LLMs memorize training data. Under the right prompts, they reproduce it. Here's how memorization works, how to measure it, and the specific privacy risks in code generation models.
Human red-teaming finds attacks automated evals miss. Automated evals achieve scale humans can't. Here's how to combine them, and what each can and can't tell you.
Training-time alignment is not enough. Production AI systems need runtime layers that detect, intercept, and respond to harmful inputs and outputs. Here's how to build them.
Most safety benchmarks are gameable, distribution-shifted, or measure the wrong thing. Here's what separates a rigorous safety evaluation from a checkbox.