I think you've highlighted what is arguably one of the biggest challenge in modern AI security: detection has advanced significantly faster than enforcement. We have seen platforms such as Palo Alto Networks can help close this gap is through the combination of AI-enhanced detection and integrated enforcement capabilities across the wider security architecture. When configured correctly, policies can be applied inline through components such as Prisma Access, Next-Generation Firewalls, Prisma SASE, Browser Security, AI Access Security, and Cortex. This allows certain classes of activity to be inspected, evaluated, and blocked in real time, rather than generating an alert that requires subsequent human intervention. We've seen organisations use Palo Alto's AI-powered security capabilities to automatically block unauthorised AI usage, prevent sensitive data exposure, terminate risky sessions, restrict high-risk agent actions, and enforce DLP policies without requiring a manual review step. When combined with Cortex automation and the broader Palo Alto ecosystem, the time between detection and enforcement can be reduced from minutes to effectively real time for many attack scenarios. That said, there is always a balance to strike. An overly aggressive inline enforcement model can create operational disruption if policies are not sufficiently tuned. The objective is not simply to block faster, but to block accurately. The organisations achieving the best outcomes are typically those that have invested time in policy tuning, data classification, identity context, and risk-based decision making before enabling automated enforcement. In my view, the end state is not AI Runtime Security plus a separate response process. The end state is a security architecture where runtime monitoring, policy evaluation, identity, DLP, and enforcement are tightly integrated, allowing the system to both observe and act. The good news is that the technology to achieve that already exists....