AI in Governance

AI Governance companies help organizations inventory models, document risk, enforce policies, monitor drift or incidents, and evidence controls for internal audit and regulators—distinct from teams that only train or serve models.

As enterprises deploy more models, governance becomes the control plane. Buyers confuse MLOps with governance; Brel separates policy/assurance products when materials support that claim.

• Incomplete model inventories
• Unclear ownership across risk, legal, and engineering
• Mapping controls to frameworks (e.g., NIST AI RMF)
• Third-party model risk

Adoption rises with regulated industries and board-level AI policies. Tools succeed when integrated into existing GRC and MLOps workflows rather than standalone checklists.

Is AI Governance the same as Responsible AI?

Responsible AI is the practice and principles; governance is the operating system of policies, roles, and controls. Many products support both.

• NIST AI Risk Management Framework
• EU AI Act overview