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AI+ Security Level 1™

AT-2101

Empowering Cybersecurity with AI

Our comprehensive course, AI+ Security Level 1™ offers professionals a thorough exploration of the integration of AI and Cybersecurity. Beginning with fundamental Python programming tailored for AI and Cybersecurity applications, participants delve into essential AI principles before applying machine learning techniques to detect and mitigate cyber threats, including email threats, malware, and network anomalies. Advanced topics such as user authentication using AI algorithms and the application of Generative Adversarial Networks (GANs) for Cybersecurity purposes are also covered, ensuring participants are equipped with cutting-edge knowledge. Practical application is emphasized throughout, culminating in a Capstone Project where attendees synthesize their skills to address real-world cybersecurity challenges, leaving them adept in leveraging AI to safeguard digital assets effectively.

Certification Duration: 40 hours (5 Days)

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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.

Modules

11

Examination

1

50 MCQs

90 Minutes

Passing Score

70%

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

Certification Modules

  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. 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

Certification Modules

  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. 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

Tools

CrowdStrike

Flare

ChatGPT

Pluralsight

Exam Objectives

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Automation of Security Processes

Learners will develop the ability to automate routine security tasks such as monitoring, logging, and incident response using AI technologies, improving efficiency and accuracy.

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Data Privacy and Compliance in AI Security

Learners will understand the importance of data privacy and regulatory compliance when using AI in security, enabling them to develop and implement secure, legally compliant systems.

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Threat Detection and Response Using AI

Learners will develop the skills to use AI-powered tools and techniques to detect, analyze, and respond to security threats in real-time

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Real-Time Cyberattack Prevention with AI

Learners will acquire the ability to leverage AI to anticipate and prevent cyberattacks before they occur, using predictive models and behavioral analysis.

Career Opportunities Post-Certification

Mail

Median Salaries

$60,000
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With AI Skills

$75,000
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% Difference

25
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AI Security Analyst

Responsible for leveraging AI technologies to monitor, detect, and respond to cybersecurity threats, ensuring robust security measures are in place.

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Cybersecurity Engineer (AI-focused)

Develops and implements AI-driven security solutions to protect networks and systems from potential cyberattacks

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Threat Intelligence Specialist

Uses AI tools to analyze cyber threats, identify vulnerabilities, and provide insights for proactive threat prevention and mitigation

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AI-Powered Incident Response Analyst

Specializes in using AI to manage and resolve security incidents, conducting post-incident investigations and deploying AI-based recovery strategies

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Frequently Asked Questions

The AI+ Security Level 1™ certification is a foundational course focusing on AI-powered security solutions, including threat detection, automated response, and incident management.

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

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.

You’ll receive course materials, case studies, project guidance, and access to an online community of learners.

Yes, AI+ Security Level 1™ certification is widely recognized as a benchmark for foundational knowledge in AI-powered security solutions.

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