An effective MVG framework isn’t a single document; it’s an integrated system resting on four critical pillars. Neglect any one, and the structure collapses.

1. Policy Pillar: The “What” and “Why” — Setting the Rules of the Road
2. Purpose: Defines the organization’s binding commitments, standards, and expectations for responsible AI development, deployment, and use.
3. Core Components:
4. Risk Classification Schema: A clear system for categorizing AI applications based on potential impact (e.g., High-Risk: Hiring, Credit Scoring, Critical Infrastructure; Medium-Risk: Internal Process Automation; Low-Risk: Basic Chatbots). This dictates the level of governance scrutiny. (e.g., Align with NIST AI RMF or EU AI Act categories).
5. Core Mandatory Requirements: Specific, non-negotiable obligations applicable to all AI projects. Examples:
6. Human Oversight: Define acceptable levels of human-in-the-loop, on-the-loop, or review for different risk classes.
7. Fairness & Bias Mitigation: Requirements for impact assessments, testing metrics (e.g., demographic parity difference, equal opportunity difference), and mitigation steps.
8. Transparency & Explainability: Minimum standards for model documentation (e.g., datasheets, model cards), user notifications, and explainability techniques required based on risk.
9. Robustness, Safety & Security: Requirements for adversarial testing, accuracy thresholds, drift monitoring, and secure

Read More: From Principles to Playbook: Build an AI-Governance Framework in 30 Days

About Us:

Meet Nate Patel — a dedicated AI strategist and consultant guiding enterprises through ethical AI adoption, digital transformation, and innovation. Learn about his vision, experience, and how he helps organizations align AI with values and outcomes.