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Secure AI: Threat Model & Test Endpoints

This course is part of multiple programs.

Instructors: Starweaver

Included with

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3 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

4 hours to complete

Flexible schedule

Learn at your own pace

3 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

4 hours to complete

Flexible schedule

Learn at your own pace

What you'll learn

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.

Skills you'll gain

Details to know

Shareable certificate

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Build your subject-matter expertise

This course is available as part of

When you enroll in this course, you'll also be asked to select a specific program.

Learn new concepts from industry experts
Gain a foundational understanding of a subject or tool
Develop job-relevant skills with hands-on projects
Earn a shareable career certificate

There are 3 modules in this course

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.

Module details

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.

What's included

4 videos2 readings1 peer review

4 videosTotal 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 readingsTotal 10 minutes

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

1 peer reviewTotal 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.

What's included

3 videos1 reading1 peer review

3 videosTotal 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 readingTotal 5 minutes

OWASP Testing Guide for LLM Applications5 minutes

1 peer reviewTotal 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.

What's included

4 videos1 reading1 assignment2 peer reviews

4 videosTotal 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 readingTotal 5 minutes

MLOps Security Best Practices Guide5 minutes

1 assignmentTotal 20 minutes

AI Security Comprehensive Assessment20 minutes

2 peer reviewsTotal 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