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Phase 19: AI Safety & Red Teaming — Start Here¶

Build trustworthy AI — understand prompt injection, jailbreaks, PII leakage, bias, and how to test your systems for failure modes before they go live.

Why AI Safety Matters¶

LLMs can be manipulated, can leak private data, perpetuate bias, and generate harmful content. Understanding these failure modes is essential for production AI.

Notebooks in This Phase¶

Notebook	Topic
`01_prompt_security.ipynb`	Prompt injection, jailbreaks, defense strategies
`02_content_moderation.ipynb`	Detect and filter harmful outputs
`03_pii_privacy.ipynb`	PII detection, data anonymization, privacy
`04_bias_fairness.ipynb`	Measure and mitigate model bias
`05_red_teaming.ipynb`	Systematic adversarial testing of AI systems

Key Threat Categories¶

Threat	Description	Defense
Prompt injection	User hijacks system prompt	Input validation, sandboxing
Jailbreaking	Bypassing safety guidelines	Robust RLHF, output filtering
PII leakage	Model reveals training data	Differential privacy, data governance
Bias	Unfair outputs across groups	Diverse training data, fairness metrics
Hallucination	Confident false answers	RAG, uncertainty quantification

Prerequisites¶

Prompt Engineering (Phase 11)
Model Evaluation (Phase 16)

Learning Path¶

01_prompt_security.ipynb         ← Start here — most common threat
02_content_moderation.ipynb
03_pii_privacy.ipynb
04_bias_fairness.ipynb
05_red_teaming.ipynb             ← Advanced: systematic testing