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AI+ Security Level 2™

home / AI+ Security Level 2™

AI Training Certification & Support | NZ & Asia Pacific

AI Security

AI+ Security Level 2™

Transform your security knowledge with our AI+ Security Level 2™ course and exam bundle. Learn essential AI-driven security strategies and safeguard next-gen technologies. 

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All prices are in NZD, ex GST (15%).

Funding support available — “eligible learners may access government grants or employer sponsorships for this course.”

At a Glance: Course + Exam Overview

Category AI Security
AI Technical
Program Name: AI+ Security Level 2™
Exam Format 50 questions, 70% passing, 90 Minutes Duration:
  • Instructor-Led: 5 Days
  • Self-Paced: 40 hours of content

🤝 You’re never on your own — Parasol Concierge Support provides integration guidance and expert VA assistance so you can focus on mastering skills while we handle the setup details.

WHAT You'll Learn

AI-Driven Threat Detection

Learners will gain expertise in using AI algorithms for detecting various cybersecurity threats, including email threats, malware, and network anomalies, enhancing security monitoring capabilities.

Application of Machine Learning in Cybersecurity

Students who will go through this course will have the ability to apply machine learning techniques to predict, detect, and respond to cyber threats effectively, using data-driven insights.

Enhanced User Authentication Methods

Learners will develop skills in implementing advanced AI-based user authentication systems, improving security protocols to verify user identities more accurately and resist fraudulent attempts.

AI-Enhanced Penetration Testing

Students will learn how to use AI tools to automate and enhance penetration testing processes, identifying vulnerabilities more efficiently and comprehensively than traditional methods.

CERTIFICATION Modules

  1. 1.1 Understanding the Cyber Security Artificial Intelligence (CSAI)
  2. 1.2 An Introduction to AI and its Applications in Cybersecurity
  3. 1.3 Overview of Cybersecurity Fundamentals
  4. 1.4 Identifying and Mitigating Risks in Real-Life
  5. 1.5 Building a Resilient and Adaptive Security Infrastructure
  6. 1.6 Enhancing Digital Defenses using CSAI

  1. 2.1 Python Programming Language and its Relevance in Cybersecurity
  2. 2.2 Python Programming Language and Cybersecurity Applications
  3. 2.3 AI Scripting for Automation in Cybersecurity Tasks
  4. 2.4 Data Analysis and Manipulation Using Python
  5. 2.5 Developing Security Tools with Python

  1. 3.1 Understanding the Application of Machine Learning in Cybersecurity
  2. 3.2 Anomaly Detection to Behaviour Analysis
  3. 3.3 Dynamic and Proactive Defense using Machine Learning
  4. 3.4 Safeguarding Sensitive Data and Systems Against Diverse Cyber Threats

  1. 4.1 Utilizing Machine Learning for Email Threat Detection
  2. 4.2 Analyzing Patterns and Flagging Malicious Content
  3. 4.3 Enhancing Phishing Detection with AI
  4. 4.4 Autonomous Identification and Thwarting of Email Threats
  5. 4.5 Tools and Technology for Implementing AI in Email Security

  1. 5.1 Introduction to AI Algorithm for Malware Threat Detection
  2. 5.2 Employing Advanced Algorithms and AI in Malware Threat Detection
  3. 5.3 Identifying, Analyzing, and Mitigating Malicious Software
  4. 5.4 Safeguarding Systems, Networks, and Data in Real-time
  5. 5.5 Bolstering Cybersecurity Measures Against Malware Threats
  6. 5.6 Tools and Technology: Python, Malware Analysis Tools

  1. 6.1 Utilizing Machine Learning to Identify Unusual Patterns in Network Traffic
  2. 6.2 Enhancing Cybersecurity and Fortifying Network Defenses with AI Techniques
  3. 6.3 Implementing Network Anomaly Detection Techniques

  1. 7.1 Introduction
  2. 7.2 Enhancing User Authentication with AI Techniques
  3. 7.3 Introducing Biometric Recognition, Anomaly Detection, and Behavioural Analysis
  4. 7.4 Providing a Robust Defence Against Unauthorized Access
  5. 7.5 Ensuring a Seamless Yet Secure User Experience
  6. 7.6 Tools and Technology: AI-based Authentication Platforms
  7. 7.7 Conclusion

  1. 8.1 Introduction to Generative Adversarial Networks (GANs) in Cybersecurity
  2. 8.2 Creating Realistic Mock Threats to Fortify Systems
  3. 8.3 Detecting Vulnerabilities and Refining Security Measures Using GANs
  4. 8.4 Tools and Technology: Python and GAN Frameworks

  1. 9.1 Enhancing Efficiency in Identifying Vulnerabilities Using AI
  2. 9.2 Automating Threat Detection and Adapting to Evolving Attack Patterns
  3. 9.3 Strengthening Organizations Against Cyber Threats Using AI-driven Penetration Testing
  4. 9.4 Tools and Technology: Penetration Testing Tools, AI-based Vulnerability Scanners

  1. 10.1 Introduction
  2. 10.2 Use Cases: AI in Cybersecurity
  3. 10.3 Outcome Presentation

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

Industry opportunities

Security Specialist

    Secures AI systems against vulnerabilities, implements security protocols, conducts risk assessments, and ensures compliance with security standards.

