Hands-on Practice
summary
Practical checklist for securing AI systems that names concrete threats and fixes: shows how adversarial attacks manipulate training data or model outputs, documents real prompt-injection and chatbot data-leak incidents, and warns that model theft can be a silent breach. Lists four practical defenses: secure development practices, input validation with zero-trust access control, rotating credentials plus comprehensive logging, and regular red‑team/pen testing and security assessments to catch vulnerabilities before deployment.
AI-written summary · about
chapters (6)
- 0:00Introduction to session
- 2:22AI security vs AI insecurity
- 6:24Data and model attacks
- 10:23Vulnerabilities and risks
- 13:39Defensive strategies
- 15:54Security-first mindset
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