Generative AI Security — A Practical Guide to Securing Your AI Application
summary
Generative AI apps follow five scopes (consumer → self‑trained) and most real projects sit in scopes 3–4 using foundation models plus fine‑tuning; concrete mitigations include prompt engineering with XML delimiters, content‑moderation classifiers, RAG for current facts, sequential input/output guardrails, and red‑team testing against prompt injections. Use FM‑Eval benchmarks for offline checks, OpenTelemetry for observability, and AWS tooling — SageMaker, Amazon Bedrock, Amazon Q — for deployment and runtime controls.
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chapters (14)
- 0:00Introduction to AI security
- 2:13Responsible AI fundamentals
- 5:44From idea to production
- 10:25Top risks in generative AI
- 12:30Mitre ATT&CK for AI
- 16:01Prompt injection defenses
- 18:53Content moderation techniques
- 21:41Guardrails for AI apps
- 27:02Testing with red teaming
- 31:46Evaluating model performance
- 33:11Advanced prompt injection
- 35:17Observability in AI systems
- 36:20AWS AI security tools
- 38:05Q&A session
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