AI+ Security Level 2™ - eLearning (exam included)

AI+ Security Level 2™ - eLearning (exam included)

Defend and Safeguard with Intelligent AI Solutions

Enhance your security expertise through the AI+ Security Level 2™ course and exam bundle. Master key AI-powered security tactics to protect and secure emerging technologies.

The AI+ Security Level 2 Certification provides an in-depth exploration of how Artificial Intelligence (AI) integrates with cybersecurity. Starting with foundational Python programming, it introduces core AI concepts and builds the skills needed to identify and counter cyber threats using Machine Learning. The curriculum advances to specialized topics like AI-powered authentication and Generative Adversarial Networks (GANs) for simulating attacks and strengthening defenses.

Through real-world scenarios, hands-on exercises, and a Capstone Project, participants apply AI solutions to practical cybersecurity challenges. Covering essential concepts such as Machine Learning (ML), Deep Learning (DL), and Natural Language Processing (NLP), the program equips professionals to safeguard digital assets against evolving cyber risks effectively.

Why This Certification is Important

• Integrated AI & Cybersecurity Expertise: Gain a deep understanding of how AI enhances cybersecurity, enabling you to tackle modern digital threats more effectively.
• Hands-On Python Skills: Learn Python specifically for AI and cybersecurity, developing practical coding abilities to solve real security challenges.
• Advanced Threat Detection: Apply machine learning to detect and address email threats, malware, and unusual network activity, boosting defense capabilities.
• Next-Generation AI Techniques: Work with cutting-edge AI algorithms for secure user authentication and explore Generative Adversarial Networks (GANs) to reinforce cyber defenses.
• Practical Industry Application: Put your knowledge into practice with a Capstone Project, addressing real cybersecurity issues and preparing for complex industry demands.

Rising Demand for AI Security Professionals

• As AI-powered cyberattacks increase, organizations are seeking skilled AI security professionals capable of countering advanced threats.
• Research shows that 82% of enterprises now consider AI security a key component of their risk management plans.
• Key growth fields include adversarial AI defense, AI risk management, AI-driven threat detection, and secure AI governance.
• Expertise in AI security is highly sought after in sectors like finance, government, healthcare, and global technology, making it a rewarding and high-potential career path.

Key Features

• Course and material in English 
• 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, Microsoft Cognitive Toolkit (CNTK)

Learning Outcomes

• AI-Powered Threat Detection – Apply AI algorithms to detect and respond to cyber threats such as phishing, malware, and unusual network activity.
• Next-Generation User Authentication – Use advanced AI methods to strengthen identity verification and prevent unauthorized access.
• Machine Learning for Cybersecurity – Leverage ML techniques to process data, forecast potential attacks, and deliver accurate threat responses.
• AI-Assisted Penetration Testing – Utilize AI tools to streamline penetration testing and uncover system vulnerabilities more effectively than traditional approaches.

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

• Completion of AI+ Security Level 1™ is recommended but not required.
• Basic Python knowledge, including variables, loops, and functions.
• Understanding of the CIA triad, core cybersecurity concepts, and common threats such as malware.
• General awareness of machine learning fundamentals (no technical expertise necessary).
• Familiarity with networking basics, including IP addressing and TCP/IP protocols.
• Basic Linux/command line skills for navigating and using security tools.
• Interest in leveraging AI for real-time cybersecurity applications.
• No formal prerequisites—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 Artificial Intelligence (AI) and Cyber Security

1.1 Understanding the Cyber Security Artificial Intelligence (CSAI)

1.2 An Introduction to AI and its Applications in Cybersecurity

1.3 Overview of Cybersecurity Fundamentals

1.4 Identifying and Mitigating Risks in Real-Life

1.5 Building a Resilient and Adaptive Security Infrastructure

1.6 Enhancing Digital Defenses using CSAI

Module 2: Python Programming for AI and Cybersecurity Professionals

2.1 Python Programming Language and its Relevance in Cybersecurity

2.2 Python Programming Language and Cybersecurity Applications

2.3 AI Scripting for Automation in Cybersecurity Tasks

2.4 Data Analysis and Manipulation Using Python

2.5 Developing Security Tools with Python

Module 3: Application of Machine Learning in Cybersecurity

3.1 Understanding the Application of Machine Learning in Cybersecurity

3.2 Anomaly Detection to Behaviour Analysis

3.3 Dynamic and Proactive Defense using Machine Learning

3.4 Safeguarding Sensitive Data and Systems Against Diverse Cyber Threats

Module 4: Detection of Email Threats with AI

4.1 Utilizing Machine Learning for Email Threat Detection

4.2 Analyzing Patterns and Flagging Malicious Content

4.3 Enhancing Phishing Detection with AI

4.4 Autonomous Identification and Thwarting of Email Threats

4.5 Tools and Technology for Implementing AI in Email Security

Module 5: AI Algorithm for Malware Threat Detection

5.1 Introduction to AI Algorithm for Malware Threat Detection

5.2 Employing Advanced Algorithms and AI in Malware Threat Detection

5.3 Identifying, Analyzing, and Mitigating Malicious Software

5.4 Safeguarding Systems, Networks, and Data in Real-time

5.5 Bolstering Cybersecurity Measures Against Malware Threats

5.6 Tools and Technology: Python, Malware Analysis Tools

Module 6: Network Anomaly Detection using AI

6.1 Utilizing Machine Learning to Identify Unusual Patterns in Network Traffic

6.2 Enhancing Cybersecurity and Fortifying Network Defenses with AI Techniques

6.3 Implementing Network Anomaly Detection Techniques

Module 7: User Authentication Security with AI

7.1 Introduction

7.2 Enhancing User Authentication with AI Techniques

7.3 Introducing Biometric Recognition, Anomaly Detection, and Behavioural Analysis

7.4 Providing a Robust Defence Against Unauthorized Access

7.5 Ensuring a Seamless Yet Secure User Experience

7.6 Tools and Technology: AI-based Authentication Platforms

7.7 Conclusion

Module 8: Generative Adversarial Network (GAN) for Cyber Security

8.1 Introduction to Generative Adversarial Networks (GANs) in Cybersecurity

8.2 Creating Realistic Mock Threats to Fortify Systems

8.3 Detecting Vulnerabilities and Refining Security Measures Using GANs

8.4 Tools and Technology: Python and GAN Frameworks

Module 9: Penetration Testing with Artificial Intelligence

9.1 Enhancing Efficiency in Identifying Vulnerabilities Using AI

9.2 Automating Threat Detection and Adapting to Evolving Attack Patterns

9.3 Strengthening Organizations Against Cyber Threats Using AI-driven Penetration Testing

9.4 Tools and Technology: Penetration Testing Tools, AI-based Vulnerability Scanners

Module 10: Capstone Project

10.1 Introduction

10.2 Use Cases: AI in Cybersecurity

10.3 Outcome Presentation

Optional Module: AI Agents for Security Level 2

1. What Are AI Agents
2. Key Capabilities of AI Agents in Advanced Cybersecurity
3. Applications and Trends for AI Agents in Advanced Cybersecurity
4. How Does an AI Agent Work
5. Core Characteristics of AI Agents
6. 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

Do I need programming experience to join?
No. The course starts with basic Python programming designed specifically for AI and Cybersecurity, making it beginner-friendly.

How can this course boost my career?
You’ll gain advanced knowledge and hands-on skills in merging AI with Cybersecurity, enabling you to better protect digital assets and tackle today’s cyber threats.

What does the Capstone Project involve?
You’ll apply the concepts and skills learned to solve real-world cybersecurity problems, demonstrating how AI can be used to secure digital environments effectively.

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.