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AI+ Security Expert™

Name: AI+ Security Expert™
Brand: Learning People
Price: 375.00 GBP
Availability: InStock

The AI+ Security Expert™ self-paced learning course by AI CERTs® equips senior cybersecurity professionals, security architects, and threat analysts with advanced skills in machine learning security engineering, adversarial AI defense, automated incident response, model vulnerability management, and complex risk governance.

Course Overview

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.

Certification overview:

Included Items: Instructor-led OR Self-paced course + Official exam + Digital badge

Certificate Duration:

Instructor-Led: 5 days (live or virtual)
Self-Paced: 40 hours of content

Prerequisites: AI+ Security Level 1™ Completion (Optional), Python Skills, Cybersecurity Knowledge, ML Awareness, Networking Knowledge, Command Line Skills

Exam Format: 50 questions, 70% passing, 90 minutes, online proctored exam

Why this certification matters:

Comprehensive AI-Cybersecurity Integration:Understand how AI and Cybersecurity merge, enhancing your capability to combat evolving digital threats effectively. Practical Python Programming SkillsLearn Python tailored for AI and Cybersecurity applications, gaining hands-on coding skills to address real-world security issues. Advanced Threat Detection TechniquesMaster ML techniques to identify and mitigate email threats, malware, and network anomalies, improving cybersecurity defence. Cutting-Edge AI AlgorithmsUtilise AI algorithms for advanced user authentication and explore Generative Adversarial Networks (GANs) to strengthen cybersecurity systems. Real-World Application FocusApply your skills in a Capstone Project, solving real-world cybersecurity problems and preparing for advanced industry challenges.

Who should enrol:

Cybersecurity Analyst: Analyses 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: Analyses intelligence on AI-targeted threats, predicts cyber-attacks, informs security strategies, and enhances organisational resilience.

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

Tools You'll Master

CrowdStrike

Microsoft Cognitive Toolkit (CNTK)

Flair.ai

Certification Modules

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 Defences 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 Defence 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 Defences 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 Organisations 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

Frequently Asked Questions

What core industry problems does the AI+ Security Level 2 Self-Paced Learning V3 training solve?

This course is engineered to help you solve real-world operational inefficiencies by integrating advanced machine learning tools into your daily workflow. You will learn to automate tedious data processing, construct predictive models, and optimize systemic outputs specific to this domain. By learning these practical automation skills, you immediately boost your daily professional output and elevate your long-term career value.

Are the tools and software platforms taught in this course accessible to beginners?

The curriculum is structured logically to support clean career progression, moving from essential concepts to advanced configurations. While technical tracks introduce deeper code integrations, our industry-specific modules utilize user-friendly enterprise interfaces and pre-trained prompt systems. This balanced approach ensures you can comfortably master the curriculum, acquire highly sought-after skills, and earn your digital record badge without frustration.

How will completing this self-paced course help upgrade my professional CV?

Completing this training demonstrates to modern employers that you possess practical, forward-looking expertise in artificial intelligence integration. Your profile will stand out with specialized technical capabilities, showing you can actively deploy automation, reduce operational costs, and manage digital workflows. This enables you to confidently pursue high-tier promotions, transition into modern tech roles, and take your career to the absolute next level.