AI Usage Policy

An AI Usage Policy is a formally adopted organizational policy that governs the appropriate, sanctioned, and secure use of artificial intelligence systems, tools, and models by members of the population. It is designed to preempt misuse, establish accountability, and reduce ambiguity around the use of generative models, AI-enhanced tooling, and decision-automation systems within the organizational environment.

A comprehensive AI Usage Policy mitigates insider risk by codifying restrictions on data input, model interaction, model deployment, and tool integration, especially in contexts involving sensitive data, proprietary logic, or externally facing automation. In the absence of such guidance, subjects may unintentionally or deliberately disclose confidential information to public models, delegate sensitive decisions to unsanctioned systems, or introduce unmanaged shadow AI tooling into operational workflows.

Key Prevention Measures:

• Policy Enumeration: The AI Usage Policy must follow an enumerated structure, allowing investigators and stakeholders to precisely reference policy clauses when documenting or responding to AI-related infringements.
• Permitted Use Cases: Clearly define which AI tools are approved, for which functions, and under what conditions. Distinctions should be made between internal, sanctioned AI deployments and public, third-party platforms (e.g. ChatGPT, Copilot).
• Data Input Restrictions: Prohibit the entry of regulated data types (e.g. PII, PHI, classified content) into external or non-controlled AI systems. This restriction must be backed by enforceable policy language and reinforced through data labeling and access classification.
• Model Interaction Safeguards: Where use of generative AI is permitted, require integration into systems that log prompts, maintain user attribution, and support retrospective investigation in the event of policy violation or data leakage.
• Tool Approval Workflows: Require that new AI-enabled tools undergo formal security, legal, and operational review before deployment. This ensures visibility and governance over the introduction of capabilities that could affect risk posture.
• Change Control and Oversight: Governance responsibility should reside with a designated internal body, empowered to interpret, update, and enforce AI policy clauses in alignment with evolving capabilities and threats.