Organizations often begin deploying AI before establishing a formal process for governing it. Employees may use generative AI tools, business teams may purchase software with built-in AI capabilities, and technology teams may develop AI applications internally. As AI use expands, organizations need a consistent way to understand where AI is being used, who is responsible for it, what risks it presents, and what level of oversight is appropriate.
What is AI Governance?
The primary focus of AI governance is to set up the policies, roles, processes, and controls an organization uses to identify, assess, approve, monitor, and manage AI within its environment. Building an enterprise AI governance framework brings these elements together into a formal process for managing AI across the organization.
The NIST AI Risk Management Framework (AI RMF) provides a useful foundation for developing this approach. Its four functions, Govern, Map, Measure, and Manage, outline a structured way to address AI risk throughout the AI lifecycle. Understanding the framework, however, is only the starting point. Organizations still need to determine how those functions will work within their own environment.
How to Implement AI governance?
So, how can organizations put an AI governance framework into practice? A practical way to build an AI governance program is to follow these four stages: setting up governance and accountability, identifying and assessing AI use, applying risk-based controls, and maintaining the program over time. These stages give organizations a practical path for establishing how AI is being used, addressing identified risks, and keeping the program current as the organization’s AI environment changes.
Stage #1: Establishing Governance & Accountability
Build: Governance structure, roles, decision-making authority, and foundational policies.
The first stage is determining who is responsible for AI governance and how AI-related decisions will be made.
This is where the NIST AI RMF’s Govern function provides a great starting point for how AI should be governed across departments. It outlines the policies, processes, roles, and practices needed to manage AI risk and promote clear accountability. This does not necessarily require creating a new organizational function. In many cases, AI governance can be integrated into existing privacy, security, compliance, risk, technology, and business processes.
An enterprise governance structure should define:
- Who oversees the overall program
- Who owns individual AI systems or use cases
- Who participates in reviews
- Who can approve different levels of risk
- When issues must be escalated
Responsibilities should also be clear across functions. For example:
- Business owners are responsible for the purpose and use of an AI system.
- Technology teams may oversee implementation and technical controls.
- Privacy evaluates the use of personal information and related privacy requirements.
- Security evaluates security risks and safeguards.
- Legal and Compliance assess applicable legal, regulatory, and contractual requirements.
- AI governance leadership coordinates oversight and makes or escalates decisions based on established criteria.
The exact structure will vary by organization, but responsibilities and decision-making authority should be documented rather than assumed.
Organizations should also formalize AI governance policies that set basic expectations for how AI should be utilized. Depending on the organization, these may address acceptable and prohibited uses, data handling, human oversight, privacy and security requirements, third-party AI, approval requirements, and incident reporting.
Together, these elements establish the accountability needed to govern AI consistently rather than on a case-by-case basis.
Stage #2: Identify & Assess AI Use
Build: AI inventory and a standardized AI use-case assessment process.
The next stage is gaining visibility into where AI is used, how it functions, and which cases require additional review.
This aligns with the NIST AI RMF’s Map function, which focuses on assessing the context in which an AI system operates and identifying relevant risks, impacts, and limitations. Applying this approach gives organizations a clear picture of their AI landscape before determining the requirements that should apply to each use case.
Building an AI Inventory
An organization cannot effectively govern AI it does not know about. An AI inventory provides a centralized view of the organization’s AI use, including:
- Employee use of AI tools
- AI-enabled enterprise software
- Validating AI outputs
- Internally developed AI applications
- AI provided through third-party solutions
The inventory should capture enough information to understand each use case, including its purpose, owner, vendor or developer, data involved, users or affected individuals, and level of human involvement.
The goal is not to create a perfect inventory on day one. It is to create a baseline that can adapt as new AI systems and use cases emerge.
Assess AI Use Cases
The inventory provides visibility into existing AI activity, but governance also needs to account for AI use cases that have not yet been introduced. Organizations need a consistent process for evaluating new AI use cases before they are adopted or deployed.
