The Hidden Risks of Putting AI in Front of Your Customers

When leadership asks for AI features in the customer experience, security teams often get brought in late, after the prototype is built and the demo has already impressed the board. That's when the hard questions surface: What can this system actually say? Who's responsible when it hallucinates? How do we know it won't expose data it shouldn't?

Public-facing AI introduces risks that internal tools simply don't. Unlike deterministic software, generative AI can produce different outputs for the same input. A chatbot might confidently recommend the wrong product, a support agent might invent information it doesn't have, recommendations might be biased, and a customer-facing RAG system might surface data from the wrong tenant. In regulated industries (banking, insurance, healthcare) the stakes are immediate. One bad response can trigger compliance issues, reputational damage, or worse.

From my experience building customer-facing AI across fintech, healthcare, and ecommerce, three governance gaps consistently cause problems. First, quality ownership is unclear. When everyone can prototype with AI tools, who owns the guardrails? Product, engineering, security, and support all need defined roles, especially for monitoring non-deterministic behaviour in production. Second, internal workflows are misaligned. AI that spans support, marketing, and operations often exposes data silos and process gaps into the open. Your customer wants to talk to your organisation, not just to marketing or support - align the data. Third, speed pressure bypasses security. Teams racing to launch "AI-powered" experiences skip the evals, monitoring, and feedback loops that catch failures before customers do.

Critically, public-facing AI experiences need to be built less as projects and more as continuous operational cycles. Feedback from real usage must flow continuously into the build cycle; it never stops. The fix isn't to slow down: it's to build security into that lifecycle from day one. Assign clear ownership for AI behaviour, implement continuous monitoring for non-deterministic outputs, and align cross-functional teams before the first customer-facing release. The organisations that get this right can move fast without breaking trust. Those that don't, risk costly rollbacks and brand damage that often create more problems than they solve.