Building AI products for regulated industries is "move fast and break things" officially dead?
CEO of Kednus here, we're building AI-native intelligence & compliance tooling, so I live in the tension between shipping fast and answering 200-question security reviews.
Here's what I keep running into: enterprise and regulated buyers (finance, healthcare, gov) LOVE the AI demo, then the procurement process asks where your SOC 2 is, how you isolate tenant data, and whether a human reviews model outputs before they trigger decisions.
So, the classic startup playbook — ship an MVP, iterate in public, bolt on security later basically doesn't work here. Retrofitting compliance is 10x the cost of designing for it.
Questions for this community:
If you're building AI for regulated buyers, did you start compliance work pre-revenue or wait for the first enterprise deal to force it?
Builders in non-regulated spaces: has AI raised the trust bar for you too, or can you still ship fast and apologize later?
Curious where people land on this. My take: in AI, trust IS the product now — the model is increasingly a commodity.
Replies
from my perspective, the winning approach is "move fast with guardrails." I've found that strong documentation, monitoring, and validation actually help me ship with more confidence instead of slowing everything down.
@ludovica_eleazer how do you balance rapid iteration with the level of oversight these industries demand?
If you're targeting enterprise from day one, it might be worth talking to founders who've already gone through SOC 2. We learned a lot just by borrowing their checklist before hiring consultants.
Me thinking about tenant data isolation makes this feel very practical. What architecture choice saved your team the most effort?