Protego
🤖
Intermediate10 hours estimated

AI Security Engineer

The fastest-growing specialty in security. Learn to identify and mitigate risks unique to AI/ML systems — from prompt injection to model poisoning and AI governance.

📚 4 topic areas🔗 12 curated resources📝 10 quiz questions

What you'll cover

1

LLM Security Fundamentals

Understand the attack surface of Large Language Models: prompt injection, jailbreaks, data leakage, and insecure output handling.

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OWASP Top 10 for LLM Applications
The definitive list of LLM-specific security risks
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MITRE ATLAS (AI Threat Matrix)
Knowledge base of adversarial tactics against AI systems
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Microsoft AI Security Blog
Latest AI security research and threat intelligence
2

AI Red Teaming

Learn how to systematically test AI systems for safety and security failures before adversaries do.

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AI Red Teaming: How to Test AI Systems Security
Practical guide to AI red teaming methodology and techniques
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Microsoft PyRIT — AI Red Teaming Tool
Open-source Python Risk Identification Toolkit for generative AI
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NIST AI RMF (Risk Management Framework)
NIST framework for managing risks in AI systems
3

Model & Training Security

Protect the ML pipeline from data poisoning, model stealing, and backdoor attacks.

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Adversarial Machine Learning — NIST
NIST taxonomy of adversarial ML attacks and defenses
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ML Security Maturity Model
MITRE adversarial ML threat matrix
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Microsoft Security Copilot Guide
How AI security tools are built and how they protect themselves
4

AI Governance & Compliance

Navigate the EU AI Act, NIST AI RMF, and build internal governance for responsible AI deployment.

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EU AI Act Overview
Plain-language guide to the EU AI Act risk tiers
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NIST AI RMF Playbook
Practical guidance for implementing the AI RMF
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Microsoft Responsible AI Standard
Microsoft's public responsible AI principles and practices

Knowledge Check

Path Summary

Level
Intermediate
Estimated time
10 hours
Topics
4
Resources
12
Quiz questions
10
Passing score
70% (7/10)
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