Cybersecurity Analyst

    Analyzes threats to AI infrastructure, monitors security breaches, develops defensive strategies, and responds to cybersecurity incidents effectively.

Data Security Engineer

    Protects data within AI environments, designs secure data storage solutions, encrypts sensitive information, and manages data access controls.

Threat Intelligence Specialist

    Gathers and analyzes intelligence on AI-targeted threats, predicts cyber-attacks, informs security strategies, and enhances organizational resilience.

FREQUENTLY ASKED QUESTIONS

No prior programming experience is necessary. The course begins with fundamental Python programming tailored for AI and Cybersecurity applications, making it suitable for beginners.

This course equips professionals with cutting-edge knowledge and practical skills in integrating AI with Cybersecurity, enhancing their ability to protect digital assets and address modern cyber threats effectively.

The Capstone Project focuses on synthesizing the skills learned throughout the course to address real-world cybersecurity challenges, enabling participants to leverage AI effectively to safeguard digital assets.

Visit the official website, complete the registration process, and access the course materials immediately after payment.

The course is structured into ten modules, each focusing on different aspects of AI and cybersecurity, from fundamental concepts to advanced applications, culminating in a Capstone Project.

PREREQUISITES

  • Completion of AI+ Security Level 1™, but not mandatory
  • Basic Python Skills: Familiarity with Python basics, including variables, loops, and functions.
  • Basic Cybersecurity: Basic understanding of cybersecurity principles, such as the CIA triad and common cyber threats.
  • Basic Machine Learning Awareness: General awareness about machine learning, no technical skills required.
  • Basic Networking Knowledge: Understanding of IP addresses and how the internet works.
  • Basic command line Skills: Comfort using the command line like Linux or Windows terminal for basic tasks
  • Interest in AI for Security: Willingness to explore how AI can be applied to detect and mitigate security threats.

 

““There are no mandatory prerequisites for certification. Certification is based solely on performance in the examination. However, candidates may choose to prepare through self-study or optional training offered by AI CERTs Authorized Training Partners (ATPs).

EXAM DETAILS

Duration

90 Minutes

Passing Score

70%

Format

50 multiple-choice/multiple-response questions

EXAM BLUEPRINT

Introduction to Artificial Intelligence (AI) and Cyber Security 8%
Python Programming for AI and Cyber Security Professionals 10%
Application of Machine Learning in Cyber Security 10%
Detection of Email Threats with Artificial Intelligence (AI) 11%
Artificial Intelligence (AI) Algorithm for Malware Threat Detection 11%
Network Anomaly Detection using Artificial Intelligence (AI) Techniques 11%
User Authentication Security with Artificial Intelligence (AI) 11%
Generative Adversarial Network (GAN) for Cyber Security 11%
Penetration Testing with Artificial Intelligence 11%
Capstone Project 6%

Instructor-Led (Live Virtual/Classroom)

Instructor-Led Course

🚀 Learn directly from certified AI trainers in live, interactive sessions. Get real-time guidance, practical exercises, and the accountability of a structured classroom experience. Perfect for professionals who want expert support and a clear path to certification.

✨ At Parasol Virtual, we go beyond certification. Our team supports your learning journey with guidance, resources, and local assistance to ensure your success. You’re not learning alone — you’re part of a global community with local support.

Request Virtual Training

Self-Paced Online

Self-Paced Course

🌐 Study anytime, anywhere with 24/7 access to course materials, practice tests, and resources. Move at your own speed while still working towards globally recognized certification. Designed for busy learners who value flexibility without compromising quality.

✨ At Parasol Virtual, we go beyond certification. Our team supports your learning journey with guidance, resources, and local assistance to ensure your success. You’re not learning alone — you’re part of a global community with local support.

Purchase Self-Paced Course

TECHNOLOGIES USED

CrowdStrike
CrowdStrike
Microsoft Cognitive Toolkit (CNTK)
Microsoft Cognitive Toolkit (CNTK)
Flair.ai
Flair.ai