Anthropic's Mythos Preview model has uncovered thousands of zero-day vulnerabilities, including a 27-year-old OpenBSD bug, demonstrating advanced exploit chaining capabilities. This rapid discovery pace highlights a critical imbalance, as defenders struggle to keep up; exploited vulnerabilities often show activity before disclosure. AI further compresses this exploitation window, accelerating attackers and overwhelming security teams with disclosures faster than they can triage. Current remediation efforts are insufficient, with developers spending significant time on post-release fixes and organizations facing year-long backlogs for critical vulnerabilities. AI-generated code exacerbates this issue, introducing a deluge of new security findings and increasing vulnerabilities due to its inherent flaws. To counter this, defenders must integrate frontier AI models directly into their development pipelines, enforcing security policies at every merge request. This involves catching simple issues early in the IDE, automating triage of complex findings, and governing AI-generated fixes through established processes. A robust security pipeline ensures that questions about exposure can be answered in minutes, and remediation campaigns can be executed efficiently with automated patch generation and policy enforcement. Such a system provides a clear audit trail for compliance, demonstrating how policies are applied and risks are mitigated. With advanced AI threats imminent, organizations must proactively address pipeline gaps to strengthen their software supply chain.