NIST AI RMF Principle: Govern 

The “Govern” function in AI risk management focuses on establishing clear oversight and accountability for AI systems. 

It ensures that proper structures, roles, and policies are in place to guide the responsible development, deployment, and monitoring of AI

Develop AI Governance Structure

Governance sets the main stage for aligning AI practices with your organization’s values, goals, and level of risk tolerance. It also ensures that technical aspects of AI development are connected to broader organizational principles. 

Here’s how to approach developing your AI governance structure. 

1. Establish Accountability With a Clear AI Governance Structure

Key individuals should be tasked with specific responsibilities.  Clearly define each member’s role and responsibilities to ensure proper functions.

Level	Role/Team	Responsibilities
Executive Leadership	CEO, CTO, CIO	Set organizational goals, oversee AI governance, and ensure alignment with broader business strategy.
AI Governance Officer	Chief AI Governance Officer	Implement and manage the responsible AI framework, ensure compliance with standards, and act as an advisor.
Ethics & Compliance Committee	AI Ethics Committee	Develop and monitor ethical standards and arbitrate in ethical dilemmas or compliance disputes.
Risk Management Team	AI Risk Officers	Identify, assess, and mitigate AI-related risks and oversee testing and monitoring processes.
Data Privacy & Security Team	Data Protection Officers, Cybersecurity Experts	Ensure AI systems comply with data privacy regulations and cybersecurity protocols.
AI Development Team	AI Developers, Data Scientists	Design and build AI models, follow responsible AI guidelines, and communicate with governance teams.
Testing & Monitoring Team	QA Engineers, AI Testers	Conduct ongoing testing of AI models, ensure models operate as expected, and report anomalies or failures.
Project/Product Owners	AI Product Managers	Ensure AI projects align with organizational goals and oversee the lifecycle from development to deployment.
Stakeholders	Internal/External Stakeholders	Provide feedback on AI system performance, report concerns, and ensure the system serves user and societal interests.

2. Clarify Responsibilities and Setup Communication Channels

Create well-documented processes to help you spot and manage risks throughout the AI system’s lifecycle. This includes anticipating challenges or a plan ready to deal with any issues that pop up.

Some steps you can take to build clear processes in place are:

• Create a checklist or framework for identifying risks at each AI lifecycle stage (development, deployment, and post-deployment) that addresses issues like bias, security vulnerabilities, and data privacy concerns.
• Schedule periodic reviews or audits to assess the effectiveness of your AI systems. 
• Set up a separate communication channel, Slack workspace, or regular cross-departmental meetings to discuss AI-related tasks.

3. Align AI With Company Goals

Your AI systems should not just innovate but enhance your company’s mission and objectives. This way, you’re not just innovating for the sake of it. Instead, you ensure your AI initiatives are responsible and support the bigger picture.

• Connect AI with Your Business Vision. Look for how AI can directly support key business objectives, improve customer experience through automation, or optimize operations with smart forecasting.
• Bring Everyone to the Table. Collaborate from the start with your AI experts and business leaders. AI solutions will be more targeted and effective when both sides understand each other’s needs.
• Solve Real Problems with AI. Focus on the business challenges you know need attention. Whether reducing churn or speeding up processes, build AI tools that make a difference where it counts.
• Make AI a Decision-Making Tool. Use AI insights as part of everyday decision-making to train the system.

4. Link Tech And Ethics

AI development must align with your company’s ethical guidelines. For instance, fairness, transparency, and accountability should be woven into every part of the AI lifecycle.

Some steps you can take to link your tech processes and ethics are: 

• Set clear rules for fairness, transparency, and accountability
• Create an ethics review team and evaluate AI projects for ethical compliance
• Include ethical checks at every stage of AI creation
• Monitor AI’s effects on customers, employees, and society

5. Oversee the Full AI Lifecycle

Establish a way for your team to monitor the entire AI lifecycle—development, deployment, monitoring, and decommissioning. Key steps include:

• Assign roles for each phase to ensure clear accountability.
• Establish standards for data quality, model training, and ethical practices, including bias detection and fairness.
• Implement continuous monitoring of performance, tracking accuracy, fairness, and compliance in real-time.

