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Framework Library

AI Governance

Governing artificial intelligence responsibly across the enterprise

2 min read

Framework application path

From institutional understanding to disciplined, measurable application.

  1. 01

    Context

    Define the institutional need and the decision this framework must support.

  2. 02

    Architecture

    Understand the components and relationships that form the integrated model.

  3. 03

    Application

    Translate the model into executable roles, mechanisms and operating rhythms.

  4. 04

    Evidence

    Identify success indicators and the early signals that require intervention.

  5. 05

    Executive review

    Test progress and connect the findings to the relevant institutional assessment.

AI adoption without governance is not innovation — it is unmanaged risk.

This playbook installs the committee, policies, controls and review cadence that let an organization use AI boldly while staying responsible, compliant and trusted.

The AI governance components

Seven components make AI safe to scale.

AI Committee

Cross-functional oversight with a clear mandate.

Principles & Ethics

Fairness, transparency, privacy and human oversight.

Use-Case Approval

Risk-tiered intake, review and sign-off.

Data & Model Controls

Provenance, quality, security and monitoring.

Human Oversight

Where a human must stay in the loop.

Compliance

Regulatory alignment and audit trails.

Monitoring & Review

Drift, incidents and periodic reassessment.

Govern AI to use it boldly.

Risk-tiered approval

Productivity tools — lightweight approval, standard controls.

Customer or operational impact — committee review and monitoring.

Decisions affecting people or safety — executive sign-off, human oversight, audit.