AI governance is now an engineering backlog
2026 is when policy meets production. Deletion, backup, and auditability need real implementation, not just documentation.
Most companies wrote AI policies last year. This year they are discovering that the hard parts live in the stack.
Three places teams get stuck
1. Data deletion and backups. Recent IMY and EDPB reviews keep finding gaps in erasure and retention. If you cannot show what was deleted and when, the policy does not protect you.
2. Traceability. You need to know which model or prompt produced a decision and who approved it. Logs must be tied to business context, not only infrastructure.
3. Change control. When a model, prompt, or data source changes, it should follow the same review path as code.
How we help
We turn governance into concrete tickets: data maps, retention rules, audit logs, and approval flows for sensitive actions. That keeps you compliant without slowing teams to a halt.
If you want to see where your stack is exposed, we can do a short review and deliver a prioritized backlog.
Related services
Services that match this topic
If you want help with this, these are the teams that build it.
