Implementation Guide

Acceptable Use Policy for Generative AI at Work

An effective generative AI acceptable use policy defines what data, tools, and use cases are permitted, then maps those provisions to enforceable runtime controls such as tool allowlisting, agent permission scoping, and audit logging, so compliance does not depend solely on employee behavior.

Explore Runtime Governance Talk to an Expert

Questions Governance Leaders Should Be Able to Answer

Before drafting or revising a policy, leadership should be able to answer these questions clearly. If they cannot, the policy likely exists as a document rather than as an enforced control.

  • Which categories of sensitive or regulated data are explicitly prohibited from generative AI tools under our policy
  • How is our acceptable use policy technically enforced at runtime, and what happens when a violation is attempted
  • What audit trail do we maintain to demonstrate policy compliance to regulators, auditors, or customers
  • How are permissions for autonomous AI agents scoped differently from permissions for direct human use
  • What is our process for evaluating and approving new generative AI tools requested by business units

Translating Policy Provisions Into Runtime Controls

A written policy states intent. Runtime controls are what make that intent operational. Each provision in an acceptable use policy should map to a specific enforcement mechanism rather than relying on voluntary compliance.

Why a Written Policy Alone Is Insufficient

A policy document communicates expectations, but it has no mechanism to prevent a violation from occurring. Employees can copy sensitive data into an unsanctioned tool, and an autonomous agent can invoke an unapproved API, regardless of what the policy states, unless a technical control intervenes at the point of action. Written policy sets the standard; runtime enforcement is what makes that standard reliable.

Core Sections an Enterprise Policy Should Include

A generative AI acceptable use policy should be structured so that each section can be translated directly into a control. At minimum, it should define permitted and prohibited data categories, the list of approved tools and models, acceptable use cases by role or department, escalation and exception procedures, and the consequences of violations. Ambiguity in any of these sections makes enforcement inconsistent, since engineering and security teams need concrete rules, not general principles, to build against.

Agent Permissions Are a Distinct Governance Layer

Autonomous AI agents introduce a governance question that traditional acceptable use policies were not written to address: what happens when the "user" of a tool is not a person but a system acting on a person's behalf. Agent permissions should be scoped independently from general employee use rules, following least-privilege principles for tool access, data access, and the actions an agent is allowed to take without human approval. Treating agent permissioning as a distinct layer, rather than an extension of human use rules, prevents gaps where an agent's effective access exceeds what any individual employee would be granted directly.

Governance, Audit, and Ongoing Review

A policy is not a static artifact. As new tools are requested, new regulations take effect, and agent capabilities expand, the policy and its associated controls need a defined review cadence. Audit logs of prompts, outputs, and tool invocations, tied to requester identity, give governance teams the evidence needed to demonstrate compliance and to identify where policy provisions are not being enforced consistently in practice.

Policy Components That Require Runtime Enforcement

The table below summarizes the policy areas most likely to fail without a corresponding technical control.

Component Description
Data Handling Rules Restrictions on sensitive or regulated data entering prompts, enforced at the API gateway or model-access layer.
Approved Tool Lists Allowlisting of sanctioned AI tools to prevent unmanaged access through browsers or personal accounts.
Agent Permissions Scoped, least-privilege execution rights for AI agents and tool calls, distinct from general employee use rules.
Audit Logging Recorded prompts, outputs, and tool invocations tied to requester identity for retrospective compliance review.

Practical takeaway

If a policy provision cannot be tied to a specific enforcement point, an audit log, or an approval workflow, treat it as unenforced until that mapping exists.

Move From Policy Document to Enforced Control

Trussed AI provides runtime governance for enterprise AI agents, including policy enforcement, permission scoping, and audit logging at the point of execution.

Request a Demo