Topic

AI-Governance-Controls

AI governance controls are the technical, organizational, and procedural mechanisms that organizations implement to manage, monitor, and enforce compliance with AI policies and regulations. These controls span model development, deployment, monitoring, and retirement phases, ensuring AI systems operate within defined risk parameters and align with legal and ethical requirements. Effective governance controls are critical for enterprises to mitigate bias, ensure transparency, maintain data security, and demonstrate accountability to regulators and stakeholders.

1 item

ResearchGlobal2026-06-02

DDMI's GRC-Layered AI Approval Model Offers a Replicable Blueprint for Enterprise Governance Programs

A case study published by Dataversity details how DDMI built an operational AI governance program by embedding AI approval checkpoints directly into existing data governance and GRC workflows. The organization evaluates each AI request by both use case and product, uses a GRC tool to create traceable coordination records, and applies guardrails covering legal compliance, security, accountability, monitoring, training, and data location. The model is presented as a replicable pattern for enterprises that already have GRC infrastructure but lack formal AI decision rights.

operating-model GRC-integration approval-workflow AI-governance-controls decision-rights