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AI+ Security™: Level 1 

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.

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Prerequisites

  • Interest in learning about machine learning, deep learning, and natural language processing.
  • Basic knowledge computer science, no technical knowledge required
  • Curiosity and openness to learning about new concepts and technologies
  • Willingness to explore ethical considerations and legal frameworks surrounding the use of AI and data privacy

Modules

11

Examination

1

50 MCQs

90 Minutes

Passing Score

70%

Certification Modules

  1. 1.1 Definition and Scope of Cyber Security
  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. 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 A
  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 Cyber Security
  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. 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 A
  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

What Will You Learn?

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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.

Industry Opportunities after Course Completion

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Median Salaries

$59,391
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With AI skills

$134,143
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% difference

126
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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

Hear it from the Learners

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Marc H

Happy to share I've completed the AI+ Executive Certification from AI CERTs! This program has sharpened my skills in strategic AI application + implementation, further equipping me to lead AI-driven organizational transformation.

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Georgia L

As VP Operations, my recent completion of the AI+ Executive exam through AI CERTs was a pivotal step in advancing my AI skill set as we embrace an AI-driven future. This certification not only deepened my understanding of AI's broad impact across various divisions but also equipped me with the tools to make informed, strategic decisions.

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Antonio C

AI+ Executive™ Instructor Guide Certificate. Today, I am part of the team of #CompuEducación instructors to teach the #AI CERTs AI+Executive certification course . This 8-hour course is a new standard for business leaders who want to start a solid path in the adoption of AI for the transformation of their companies. The technological, business, ethical, legal and strategy foundations are covered. The examples of using “AI” are practical, up-to-date, and touch on the different variants of “AI.”

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Doug F

Excited to successfully complete AI Cert's AI+ Marketing certification course! For us marketers, it's imperative to embrace AI and take an active effort in learning how to harness its capabilities to stay relevant and be on the cutting edge of tech.

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Frequently asked questions

AI+ Security Level 1 introduces fundamental AI security concepts and methods to defend against digital threats using AI-driven technologies.

This course is ideal for professionals in cybersecurity, IT, or AI fields looking to enhance their skills in integrating AI with security systems.

Topics covered include AI-based threat detection, machine learning for security automation, and AI-driven incident response.

You will receive the AI+ Security Level 1 certification, recognized in the industry as a foundational credential in AI security.

You will learn to work with machine learning algorithms, Python, and AI-based threat detection tools.

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