
70% (35/50)
Recertification Requirements
AI CERTs requires recertification every year to keep your certification valid. Notifications will be sent three months before the due date, and candidates must follow the steps in the candidate handbook to complete the process.
Need Help? If you have any questions or need assistance with recertification, please reach out to our support team at support@aicerts.ai
What is AI+ Security Level 1™ certification?
The AI+ Security Level 1™ certification is a foundational course focusing on AI-powered security solutions, including threat detection, automated response, and incident management.
Who should enroll in this course?
This course is ideal for cybersecurity professionals, network engineers, IT managers, and AI enthusiasts aiming to enhance their knowledge of AI-driven security techniques.
What topics will the course cover?
You will learn about AI-based threat detection, machine learning for security automation, AI-driven incident response, and compliance with standards like GDPR, HIPAA, and NIST.
What resources will I get access to?
You’ll receive course materials, case studies, project guidance, and access to an online community of learners.
Is this certification recognized in the industry?
Yes, AI+ Security Level 1™ certification is widely recognized as a benchmark for foundational knowledge in AI-powered security solutions.
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Module 1: Introduction to Cybersecurity
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1
1.1 Definition and Scope of Cybersecurity -
2
1.2 Key Cybersecurity Concepts
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3
1.3 CIA Triad (Confidentiality, Integrity, Availability)
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4
1.4 Cybersecurity Frameworks and Standards (NIST, ISO/IEC27001)
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5
1.5 Cyber Security Laws and Regulations (e.g., GDPR, HIPAA)
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6
1.6 Importance of Cybersecurity in Modern Enterprises
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7
1.7 Careers in Cyber Security
Module 2: Operating System Fundamentals
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8
2.1 Core OS Functions (Memory Management, Process Management)
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9
2.2 User Accounts and Privileges
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10
2.3 Access Control Mechanisms (ACLs, DAC, MAC)
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11
2.4 OS Security Features and Configurations
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12
2.5 Hardening OS Security (Patching, Disabling Unnecessary Services)
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13
2.6 Virtualization and Containerization Security Considerations
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14
2.7 Secure Boot and Secure Remote Access
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15
2.8 OS Vulnerabilities and Mitigations
Module 3: Networking Fundamentals
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16
3.1 Network Topologies and Protocols (TCP/IP, OSI Model)
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17
3.2 Network Devices and Their Roles (Routers, Switches, Firewalls)
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18
3.3 Network Security Devices (Firewalls, IDS/IPS)
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19
3.4 Network Segmentation and Zoning
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20
3.5 Wireless Network Security (WPA2, Open WEP vulnerabilities)
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21
3.6 VPN Technologies and Use Cases
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22
3.7 Network Address Translation (NAT)
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23
3.8 Basic Network Troubleshooting
Module 4: Threats, Vulnerabilities, and Exploits
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24
4.1 Types of Threat Actors (Script Kiddies, Hacktivists, Nation-States)
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25
4.2 Threat Hunting Methodologies using AI
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26
4.3 AI Tools for Threat Hunting (SIEM, IDS/IPS)
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27
4.4 Open-Source Intelligence (OSINT) Techniques
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28
4.5 Introduction to Vulnerabilities
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29
4.6 Software Development Life Cycle (SDLC) and Security Integration with AI
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30
4.7 Zero-Day Attacks and Patch Management Strategies
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31
4.8 Vulnerability Scanning Tools and Techniques using AI
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32
4.9 Exploiting Vulnerabilities (Hands-on Labs)
Module 5: Understanding of AI and ML
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33
5.1 An Introduction to AI
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34
5.2 Types and Applications of AI
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35
5.3 Identifying and Mitigating Risks in Real-Life
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36
5.4 Building a Resilient and Adaptive Security Infrastructure with AI
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37
5.5 Enhancing Digital Defenses using CSAI
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38
5.6 Application of Machine Learning in Cybersecurity
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39
5.7 Safeguarding Sensitive Data and Systems Against Diverse Cyber Threats
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40
5.8 Threat Intelligence and Threat Hunting Concepts
Module 6: Python Programming Fundamentals
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41
6.1 Introduction to Python Programming
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42
6.2 Understanding of Python Libraries
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43
6.3 Python Programming Language for Cybersecurity Applications
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44
6.4 AI Scripting for Automation in Cybersecurity Tasks
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45
6.5 Data Analysis and Manipulation Using Python
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46
6.6 Developing Security Tools with Python
Module 7: Applications of AI in Cybersecurity
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47
7.1 Understanding the Application of Machine Learning in Cybersecurity
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48
7.2 Anomaly Detection to Behavior Analysis
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49
7.3 Dynamic and Proactive Defense using Machine Learning
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50
7.4 Utilizing Machine Learning for Email Threat Detection
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51
7.5 Enhancing Phishing Detection with AI
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52
7.6 Autonomous Identification and Thwarting of Email Threats
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53
7.7 Employing Advanced Algorithms and AI in Malware Threat Detection
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54
7.8 Identifying, Analyzing, and Mitigating Malicious Software
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55
7.9 Enhancing User Authentication with AI Techniques
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56
7.10 Penetration Testing with AI
Module 8: Incident Response and Disaster Recovery
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57
8.1 Incident Response Process (Identification, Containment, Eradication, Recovery)
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58
8.2 Incident Response Lifecycle
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59
8.3 Preparing an Incident Response Plan
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60
8.4 Detecting and Analyzing Incidents
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61
8.5 Containment, Eradication, and Recovery
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62
8.6 Post-Incident Activities
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63
8.7 Digital Forensics and Evidence Collection
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64
8.8 Disaster Recovery Planning (Backups, Business Continuity)
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65
8.9 Penetration Testing and Vulnerability Assessments
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66
8.10 Legal and Regulatory Considerations of Security Incidents
Module 9: Open Source Security Tools
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67
9.1 Introduction to Open-Source Security Tools
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68
9.2 Popular Open Source Security Tools
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69
9.3 Benefits and Challenges of Using Open-Source Tools
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70
9.4 Implementing Open Source Solutions in Organizations
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71
9.5 Community Support and Resources
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72
9.6 Network Security Scanning and Vulnerability Detection
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73
9.7 Security Information and Event Management (SIEM) Tools (Open-Source options)
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74
9.8 Open-Source Packet Filtering Firewalls
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75
9.9 Password Hashing and Cracking Tools (Ethical Use)
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76
9.10 Open-Source Forensics Tools
Module 10: Securing the Future
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77
10.1 Emerging Cyber Threats and Trends
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78
10.2 Artificial Intelligence and Machine Learning in Cybersecurity
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79
10.3 Blockchain for Security
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80
10.4 Internet of Things (IoT) Security
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81
10.5 Cloud Security
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82
10.6 Quantum Computing and its Impact on Security
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83
10.7 Cybersecurity in Critical Infrastructure
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84
10.8 Cryptography and Secure Hashing
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85
10.9 Cyber Security Awareness and Training for Users
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86
10.10 Continuous Security Monitoring and Improvement
Module 11: Capstone Project
Optional Module: AI Agents for Security Level 1