Create Policies and Standards

Establish clear guidelines to govern the development, deployment, and monitoring of AI systems, ensuring consistency, accountability, and ethical practices. These policies help align AI operations with organizational goals, legal requirements, and risk management strategies outlined by the NIST AI RMF.

1. Develop Clear Risk Management Policies 

Establish clear policies for handling AI risks. This means you must outline how to identify, assess, and manage biases, transparency issues, or data privacy risks. 

Organizational AI risk management policies should focus on several key areas:

• Define Key Terms. Clearly explain important AI-related concepts and outline the AI systems’ specific purposes and intended uses.
• Integrate with Governance. Link AI governance with the organization’s broader governance and risk management controls.
• Align with Data Governance. Ensure that AI policies align with existing data governance practices, especially when dealing with sensitive or high-risk data.
• Set Standards. Establish clear guidelines for experimental design, ensuring high data quality and robust model training practices.
• Risk Mapping. Provide a detailed process for identifying, assessing, and documenting risks associated with AI systems.
• Testing and Validation. Clearly outline the procedures for testing and validating AI models to ensure their reliability and accuracy.

2. Create Consistent Procedures for Oversight 

You need standardized procedures that everyone can follow. This means having a clear process for managing AI risks at every stage of the system’s lifecycle. 

For example, before launching a new AI feature in your main product, ask your development team to submit an internal risk assessment report to the compliance team.

Some procedures for oversight include:

• Regularly assess potential risks related to AI, such as bias, security vulnerabilities, or unintended outcomes.
• Keep thorough records of AI decision-making processes, model performance, and regulation compliance.

3. Stay Compliant With Legal Requirements 

Make sure your AI policies comply with the laws and regulations that govern AI. It is also a vital practice to regularly review your processes to stay on top of other regulation changes (like GDPR or any industry-specific requirements). 

Clear compliance standards can help avoid headaches and keep you operating within legal boundaries.

4. Document All Steps

Good documentation is key to transparency. You want to keep a record of everything from how your AI systems are designed and tested to how they’re deployed. 

This helps with internal reviews and shows regulators and auditors that you’re committed to accountability and trustworthiness. 

5. Plan for Regular Reviews 

AI systems aren’t static, and neither should your policies be. Set up regular reviews of your risk management practices to ensure they evolve with new technologies and potential risks. 

Decide how often these reviews will occur quarterly or annually and who will lead them. This will keep your processes fresh and aligned with NIST AI RMF as things change over time.

Foster a Culture of Responsible AI

Misuses of AI—like deepfakes—raise legal and ethical concerns, making it essential for organizations to promote responsible AI practices. Here are seven key actions to foster a culture of responsible AI:

Promote Safety and Security

Prioritize safety to prevent harm and build trust.

• Conduct risk assessments throughout the AI lifecycle.
• Implement cybersecurity protocols and containment strategies.
• Test AI thoroughly before deployment and maintain human oversight.

Ensure Validity and Reliability

Ensure AI is accurate, consistent, and dependable.

• Use diverse, high-quality datasets to avoid bias.
• Apply validation techniques and monitor performance regularly.
• Set up error detection systems for quick issue resolution.

Lead with Explainability and Transparency

Make AI decisions understandable to build trust.

• Use explainable AI (XAI) techniques.
• Select interpretable models and provide user-friendly visualizations.
• Document AI designs, data sources, and decision processes.

Establish Accountability

Assign clear roles to ensure responsibility for AI outcomes.

• Define stakeholder roles and regularly audit systems.
• Ensure compliance with regulations and legal standards.

Build Fair and Unbiased Systems

Prevent AI from amplifying biases and discrimination.

• Conduct bias audits and apply mitigation techniques.
• Assess performance across demographic groups and include diverse perspectives.

Protect Data and Privacy

Safeguard personal data to maintain trust and legal compliance.

• Use robust data security measures and limit data collection.
• Ensure informed consent and prepare protocols for data breaches.

Design for Human-Centeredness

Ensure AI supports and enhances human well-being.

• Engage users to understand their needs.
• Focus on augmenting skills, not replacing humans.
• Maintain human oversight and evaluate AI’s impact on well-being.