u/Choiboy11

Running internal LLMs for a few use cases and the security team is flagging prompt injection as a top risk. Attacker sends a crafted input that overrides the model's instructions. It's not theoretical, it's being actively exploited.

Check Point has prompt injection defense built into their AI Factory Security Blueprint, designed for orgs running AI infrastructure at scale. They do it at the infrastructure layer via integration with NVIDIA BlueField hardware so it doesn't eat into your GPU cycles. Protect AI and Lakera are also decent names in this space.

This is a genuinely new attack surface and most traditional security tools aren't built for it. What's your AI security stack looking like?

For those of you who’ve added dashboards to your app for your users/customers, did you build it in-house or use some kind of platform? We’ve been debating and it’s turning into a whole thing. On one hand, building it ourselves gives us full control and will fit our designs perfectly. On the other, our engineers are already drowning in other priorities and I’m worried it’ll just drag on forever. A tool might save time but embedding your BI tool feels like it would suck for UX and load slow.

If you’ve gone through this, how did you decide? And if you went with a platform, did it actually feel like part of your product or was it obviously bolted on?