Beyond the Black Box: Interpretability as AI Security's Missing Layer

About this session

The strongest signal of attacks against AI models, like jailbreaks and prompt injections, isn't in the inputs or outputs but in the activations between them. AI security today treats models as black boxes, layering guardrails around systems that no one fully understands. The talk will show how mechanistic interpretability changes that equation, giving defenders the same internal visibility that EDR brought to endpoints over a decade ago. Detecting attacks at the activation level produces guardrails that match or exceed purpose-built classifiers while running faster and lighter. If you're building or defending AI systems and want to move past black-box assumptions, this talk will give you a new playbook.