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Checklist for Creating an AI Governance Policy

November 11, 2024

An effective AI governance policy requires careful planning, input from key stakeholders, and alignment with regulatory standards.

Below is a checklist for creating an AI governance policy that addresses compliance, security, and ethical considerations.

Futuristic AI robot holding scales of justice, symbolizing the intersection of artificial intelligence and law in a digital, high-tech environment.

1. Assess Regulatory Requirements

  • Identify applicable regulations (e.g., GDPR, HIPAA) and ensure your governance policy includes provisions for compliance.
  • Consult with legal and compliance teams to align policies with industry-specific regulations.

2. Define AI Policy Framework

  • Develop a clear purpose statement that outlines why and how AI will be used in the organization.
  • Define roles, responsibilities, and accountability structures to oversee AI use and compliance.

3. Outline Acceptable Use Cases

  • Identify and document acceptable use cases for AI applications, specifying their purpose, scope, and limitations.
  • Provide examples of approved tasks and responsibilities for each role involved with AI usage.

4. Specify Prohibited Uses

  • List activities and use cases that are prohibited, with a focus on high-risk areas and activities that pose security or compliance risks.
  • Include examples of misuse to clarify prohibited activities for users.

5. Develop a User Training Program

  • Provide AI-specific training for all users, covering data protection, ethical standards, and acceptable use.
  • Schedule ongoing training sessions to update employees on new policies or regulatory changes.

6. Establish Access Controls

  • Define who can access AI tools and data, based on roles and responsibilities.
  • Implement access control measures, such as RBAC, to restrict AI access based on job requirements.

7. Implement Data Protection Measures

  • Include data encryption, anonymization, and data retention policies to protect sensitive information processed by AI applications.
  • Define guidelines for data classification and tagging to ensure compliance with privacy standards.

8. Document and Review Policies Periodically

  • Keep the AI governance policy up to date, with regular reviews to address changes in technology, regulations, or business needs.
  • Encourage feedback from users and stakeholders to continually improve the policy.

9. Establish a Feedback Loop and Continuous Improvement Process

  • Provide a channel for users to report issues, suggest improvements, and seek clarification on policy-related questions.
  • Use feedback and insights from policy implementation to refine and enhance governance practices over time.

Establishing robust AI governance and compliance practices is foundational for managing AI applications responsibly. By defining clear policies, educating users, and documenting use cases, organizations can prevent misuse, protect sensitive information, and maintain regulatory compliance. A well-structured AI governance framework not only secures AI systems but also helps build a culture of trust and accountability around AI use.

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JR Garcia

JR Garcia

ANM Solutions Engineering Director

JR Garcia is the Director of Solutions Engineering at ANM. With over two years of experience in this role, JR leads solutions engineering teams in Arizona, New Mexico, and Texas, focusing on network, data center, and security architectures. Prior to joining ANM he was a solutions engineer at Cisco Systems, and worked in Product Development for a service provider in Anchorage, AK. He is a CCIE and holds degrees in Telecommunications and Business Management.