AI governance is no longer about good intentions. Under the European Banking Authority’s updated outsourcing rules, every model, dataset, and decision pipeline tied to critical functions must now align with concrete oversight demands. If your AI touches outsourced services, the EBA expects documented accountability, demonstrable risk management, and proof of operational resilience—on demand.
The guidelines link AI governance directly to outsourcing risk. This means mapping where your algorithms live, who maintains them, how they evolve, and how failures are caught before they cause damage. It means defining service-level agreements that cover explainability, reproducibility, and exit strategies in case of termination. Each function that relies on AI must be traceable to a responsible party, both internally and with third-party providers.
Compliance under these rules is not just a legal shield. It is a technical architecture challenge. Software teams must build monitoring layers that are auditable. Decision logs must track model changes with timestamps and full metadata. Data flows must be classified, encrypted, and isolated by criticality. The EBA position makes it clear: if you can’t show operational evidence, you don’t have governance.