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Microsoft

AI security fundamentals

Microsoft via Microsoft Learn

Overview

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  • Fundamental concepts of AI security

    After completing this module, you'll be able to:

    • Understand and describe the basic concepts of AI security
    • Describe the three layers of AI architecture
    • Describe new, AI specific attack techniques
  • Security controls that you can implement in AI systems to increase the security posture of AI environments

    After completing this module, you'll be able to:

    • Describe security controls for AI systems
    • Understand when these controls should be used
    • Understand the types of attacks these controls mitigate
  • Introduction to AI security testing

    After completing this module, you'll be able to:

    • Describe AI red teaming
    • Understand the three categories of AI red teaming
    • Plan an AI red teaming exercise

Syllabus

  • Fundamentals of AI security
    • Introduction
    • Basic concepts of AI security
    • AI architecture layers
    • AI jailbreaking
    • AI prompt injection
    • AI model manipulation
    • Data exfiltration
    • AI overreliance
    • Module assessment
    • Summary
  • AI security controls
    • Introduction
    • Review AI open-source libraries
    • Content filters
    • Implement AI data security
    • Create metaprompts
    • Ground AI systems
    • Implement application security best practices for AI enabled applications
    • Module assessment
    • Summary
  • Introduction to AI security testing
    • What is AI red teaming?
    • The three categories of AI red teaming
    • Planning AI red teaming
    • Module assessment
    • Summary

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