FlowSharp adopts a AI with governance approach natively: AI is not a layer added on top of the process but operates inside the process with declared rules, permissions and controls.
AI governance mechanisms:
Data Shield — protects sensitive data at field level. Data marked as sensitive is never exposed to AI models, even when the AI task operates on the same item. Sensitivity follows the data wherever it is copied or transferred.
Data Gate — explicit checkpoints in the flow that block advancement until certain data or conditions are met. Prevents cases from proceeding on incomplete or unverified bases.
Native human-in-the-loop — AI tasks can require human confirmation before producing effects. The operator sees the AI output, evaluates it and decides whether to proceed, correct or escalate.
Roles and permissions — every AI action is configured with the role that authorises it. A model cannot access data or perform actions outside the declared perimeter.
Complete audit — every AI output, every confirmation and every deviation is recorded in the item's timeline with timestamp, responsible party and action detail.
This approach allows AI to analyse, classify, suggest and assist without losing operational control or traceability.