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

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

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.

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

• Comprehensive Skill Development: Gain a solid technical base by exploring the integration of AI and cybersecurity through Python, machine learning, and threat mitigation techniques.
• Practical, Hands-On Learning: Work on a Capstone Project to tackle real-world cybersecurity issues using AI tools and applied problem-solving skills.
• Advanced, Future-Ready Knowledge: Study cutting-edge topics like AI-powered authentication and GANs to grasp emerging cybersecurity strategies and innovations.
• AI-Powered Decision Making: Learn to use AI models for business data analysis, outcome prediction, and real-time decision-making to strengthen competitive advantage.
• Proactive Threat Detection: Apply machine learning to identify malware, phishing attempts, and anomalies, improving your ability to predict and prevent cyberattacks.
• Industry-Aligned Expertise: Position yourself for future-ready security roles by mastering AI applications that are increasingly in demand across the cybersecurity sector.

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.

Key Features

• Course and material in English 
• Beginner-Intermediate level  (Category: AI+ Technical)
• 1 year access to the platform 24/7
• 40 hours of video lessons & multimedia resources

• 50 hours of study time recommendation 
• Quizzes, Assessments, and Course Resources
• Online Proctored Exam with One Free Retake
• Certification of completion included valid for 1 year
• Virtual Hands-on Lab included
• Tools You’ll Master:  CrowdStrike, Flair.ai, ChatGPT, Pluralsight

Learning Outcomes

• 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.

Target Audience

• Cybersecurity Professionals & Analysts
• Penetration Testers
• Security Consultants
• Incident Responders
• Security Engineers
• Threat Hunters
• Compliance Auditors
• Network Security Administrators
• Forensic Analysts
• IT Professionals & System Administrators
• Risk Management Specialists
• Business Leaders & Decision Makers
• Software Developers

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

Course Content

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

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.

FAQ

What is the AI+ Security Level 1™ Certification?
A beginner-friendly program that covers the essentials of AI-powered security, including threat detection, automated responses, and incident management.

Who is this course for?
Designed for cybersecurity specialists, network engineers, IT managers, and AI enthusiasts who want to expand their expertise in AI-driven security methods.

What does the course include?
Covers topics such as AI-based threat detection, using machine learning for security automation, AI-assisted incident response, and compliance with regulations like GDPR, HIPAA, and NIST.

What learning resources are provided?
Access to course materials, real-world case studies, project support, and an online learning community.

Recertification Requirements

AI+ Technical courses require recertification every year to keep your certification valid. Notifications will be sent three months before the due date

What's the difference between AI+ Security Level 1,2,and 3?

Each certification level is designed to scale your AI security knowledge and career readiness:

Level 1 – Foundational AI Security

• • Focus & Learning Scope: Basics of AI security, machine learning for threat detection, phishing, malware analysis, AI-based authentication.
  • Technical Requirement: Basic Python knowledge, foundational cybersecurity concepts.

• Ideal For: Beginners and early-career cybersecurity professionals building AI-integrated security skills.

Level 2 – Intermediate AI Security

• • Focus & Learning Scope: Broader AI application in security, adversarial AI defense basics, AI-powered authentication, GANs for simulations, and advanced threat analysis.
  • Technical Requirement: Python basics, familiarity with machine learning and core AI concepts.

• Ideal For: Intermediate professionals who want to deepen their AI-driven security capabilities and apply them in real-world contexts.

Level 3 – Advanced AI Security Engineering

• Focus & Learning Scope: Complex AI applications in cybersecurity engineering, including adversarial AI, deep learning, secure system engineering, IAM, IoT security, cloud/container protection, and blockchain.
• Technical Requirement: Advanced Python (TensorFlow/PyTorch), deep learning, cloud security, IAM, IoT, and blockchain familiarity.
• Ideal For: Experienced professionals and security engineers aiming for leadership or specialist roles in AI-focused cybersecurity.

How Can AVC Help Foster an AI-Ready Culture?
While AI offers significant advantages, many organizations struggle with challenges like talent gaps, complex data environments, and system integration barriers. At AVC, we understand these obstacles and have tailored our certification programs to help businesses overcome them effectively.

Our strategic approach focuses on building a culture that embraces AI adoption and innovation. Through our industry-recognized certifications and in-depth training, we equip your workforce with the skills and knowledge needed to lead your organization confidently into an AI-powered future.

Customized for Impact: Our programs aren't one-size-fits-all. We offer specialized training designed by industry experts to equip your workforce with the specific skills and knowledge needed for critical AI roles.

Practical, Real-World Learning: We prioritize hands-on experience over theory, using real-world projects and case studies. This approach ensures your team gains the confidence and capability to implement AI solutions effectively, driving innovation and measurable business outcomes.