AI will find vulnerabilities at an unprecedented rate. The real crisis will be the organizational inability to patch them, especially in critical infrastructure with long update cycles and unsupported software where original developers are long gone. The problem shifts from finding flaws to fixing them at scale.

As AI models become adept at finding software vulnerabilities, there's a limited time for companies to use these tools defensively. This brief "catch-up" period exists before these powerful capabilities become widely available to malicious actors, creating an urgent, time-boxed need for proactive patching of legacy systems.

Palo Alto Networks' CEO explains that AI tools are discovering software vulnerabilities at an unprecedented rate. This will cause a short-term deluge of patches, but it's effectively cleaning up years of bad code and will ultimately strengthen the entire ecosystem.

The rise of AI-powered attacks means vulnerabilities are exploited in minutes. Palo Alto Networks responded by developing a capability to deliver and deploy patches to all customers in just four hours, a massive reduction from the industry average of 55 days, fundamentally changing incident response time.

AI models have solved vulnerability discovery so effectively they've exposed a new, larger bottleneck: remediation. With projects like Glasswing reporting a 10-to-1 ratio of bugs found to bugs fixed, the industry's challenge has rapidly shifted from finding flaws to having the human capacity to patch an overwhelming number of them.

Frontier AI models are dramatically reducing the time it takes for a newly discovered software vulnerability to be turned into a functional exploit. This acceleration means traditional, onerous patching cycles are no longer viable. Organizations must find new ways to patch systems almost immediately, as exploits can appear within hours of a vulnerability's announcement.

The plummeting cost of finding exploits via AI models means enterprises cannot simply patch vulnerabilities reactively. The necessary strategic shift is to build foundational security controls for each asset class, including a new, dedicated security layer specifically for the AI stack.

AI models are better at finding bad code than writing good code. This capability will rapidly uncover vulnerabilities in open-source, custom, and vendor software that would have otherwise taken 10 years to find. This creates an urgent, large-scale need for patching across all industries.

The traditional cybersecurity model of humans finding and patching vulnerabilities cannot keep pace with AI that discovers thousands of exploits in hours. This fundamental mismatch in speed and scale will require a complete overhaul of how software security is managed.