AI is breaking traditional security models — Here’s where they fail first

Traditional enterprise security relied on slow, manual processes where vulnerabilities were discovered through periodic scans, then triaged and fixed in a delayed workflow. AI and LLM-based systems are breaking this model by automating triage (the process of sorting and prioritizing findings), delivering vulnerabilities with full context and demanding immediate action, which forces organizations to rethink who is responsible for fixes and how quickly decisions happen. This shift also makes accountability explicit rather than implicit, requiring security teams to transition from handling individual findings to overseeing AI decision-making accuracy and approving exceptions.