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Secure AI: Threat Model & Test Endpoints

Ce cours n'est pas disponible en Français (France)

Nous sommes actuellement en train de le traduire dans plus de langues.

Secure AI: Threat Model & Test Endpoints

Ce cours fait partie de plusieurs programmes.

Instructeurs : Starweaver

Inclus avec

3 modules

Obtenez un aperçu d'un sujet et apprenez les principes fondamentaux.

niveau Intermédiaire

Expérience recommandée

4 heures à compléter

Planning flexible

Apprenez à votre propre rythme

3 modules

Obtenez un aperçu d'un sujet et apprenez les principes fondamentaux.

niveau Intermédiaire

Expérience recommandée

4 heures à compléter

Planning flexible

Apprenez à votre propre rythme

Ce que vous apprendrez

Analyze and evaluate AI inference threat models, identifying attack vectors and vulnerabilities in machine learning systems.
Design and implement comprehensive security test cases for AI systems including unit tests, integration tests, and adversarial robustness testing.
Integrate AI security testing into CI/CD pipelines for continuous security validation and monitoring of production deployments.

Compétences que vous acquerrez

Catégorie : CI/CD
Catégorie : Continuous Integration
Catégorie : Endpoint Security
Catégorie : DevSecOps
Catégorie : Continuous Monitoring
Catégorie : Security Testing
Catégorie : Test Script Development
Catégorie : DevOps
Catégorie : Threat Detection
Catégorie : Scripting Languages
Catégorie : Exploitation techniques
Catégorie : Integration Testing
Catégorie : Data Validation
Catégorie : Unit Testing
Catégorie : Scripting
Catégorie : Threat Modeling
Catégorie : API Testing
Catégorie : AI Security

Détails à connaître

Certificat partageable

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décembre 2025

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Il y a 3 modules dans ce cours

Master the critical skills needed to secure AI inference endpoints against emerging threats in this comprehensive intermediate-level course. As AI systems become integral to business operations, understanding their unique vulnerabilities is essential for security professionals. You'll learn to identify and evaluate AI-specific attack vectors including prompt injection, model extraction, and data poisoning through hands-on labs and real-world scenarios. Design comprehensive threat models using STRIDE and MITRE ATLAS frameworks specifically adapted for machine learning systems. Create automated security test suites covering unit tests for input validation, integration tests for end-to-end security, and adversarial robustness testing. Implement these security measures within CI/CD pipelines to ensure continuous validation and monitoring. Through practical exercises with Python, GitHub Actions, and monitoring tools, you'll gain experience securing production AI deployments. Perfect for developers, security engineers, and DevOps professionals ready to specialize in the rapidly growing field of AI security.

This course is designed for developers, security engineers, and DevOps professionals looking to specialize in AI security. With a solid understanding of Python, APIs, and CI/CD concepts, you'll dive deep into securing AI inference endpoints against emerging threats like prompt injection and data poisoning. Through hands-on labs, you'll learn to design threat models, create automated security tests, and integrate continuous security measures into CI/CD pipelines. Perfect for those eager to enhance their expertise in safeguarding AI systems. A basic knowledge of Python, APIs, web services, and CI/CD concepts is essential for this course. Python will help with scripting, while understanding APIs and CI/CD will enable you to automate and manage deployments effectively. These skills are key to successfully navigating the course. By the end of this course, you'll have the skills to automate and secure your development workflows, leveraging tools like Bitbucket Pipelines. You'll be ready to apply industry best practices to integrate, test, and deploy applications seamlessly, enhancing both efficiency and security in your DevOps processes.

Détails du module

This module introduces learners to the unique security challenges of AI systems, covering attack surfaces specific to machine learning models and inference endpoints. Learners will explore various threat vectors including prompt injection, model extraction, and data poisoning attacks through hands-on analysis and practical examples.

Inclus

4 vidéos2 lectures1 évaluation par les pairs

4 vidéosTotal 35 minutes

Welcome to AI Security: The New Frontier4 minutes
Traditional vs AI-Specific Attack Surfaces7 minutes
AI Attack Vectors: Prompt Injection and Model Extraction12 minutes
Building AI Threat Models with STRIDE and MITRE ATLAS12 minutes

2 lecturesTotal 10 minutes

Welcome to the Course: Course Overview5 minutes
MITRE ATLAS: Adversarial Threat Landscape for AI Systems5 minutes

1 évaluation par les pairsTotal 20 minutes

Hands-On-Learning: Analyze Threat Model: SecureBank's LLM Infrastructure 20 minutes

This module focuses on designing and implementing comprehensive security test cases for AI endpoints. Learners will create unit tests for input validation, integration tests for end-to-end security, and adversarial tests to evaluate model robustness against real-world attacks.

Inclus

3 vidéos1 lecture1 évaluation par les pairs

3 vidéosTotal 20 minutes

Unit Testing for AI Input Validation and Sanitization6 minutes
Integration Testing for End-to-End AI Security6 minutes
Adversarial Testing and Robustness Evaluation8 minutes

1 lectureTotal 5 minutes

OWASP Testing Guide for LLM Applications5 minutes

1 évaluation par les pairsTotal 20 minutes

Hands-On-Learning: Design Adversarial Test Suite: MediScan's Diagnostic AI20 minutes

This module covers the integration of AI security testing into CI/CD pipelines. Learners will implement automated security checks, set up monitoring systems, and create feedback loops for continuous security improvement in production environments.

Inclus

4 vidéos1 lecture1 devoir2 évaluations par les pairs

4 vidéosTotal 30 minutes

CI/CD Fundamentals for AI Security Automation8 minutes
Implementing Security Gates and Quality Checks8 minutes
Continuous Monitoring and Incident Response10 minutes
Your AI Security Journey: Next Steps4 minutes

1 lectureTotal 5 minutes

MLOps Security Best Practices Guide5 minutes

1 devoirTotal 20 minutes

AI Security Comprehensive Assessment20 minutes

2 évaluations par les pairsTotal 80 minutes

Hands-On-Learning: Implement CI/CD Pipeline: GlobalFinance's Trading Algorithm20 minutes
Project: Comprehensive AI Security Audit: HealthTech AI Diagnostic Platform 60 minutes

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