The emergence of 'Frontier Models'—AI systems with capabilities at the absolute edge of machine intelligence—has fundamentally altered the Cyber Resilience Agenda. These models, often referred to in intelligence circles as the 'Mythos' layer, are no longer mere assistants; they are becoming the primary architects of both offensive and defensive operations. For the modern CISO, the influence of Mythos is felt in the collapse of the 'Exploit Gap.' Historically, the time between a vulnerability's discovery and its weaponization allowed for a defensive buffer. Frontier models have reduced this gap to near-zero by automating the 'sinkholing' of APIs and the generation of polymorphic shellcode that evades signature-based detection. However, the Mythos impact is not solely destructive. Frontier models are also being integrated into 'Autonomous Defense' systems that can perform real-time code auditing and automated micro-patching. The challenge lies in the 'Frontier Paradox': the same model that identifies a critical flaw in a legacy ERP system can also generate the exploit to subvert it. This creates a state of permanent architectural tension. Resilience in the Mythos era requires a shift from 'Static Defense' to 'Dynamic Integrity.' This involves the use of AI-driven 'Digital Twins' to simulate attacks in real-time and the implementation of 'Moving Target Defense' (MTD) strategies that constantly shift the attack surface. Furthermore, the theft of source code from major security vendors like Trellix suggests that threat actors are actively seeking to 'fine-tune' their own frontier models on proprietary defensive logic. This 'AI-on-AI' warfare means that the security of the training data and the integrity of the model weights are now as critical as the security of the production network. The resilience agenda must now include 'Model Sanitization' and 'Adversarial Robustness' as core pillars. **Call to Action for CISOs:** 1. **Adopt AI-Augmented Patching:** Move beyond manual review; implement automated patch validation pipelines that use LLMs to verify fix integrity. 2. **Secure the AI Supply Chain:** Audit all third-party AI integrations for 'Prompt Injection' and 'Data Poisoning' risks. 3. **Pivot to Behavioral Baselines:** Since AI-generated exploits often bypass signatures, focus on high-fidelity behavioral telemetry to detect anomalous API calls and token usage. 4. **Red-Team the Models:** Conduct adversarial testing against your own defensive AI to identify blind spots in its logic.