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.

The foundational design of payment systems prioritized ease of adoption by widely distributing theoretically secret information, like credit card and bank account numbers. This decision created a permanent security vulnerability that has required decades of reactive, add-on security measures.

Low-code platforms have a massive opportunity to solve a decades-old security challenge by embedding "secure by default" guardrails. The key is transforming security from a technical hurdle into a configurable UI problem, making it digestible and manageable for the non-technical users who now build applications.

AI leaders aren't ignoring risks because they're malicious, but because they are trapped in a high-stakes competitive race. This "code red" environment incentivizes patching safety issues case-by-case rather than fundamentally re-architecting AI systems to be safe by construction.

For decades, software has contained vulnerabilities manageable only due to a limited number of human attackers. AI allows any individual to spin up hundreds of qualified "attackers" instantly, creating a massive force that will systematically exploit this historical security debt, leading to widespread chaos.

The emergence of AI that can easily expose software vulnerabilities may end the era of rapid, security-last development ('vibe coding'). Companies will be forced to shift resources, potentially spending over 50% of their token budgets on hardening systems before shipping products.

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.