AI Security & Red Teaming
Understand AI security from both the attacker's and defender's perspective. This path covers the major attack vectors against AI systems — prompt injection, data poisoning, model theft, and adversarial attacks — along with defensive strategies and red teaming methodologies.
What You'll Learn
- Understand the AI security threat landscape
- Identify and exploit prompt injection vulnerabilities
- Detect and prevent data poisoning attacks
- Defend against model extraction and theft
- Conduct structured AI red team exercises
- Build security testing into AI development pipelines
- Implement defense-in-depth strategies for AI systems
Course Lessons
The AI Security Threat Landscape
18 min readMap the attack surface of AI systems — from training data to inference, understanding where AI systems are vulnerable and what attackers target.
Prompt Injection: Attack and Defense
22 min readDeep dive into prompt injection — direct injection, indirect injection through data sources, and defense strategies that actually work.
Data Poisoning and Training-Time Attacks
20 min readUnderstand how attackers can corrupt training data to introduce backdoors, biases, or vulnerabilities into AI models.
Model Extraction and Intellectual Property Theft
18 min readLearn how models can be stolen through API access, and defensive measures including rate limiting, output perturbation, and watermarking.
Conducting AI Red Team Exercises
22 min readPlan and execute structured red team exercises for AI systems — scope definition, attack planning, execution, reporting, and remediation.
Defense in Depth for AI Systems
18 min readBuild layered security for AI applications — input validation, output filtering, monitoring, access controls, and incident response procedures.
Security Testing in AI CI/CD Pipelines
15 min readIntegrate automated security testing into your AI development pipeline — adversarial testing, regression checks, and continuous red teaming.
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