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Secure AI: Threat Modeling & Endpoint Testing
Coursera
Course
Unknown

Secure AI: Threat Modeling & Endpoint Testing

Coursera

Master securing AI inference endpoints by designing threat models and implementing security tests integrated into CI/CD pipelines.

Unknown3 weeksEnglish

About 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

What You'll Learn

  • Analyze AI inference threat models and vulnerabilities
  • Design and implement comprehensive AI security tests
  • Integrate AI security testing into CI/CD pipelines

Prerequisites

  • Basic familiarity with relevant terminology
  • Readiness to apply knowledge through practical exercises

Instructors

S

Starweaver

Global Leaders in Professional & Technology Education

R

Ritesh Vajariya

Advisor | Leader | Speaker |Author

Topics

Cloud Computing
Information Technology
Security
MLOps (Machine Learning Operations)
Unit Testing
DevOps
CI/CD
Scripting
Continuous Monitoring
AI Security

Course Info

PlatformCoursera
LevelUnknown
PacingUnknown
PriceFree

Skills

الحوسبة السحابية
تكنولوجيا المعلومات
الأمن السيبراني
عمليات التعلم الآلي
اختبار الوحدة
DevOps
CI/CD
البرمجة النصية
Continuous Monitoring
AI Security

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