The breach of Mistral AI and the rise of 'Mythos' class models have forced a radical rethink of AI security. 'Frontier Model Defense' is no longer just about protecting the API; it is about protecting the entire lifecycle of the model, from training data to weights. Defenders must now leverage AI to fight AI. This includes 'Adversarial Training,' where models are intentionally exposed to malicious prompts during development to build 'neural immunity.' Furthermore, we are seeing the emergence of 'Model Watermarking,' which allows developers to track the unauthorized use of their weights. However, the most critical defense is 'Architectural Isolation.' Frontier models should be developed in 'Air-Gapped Neural Vaults' where the training environment has no direct path to the internet. The Mistral incident shows that even the most advanced AI companies are vulnerable to traditional 'Identity-Based' attacks. Therefore, the defense of the model is inextricably linked to the defense of the developer. We recommend the use of 'Confidential Computing' (e.g., Intel SGX or AWS Nitro) to ensure that model weights are never decrypted in system memory, even during inference. This prevents a compromised OS from 'scraping' the model from RAM. As we move toward 2027, the 'Neural Perimeter' will be the most contested space in the cyber domain. Organizations that fail to treat their models as 'High-Value Assets' (HVAs) will find themselves on the wrong side of the AI divide.