Google and others warn that this trend will continue. If rapid AI-assisted discovery and exploitation keep accelerating, defenders cannot afford “unprioritized mass-patching” as in the past. Instead, they must move to a threat-intelligence-driven triage model: focus on the vulnerabilities actually being targeted, and automate as much response as possible. This means continuous scanning, asset inventory and risk-based prioritization — often called exposure management using tools like Wiz, Tenable and Microsoft Defender for Cloud. For example, Security Boulevard explains that an exposure management program uses AI to constantly map assets, vulnerabilities and misconfigurations, then automatically prioritize and remediate the riskiest ones. This approach is essentially a “Mythos-ready” security program — one designed to handle the avalanche of findings that AI will generate, and to fix them at machine speed. AI, essentially battling AI
Using AI defensively
Importantly, defenders can use AI as well as attackers, so it benefits both sides of the coin. Anthropic’s own Project Glasswing is a case in point, as seen earlier. In early April 2026, only ~50 organizations were testing Mythos Preview, but within weeks they had already run it across their codebases and uncovered over 10,000 high- or critical-severity flaws. Anthropic is now expanding Glasswing to ~150 additional organizations deemed critical (power, healthcare, communications, etc.) whose code impacts millions of users. The aim is to train industry on new AI-powered workflows: Partners share best practices, triage vulnerabilities together and even use AI to suggest fixes and pre-emptively harden code.
Glasswing’s lessons show that AI can speed up defensive tasks too. For example, Mythos-level models can be repurposed to write patches or convert legacy code into memory-safe languages, and to run “AI red teams” against one’s own systems. Anthropic reports that partners are now using Mythos to automatically generate fixes, and their new public tool (Claude Security) helps scan code and suggest patches using a slightly safer variant of Mythos. In practice, organizations should consider adding AI to their security toolchain and leverage automated penetration testing, intelligent asset discovery and tagging, and AI-assisted patch generation to help keep pace with attackers.
