The Vulnerability Management Process is Broken, and AI Has Widened the Gap
Vulnerability management teams are increasingly overwhelmed by security alert backlogs which can often number in the millions. The growth of alert backlogs is driven both by more alerts, as well as the limitations of existing vulnerability management programs in assessing and triaging the alerts.
Autonomous AI models can now find flaws, weaponize them, and exploit them at a scale and speed no human team can match. Advanced reasoning models such as Anthropic Claude Mythos and OpenAI Daybreak can surface critical vulnerabilities buried in decades-old code that eluded researchers for years, and bridge the gap from discovery to a working exploit in a single afternoon. Threat actors deploy these capabilities as 24/7 bots that scan and target low, medium, and high severity weaknesses continuously. The periodic, human-speed patching cycle is effectively dead.
These dynamics compound the challenges vulnerability management teams already face when trying to identify and fix risk:
- Most employ manual assessment processes that can only scale linearly by adding more analysts.
- They struggle to automate prioritization based on actual risk and specific business impact, and to justify which finding is a priority to fix.
- Assigning remediation responsibility is a guessing game, especially in complex organizations. Teams cannot standardize processes without a consolidated approach across tools and centralized collaboration across remediation workflows.
- AI-driven discovery is now adding vulnerabilities to the backlog faster than teams can triage them, turning volume into an existential problem rather than an operational one.
Download the white paper to learn more about how security teams can fix the vulnerability management process to reduce risk.
