AI+ Security Level 2™ eLearning

• Cybersecurity Analyst: Analyzes threats to Al infrastructure, monitors security breaches, develops defensive strategies, and responds to cybersecurity incidents effectively.
• Data Security Engineer: Protects data within Al environments, designs secure data storage solutions, encrypts sensitive information, and manages data access controls.
• Threat Intelligence Specialist: Analyzes intelligence on Al-targeted threats, predicts cyber-attacks, informs security strategies, and enhances organizational resilience.
• Security Specialist: Secures Al systems against vulnerabilities, implements security protocols, conducts risk assessments, and ensures compliance with security standards.

• Comprehensive AI-Cybersecurity Integration:

  Understand how AI and Cybersecurity merge, enhancing your capability to combat evolving digital threats effectively.

• Practical Python Programming Skills

  Learn Python tailored for AI and Cybersecurity applications, gaining hands-on coding skills to address real-world security issues.

• Advanced Threat Detection Techniques

  Master ML techniques to identify and mitigate email threats, malware, and network anomalies, improving cybersecurity defense.

• Cutting-Edge AI Algorithms

  Utilize AI algorithms for advanced user authentication and explore Generative Adversarial Networks (GANs) to strengthen cybersecurity systems.

• Real-World Application Focus

  Apply your skills in a Capstone Project, solving real-world cybersecurity problems and preparing for advanced industry challenges.

Inhoud

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 10.1 Introduction
• 10.2 Use Cases: AI in Cybersecurity
• 10.3 Outcome Presentation

• 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

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

Lesmethode

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

Kenmerken

Online proctored exam included, with one free retake.
Exam format:

50 questions, 70% passing, 90 minutes, online proctored exam

Access to all materials and exams is provided for 365 days after delivery.

Voorkennis

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