AI Governance Framework for Businesses: What to Include
An AI governance framework gives an organization a repeatable way to manage artificial intelligence across departments, applications and business processes. Rather than treating every AI project as a separate experiment, a framework establishes common expectations for risk, security, privacy, accountability and oversight.
What Is an AI Governance Framework?
An AI governance framework is a structured set of policies, roles, processes and controls used to guide the responsible use of AI. The framework should be proportional to the organization’s size, industry, information and risk profile.
Core Components of an AI Governance Framework
1. AI Governance Policy
Define acceptable and unacceptable AI use, approved tools, information-handling expectations, employee responsibilities and escalation requirements.
2. AI Inventory
Maintain visibility into AI tools, systems and use cases. Knowing where AI is being used is foundational to governance.
3. Risk Classification
Not every AI use case presents the same level of risk. Establish criteria for determining which applications require additional review.
4. Data Governance
Define how organizational, customer and employee information can be used with AI systems. Connect AI governance with existing information protection and data management practices.
5. Security Controls
Identity, access, authentication, endpoint security and monitoring should be considered when deploying AI systems.
6. Privacy
Review whether AI systems process personal or sensitive information and determine appropriate privacy controls and contractual considerations.
7. Human Oversight
Establish where people must review AI outputs or decisions before action is taken.
8. Vendor and Third-Party Risk
AI providers can introduce additional considerations around data handling, security, availability, contracts and service changes.
9. Training and Awareness
Employees need practical guidance about approved AI tools, sensitive information, verification of AI outputs and reporting concerns.
10. Monitoring and Review
Governance should evolve as AI tools, business processes and risks change. Establish a process for periodic review.
Connecting Governance to the Rest of Your AI Program
A framework works best when it is connected to the organization’s broader AI program. Start with an AI readiness assessment to understand current capabilities and gaps. Then use AI strategy consulting to prioritize business opportunities and implementation priorities.
The resulting controls can be developed through AI governance consulting.
AI Governance and Microsoft 365
For organizations using Microsoft 365, governance may intersect with identity, devices, collaboration, email, documents and information protection. Microsoft 365 consulting can help organizations evaluate the technical foundation that supports secure AI adoption.
A Practical Starting Point
- Document current AI use.
- Identify business-critical and higher-risk use cases.
- Establish an initial AI policy.
- Assign governance ownership.
- Connect AI controls to security and data practices.
- Create an approval and review process.
- Train employees and review the framework periodically.
A practical framework should enable responsible adoption rather than create unnecessary friction. The objective is to make AI use more visible, understandable and manageable.
Contact WS Tech Consulting to discuss an AI governance framework for your organization.
Start with our guide to what AI governance means before building a framework.