Securing AI at Scale: Practical Defenses against Prompt Injection, Adversarial Attacks, and Model Poisoning

I will share a practical perspective on the newest ways to protect systems, such as filtering out harmful input, keeping system prompts separate, and using sandboxing to prevent prompt injection; training models to handle tricky situations, adding randomness for better results, and cleaning up generated outputs for model performance; and tracking data sources, spotting unusual activities, and using federated learning to prevent model poisoning. I’ll discuss how we integrate these controls into our SRE and incident response workflows and how layered, zero-trust architectures and continuous adversarial testing have become essential for maintaining both reliability and trust in AI-driven services.