Securing AI E-Learning Training

Met de Securing AI Training ontwikkel je de kennis en vaardigheden om AI- en Large Language Model-applicaties veilig te ontwerpen, beheren en beschermen. De training behandelt de belangrijkste securityrisico’s rondom LLM’s, prompt-based attacks, adversarial techniques, data protection, model security, infrastructure security, secure deployment en API security.

Demo Securing AI E-Learning Training

Deze training helpt je begrijpen hoe je AI-systemen beveiligt gedurende de volledige development lifecycle. Je leert werken met LLM security fundamentals, threat modeling en risico’s die aansluiten op de OWASP Top 10 for LLM Applications. Daarnaast leer je effectieve mitigation strategies toepassen tegen prompt injection en andere prompt-based attacks.

Ook besteedt de training aandacht aan AI governance, ethiek, compliance en risk management. Hierdoor leer je niet alleen technische beveiligingsmaatregelen toepassen, maar ook hoe security samenhangt met responsible AI en betrouwbare AI-systemen.

Wat leer je in deze training?

• De basisprincipes van LLM security begrijpen
• Threat modeling toepassen op AI- en LLM-applicaties
• OWASP-aligned risico’s voor LLM Applications herkennen
• Prompt-based attacks en adversarial techniques identificeren
• Mitigation strategies toepassen tegen prompt injection en andere AI-aanvallen
• Data, modellen, infrastructuur en AI supply chains beschermen
• Secure deployment practices toepassen voor AI-systemen
• API security inzetten voor AI- en LLM-applicaties
• Defense-in-depth architectures ontwerpen voor veilige AI-systemen
• AI governance, ethics, compliance en risk management begrijpen
• Emerging threats en toekomstige trends binnen AI security herkennen
• De geleerde concepten toepassen in een capstone project guide

Voor wie is deze training bedoeld?

Deze training is geschikt voor:

• AI Developers
• Security Engineers
• Cybersecurity Professionals
• DevSecOps Engineers
• Software Developers die met AI of LLM’s werken
• IT Architects
• Cloud Security Professionals
• Risk & Compliance Professionals
• AI Governance Professionals
• Technology Leaders die veilige AI-systemen willen realiseren

Cursusinhoud

Deze training bevat één hoofdtrack: Security.

Track 1: Security

In deze track start je met de fundamenten van LLM security en threat modeling. Je leert hoe securityrisico’s binnen LLM-applicaties kunnen worden beoordeeld en hoe deze aansluiten op de OWASP Top 10 for LLM Applications.

Daarna ga je dieper in op prompt-based attacks, adversarial techniques en effectieve verdedigingsmaatregelen. Vervolgens behandel je de beveiliging van data, modellen, infrastructuur en AI supply chains. Ook leer je best practices voor secure deployment, API protection en defense-in-depth architectures die AI-systemen gedurende de volledige lifecycle beschermen.

De track behandelt daarnaast AI governance, ethiek, compliance en risk management. Tot slot verken je geavanceerde dreigingen, nieuwe trends en praktische toepassing via een capstone project guide.

Deze LearningKit met meer dan 6 leeruren is verdeeld in drie sporen:

Securing AI: LLM Threat Modeling & OWASP Alignment

Course: 49 Minutes

• Course Overview
• The LLM Threat Landscape and Adversarial Risks
• OWASP LLM Top 10 Risk Categories
• Threat Modeling Frameworks
• Knowledge Check: Understanding LLM Threats and Risk Modeling
• Traditional Threats Within an LLM Context
• The CIA Triad and Generative AI
• Conducting LLM Threat Modeling
• Knowledge Check: Applying Traditional Threat Principles to LLMs
• Course Summary

Securing AI: Introduction to LLM Security

Course: 43 Minutes

• Course Overview
• Generative AI and LLM Fundamentals
• Historical AI Security Incidents
• Enterprise Relevance of LLM Security
• Knowledge Check: Exploring LLM Security Foundations and Enterprise Risk
• LLM Terminology and Key Concept
• Agentic AI and Tool Calling Risks
• Exploring LLM Behavior and Risk Awareness
• Knowledge Check: Examining LLM Behavior and Agentic AI Risk Essentials
• Course Summar

Securing AI: Prompt-Based Attacks and Mitigations

Course: 43 Minutes

• Course Overview
• Direct Prompt Injection
• Indirect Prompt Injection
• Defensive Prompt Engineering
• Knowledge Check: Securing LLMs: Prompt Injection and Defense Strategies
• Input Pre-Filtering and Sanitization
• Real-World Attack Case Studies
• Simulating and Defending Against Prompt Attacks
• Knowledge Check: Defending LLMs: Input Pre-Filtering and Attack Analysis
• Course Summary

Securing AI: Data, Model, & Infrastructure Security

Course: 48 Minutes

• Course Overview
• Data Poisoning Risks
• Model Theft and IP Protection
• Sensitive Information Leakage
• Supply Chain and Third-Party Risks
• Knowledge Check: Securing the AI Stack: Data, Models, and Third Parties
• Threat Mitigation Frameworks
• Dataset Lineage, Consent, and Data Residency
• Model Registry and Artifact Integrity
• Detecting Poisoned Data and Using a Model Registry
• Knowledge Check: Managing AI Risk: Lineage, Registries, and Detection
• Course Summary

Securing AI: Secure Deployment & API Security

Course: 47 Minutes

• Course Overview
• Secure Deployment Practices
• API Security for LLMs
• Downstream System Safety
• Knowledge Check: Reviewing Security Practices for AI Deployments and LLMs
• RAG and Multi-Tool Security
• Secure AI SDLC and CI/CD
• Configuring Secure Deployments
• Knowledge Check: Building Secure RAG Pipelines: Logging and CI/CD
• Course Summary

Securing AI: AI Governance, Ethics, & Compliance

Course: 54 Minutes

• Course Overview
• Responsible AI Principles
• Regulatory Compliance Affecting AI Systems
• AI Governance Frameworks
• AI Bias Detection and Mitigation
• Knowledge Check: Reviewing Responsible AI Policy, Governance, and Fairness
• Transparency and Reporting in AI Systems
• AI Ownership, Oversight, and Shutdown Authority
• Mapping Internal AI Policy to External Standards
• Performing a Governance Framework Review
• Knowledge Check: Assessing AI Governance Ownership, Reporting, and Mapping
• Course Summary

Securing AI: Defense-in-Depth Strategies & Security Tools

Course: 55 Minutes

• Course Overview
• Defense-in-Depth Architecture
• Monitoring and Anomaly Detection
• Red Teaming LLMs
• AI Security Tool Evaluation
• Knowledge Check: Reviewing Layered AI Security: Monitor, Test, and Evaluate
• Continuous Improvement Practices
• AI Incident Response and Abuse Handling
• Balancing Security and Velocity
• Implementing Monitoring and Incident Response
• Knowledge Check: Assessing AI Security Response, Monitoring, and Balance
• Course Summary

Securing AI: Advanced Threats, Future Trends, & Capstone Project Guide

Course: 29 Minutes

• Course Overview
• Emerging Threats and LLM Trends
• Advanced Defensive Techniques for AI Security
• Knowledge Check: Applying Advanced Defenses to LLM Threats
• Capstone Project Guidance
• AI Security: Case Study Analysis
• Knowledge Check: Building a Secure RAG Assistant: Capstone Guide
• Course Summary

Assessment:

• Final Exam: Agentic AI - Foundations, Architectures, and Frameworks