How we shipped a secure AI feature without slowing the product down

When our support team asked for an assistant that could summarize customer conversations, the first instinct was to start with the model. We started somewhere less exciting: mapping what the assistant must never see, infer, or send back.

The team drew a narrow trust boundary around the feature, added redaction before retrieval, and wrote twelve failure cases before the first prototype reached a real user. That work did not slow the launch. It made the decision surface small enough for everyone to understand.

Our final review was a 25-minute ritual at the end of each sprint. Product, engineering, and security recorded one decision, one residual risk, and one owner. The format was deliberately lightweight so it could survive a busy week.

“The best systems make the safe path the easy path.”