we have genAI workflows in prod across engineering and sales, but guardrails are messy.
we’ve tried a few approaches. some catch obvious issues but don’t actually stop risky behavior in real time. others are too aggressive and end up blocking normal usage or adding noticeable latency.
the biggest problem is balancing control vs usability. once guardrails start interfering with everyday workflows, people work around them or disable them entirely.
we’ve also seen gaps with things like embedded models in tools or indirect usage paths that don’t go through a single control point.
management wants something that can prevent sensitive data from being exposed through prompts, without slowing everything down or breaking how teams use AI day to day.
what’s actually working for you at scale? how are you enforcing guardrails in a way that holds up under real usage without disrupting workflows