Mind your business: Secure your generative AI application on AWS
summary
Walks through a concrete RAG architecture that uses embeddings, a vector store and Amazon Bedrock for inference and guardrails and shows how documents get chunked, embedded and semantically retrieved into prompts. Recommends explicit controls: prompt templates versioned and permissioned, continuous monitoring for model drift, fine tuning or temperature reduction to curb hallucinations, and Bedrock content filters that can mask or block outputs and register up to three‑word profanity phrases.
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chapters (13)
- 0:00Introduction to AI security
- 0:51Application security controls
- 2:56Generative AI risk layers
- 4:48Architecture vulnerabilities
- 6:54Preventing injection attacks
- 8:06Robust prompt engineering
- 11:18Mitigating hallucinations
- 12:21Securing RAG architectures
- 13:55Managing toxicity and bias
- 15:04Using Amazon Bedrock tools
- 15:35Implementing guardrails
- 16:02Securing external plugins
- 16:33Final summary and best practices
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