The level of review can be based on factors such as data sensitivity, potential impact on individuals, level of automation, business criticality, and regulatory requirements.
For example, an internal tool used to summarize public information may require limited review, while an AI system that processes sensitive employee information or supports hiring decisions may require additional privacy, legal, security, or compliance review.
This risk-based approach helps organizations apply an appropriate level of governance without subjecting every AI use case to the same process.
Stage #3: Apply Risk-Based Controls
Build: Risk-based requirements and controls that can be incorporated into existing business and technology processes.
Once an AI use case has been assessed, the organization can translate the identified risks and requirements into specific conditions for how the system can be developed, implemented, and operated.
This builds on the NIST AI RMF’s Measure and Manage functions. Measure supports the evaluation and monitoring of AI risks, while Manage focuses on the prioritization of those risks and determines how they should be handled. Together, these functions help translate risk findings into appropriate actions.
Controls should correspond to the nature of the use case and the assessment findings. Depending on the circumstances, these may include:
- Data access and handling restrictions
- Security and access controls
- Testing and validation requirements
- Human review requirements
- Documentation requirements
- Vendor or third-party requirements
- Monitoring and reporting requirements
Higher-risk use cases may require additional conditions before deployment, such as remediation of identified issues, additional testing, documented human review, or approval from designated stakeholders.
Stage #4: Maintain the Program
Build: A repeatable operating cadence for policies, issues, exceptions, training, and leadership reporting.
AI governance requires ongoing management rather than a one-time implementation. As the organization’s AI environment changes, governance activities need to remain operational and aligned with emerging business needs.
Organizations should establish a regular cadence for overseeing the governance program, with responsibilities assigned for:
- Maintaining policies
- Tracking open issues and exceptions
- Managing incidents
- Reporting relevant information to leadership
AI use cases and their associated controls should be reviewed periodically to confirm that they remain appropriate as systems, data, business processes, and applicable requirements change. A reassessment may be appropriate when an AI system is materially modified, begins using new types of data, expands to new users or purposes, or takes on a greater level of automation.
Training should also be incorporated into existing employee and role-based training programs so that employees understand organizational expectations for AI use.
Conclusion
AI governance gives organizations a consistent way to manage how AI is introduced and applied across the business. Without a defined process, AI decisions may be handled differently across departments, making it more difficult to maintain accountability, understand risks, and apply appropriate requirements.
Building an enterprise AI governance program does not require implementing all governance elements at once. Organizations can start by establishing clear accountability, creating visibility into AI use, assessing use cases based on their potential risks and impacts, and applying controls that correspond to those risks.
The NIST AI RMF offers a practical framework for AI governance, but its value comes from translating its principles into practices that fit the organization’s existing structure, risk environment, and operations. This allows organizations to put governance into practice while maintaining the flexibility to adapt as their AI landscape evolves.
FAQ
What Is AI Governance?
AI governance is the set of policies, roles, processes, and controls an organization uses to manage how AI is developed, purchased, deployed, and used. It establishes accountability and provides a consistent process for addressing AI-related risks and requirements.
Why Is AI Governance Important?
AI governance helps organizations maintain accountability and visibility as AI use expands. It provides a consistent way to identify and address considerations such as privacy, security, legal, compliance, and operational risk.
How Does the NIST AI RMF Support AI Governance?
The NIST AI RMF provides a structured approach to managing AI risk through its four functions: Govern, Map, Measure, and Manage. Organizations can use these functions as a foundation and adapt them to their existing governance and risk management processes.
Does Every AI Use Case Require the Same Level of Governance?
No. The level of governance can vary based on factors such as data sensitivity, potential impact on individuals, level of automation, business criticality, and applicable requirements. A risk-based approach allows organizations to apply additional reviews and requirements where they are needed.
Who Is Responsible for AI Governance?
AI governance is typically a shared responsibility across business, technology, privacy, security, legal, compliance, and other relevant functions. An organization should assign clear ownership for the overall program and individual AI use cases, along with defined decision-making and escalation responsibilities.
