Every major technology wave follows the same script. Capability arrives. Infrastructure lags. The gap between the two is where things break.
The internet gave us e-commerce before it gave us SSL. Mobile gave us banking apps before it gave us biometric authentication. We shipped first, secured second, and spent years cleaning up the mess.
AI is doing the same thing, except the surface area for harm is wider and the timeline is shorter. LLMs are being wired into live customer data, internal knowledge bases, and tool-calling APIs faster than the security thinking around them can keep up. Most of the teams doing this are not reckless. They are just moving at the speed the market demands, without the infrastructure to match.
That gap is why I am building Koreshield.
What actually concerns me
The problem is not that these models are too powerful (not yet), but the environments we are dropping them into are not ready for them.
Prompt injection is still the top vulnerability in OWASP's LLM Top 10. Fewer than 35% of organisations have deployed dedicated LLM defences. This is not an advanced research territory. We can see it playing out in CRM tools, HR systems, and customer-facing assistants that handle genuinely sensitive data every single day.
In regulated industries, the consequences are not abstract. A misconfigured AI assistant touching patient records is a GDPR / HIPAA incident. An over-privileged agent with API access to a payment system is a fraud vector. I promise that these are not hypothetical scenarios. They are already being documented.
Most teams do not have the bandwidth or the specialised expertise to build proper runtime enforcement themselves. So they ship without it, and hope nothing surfaces.
Why 'firewall' and not 'guardrails'
Guardrails nudge you when you drift. Useful, if the problem is occasional.
A firewall enforces. It sits at the boundary, inspects what passes through, and blocks what should not cross. That is a fundamentally different design posture, and in my view, the right one for LLM security.
At Koreshield, we sit as an OpenAI-compatible proxy between an application and its LLM. Every prompt is inspected before it reaches the model. Every response is checked before it returns to the user. The overhead is under 50ms, negligible against actual model inference time.
The design is deliberate: no code rewrites, no lengthy deployment cycle. Point your application at us instead of the model provider directly, and you have runtime enforcement live in under thirty minutes. That matters because the organisations that need this most are moving fast, not sitting through six-month security review processes.
What success looks like in 2035
In ten years, I want it to be genuinely obvious, not something security teams have to argue for, that shipping an AI feature without runtime protection is like deploying a web application without HTTPS.
Not a debate. Not a trade-off conversation between security and velocity. Just clearly, visibly incomplete.
The patterns we are documenting now should be what product teams learn in onboarding, the same way SQL injection is taught today. Known. Named. Something you simply defend against by default.
This decade is when that infrastructure gets built. I would rather be building it.