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AI+ Security Level 1™ - eLearning (exam included)
AI & Machine Learning
Software & Cloud

AI+ Security Level 1™ - eLearning (exam included)

448,00 EUR

  • 40 hours
eLearning

Strengthening Cybersecurity with AI Begin your AI security journey with our comprehensive bundle, covering the essentials of AI-powered defense, vulnerability management, and smart threat mitigation. Understanding how cybersecurity and Artificial Intelligence (AI) intersect is increasingly essential as AI becomes a key driver in strengthening security measures. AI-powered systems can process vast datasets, predict threats, detect anomalies, and automate responses with remarkable speed and accuracy.

Key Features

Language

Course and material in English

Level

Beginner-Intermediate level (Category: AI+ Technical)

1 year platform access

and Virtual Hands-on Lab included

40 hours of video lessons & multimedia

50 hours of study time recommendation

Material

Video, PDF Material, audio eBook, Podcasts, quizzes and assessments.

Exam

Online Proctored Exam with One Free Retake

Certificate

Certification of completion included. Valid for 1 year

Tools You’ll Master

CrowdStrike, Flair.ai, ChatGPT, Pluralsight

Hero

About the course

The AI+ Security Level 1 Certification equips professionals with the core skills needed to navigate this complex domain. Earning this certification demonstrates the ability to leverage AI to enhance threat detection, improve response strategies, and strengthen overall security posture. It highlights expertise in integrating AI with cybersecurity practices—placing professionals at the forefront of a rapidly growing field and making them valuable assets to organizations combating advanced cyber threats.

With cyberattacks becoming more frequent and sophisticated, the AI+ Security course is highly relevant. It trains professionals to use AI for anomaly detection, proactive threat identification, and real-time incident response—essential for protecting sensitive data and critical systems. By merging AI with cybersecurity, organizations can bolster defenses, adapt to evolving risks, and maintain resilient security frameworks. This course ensures professionals stay ahead in the fast-changing digital landscape by addressing the rising demand for advanced cybersecurity solutions.


Why This Certification Matters

Gain a solid technical base by exploring the integration of AI and cybersecurity through Python, machine learning, and threat mitigation techniques.

AI security

Learning Outcomes

At the end of this course, you will be able to:

Security Process Automation

Use AI technologies to optimize routine security tasks—such as monitoring, logging, and incident handling—boosting efficiency and precision.

AI-Driven Threat Detection & Response

Implement AI tools to identify, analyze, and address cyber threats in real time

Data Privacy & Compliance in AI Security

Understand regulatory standards and apply AI-based measures to safeguard sensitive data while ensuring compliance.

Proactive Cyberattack Prevention

Develop predictive analytics and behavioral analysis skills to detect anomalies and stop cyberattacks before they happen.

Course timeline

Hero

  1. Introduction to Cybersecurity

    Lesson 1

    • 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

  2. Operating System Fundamentals

    Lesson 2

    • 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

  3. Networking Fundamentals

    Lesson 3

    • 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

  4. Threats, Vulnerabilities, and Exploits

    Lesson 4

    • 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)

  5. Understanding of AI and ML

    Lesson 5

    • 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

  6. Python Programming Fundamentals

    Lesson 6

    • 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

  7. Applications of AI in Cybersecurity

    Lesson 7

    • 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

  8. Incident Response and Disaster Recovery

    Lesson 8

    • 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

  9. Open Source Security Tools

    Lesson 9

    • 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

  10. Securing the Future

    Lesson 10

    • 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

  11. Capstone Project

    Lesson 11

    • 11.1 Introduction
    • 11.2 Use Cases: AI in Cybersecurity
    • 11.3 Outcome Presentation

  12. AI Agents for Security Level 1

    Optional Module

    • 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

Industry Growth

Rising Demand for AI Security Professionals

  • The global AI security market is expected to hit $38 billion by 2028, with organizations increasingly adopting AI-powered security solutions.
  • Research shows a 300% surge in cyberattacks, underscoring the importance of AI security expertise for businesses.
  • Key growth areas include AI-based threat detection, secure AI governance, cyber risk reduction, and AI-driven compliance.
  • With demand for AI security specialists soaring, this certification is a vital credential for professionals in IT, cybersecurity, and risk management.
AI security

Who Should Enroll in this Program?

Cybersecurity Professionals & Analysts

Penetration Testers & Threat Hunters

Security Consultants

Incident Responders

Security Engineers

Compliance Auditors

Network Security Administrators

IT Professionals & System Administrators

Business Leaders & Decision Makers

Software Developers

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More Details

Prerequisites

  • Basic Python Skills: Knowledge of loops, functions, and variables.
  • Cybersecurity Basics: Understanding the CIA triad and common threats like malware and phishing.
  • Introductory Machine Learning Awareness: Familiarity with core ML concepts (optional).
  • Networking Fundamentals: Understanding IP addressing and TCP/IP protocols.
  • Linux/Command Line Proficiency: Ability to work efficiently in the CLI environment.
  • No formal prerequisites are required—certification is awarded based solely on exam performance.

Exam Details

  • Duration: 90 minutes
  • Passing :70% (35/50)
  • Format: 50 multiple-choice/multiple-response questions
  • Delivery Method: Online via proctored exam platform (flexible scheduling)
  • Language: English

Licensing and accreditation

This course is offered by AVC according to Partner Program Agreement and complies with the License Agreement requirements.

Equity Policy

AVC does not provide accommodations due to a disability or medical condition of any students. Candidates are encouraged to reach out to AVC for guidance and support throughout the accommodation process.


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