Project Glasswing and the Next Challenge for Defenders: Turning Faster Discovery into Faster Action

Anthropic’s Project Glasswing has sparked plenty of discussion about what AI might soon do for vulnerability discovery, but the more useful question for most security teams is how to prepare for, and more importantly seize the opportunity of, what comes next. As we wrote in our earlier blog, What Project Glasswing Means for Security Leaders , AI is becoming more capable of finding software flaws. The pressure that follows lands on the teams responsible for deciding what matters, validating risk, assigning ownership, and getting remediation moving across environments that were already hard to manage. We believe that the organizations that will benefit most from the next wave of AI will be the ones that understand their environment well enough to use these emerging AI models with intent, rather than layering them onto immature processes and hoping that speed alone will solve the backlog. What this moment means for security teams The number of publicly tracked software vulnerabilities has broken records almost every year over the last decade, while supply chain risk has continued to rise. Most teams were already feeling the strain of more findings than they could process cleanly. The Common Vulnerabilities and Exposures (CVE) program, the standard system for identifying and tracking known vulnerabilities, recorded 48,185 disclosures in 2025, a 20% increase over 2024, with roughly 40% of those disclosed vulnerabilities rated high or critical. The pace in 2026 was already working out to hundreds of new CVEs per day when those figures were cited. That tells you something important about the current environment: the challenge has not necessarily been a lack of findings, but instead converting a growing stream of findings into measurable risk reduction. The reality is that very few organizations are going to hand a model free rein over their most sensitive environments the minute those capabilities become more widely available. Trust will be built in stages: early adoption is much more likely to focus on backlog reduction, triage support, patch testing, and repetitive lower-tier remediation work that consumes time without carrying the same level of operational risk as the most critical systems in the business. That is a more realistic starting point, and it leads to a more useful question. Before teams apply AI more broadly, they need to understand their environment well enough to use it intentionally. Establish the foundation before layering in AI The promise from Project Glasswing and almost every other AI-powered security initiative is quite similar: leverage AI to identify patterns, summarize risk, suggest fixes, and speed up repetitive work. Regardless of technology, success still depends on how well an organization understands its environment, the context around each finding, and the process used to act on it. A model can generate more output than a team ever could on its own, but that output becomes noise if the organization cannot answer basic qu