AI Policy

Generative AI tools became available to almost every employee almost overnight. People began pasting documents into chatbots, using AI to write code and customer emails, and signing up for AI features in the software they already used. Many organizations realized they had no rules covering any of it. An AI policy fills that gap. It gives employees confidence to use AI productively, within clear limits. It tells staff which AI tools they may use, what information they must never put into them, when AI outputs must be checked by a person and how to get approval for new AI uses.

The term AI policy can also refer to government policy on AI, but within organizations it usually means an internal policy. Some organizations have a single AI policy, while others separate an acceptable use policy for employees from standards for teams that develop or procure AI. Regulations such as the EU AI Act and frameworks such as ISO 42001 expect documented policies as part of AI governance. For legal, compliance, security, HR and technology leaders, a clear AI policy is often the first and most visible step in governing AI.

Definition of AI Policy

An AI policy is a governing document, approved by leadership, that sets out the organization's principles, rules, responsibilities and procedures for the use, development, procurement and oversight of artificial intelligence, and that applies to employees, contractors and, through contracts, third parties.

It is a core part of an AI governance framework, is managed through policy management and helps reduce the risks of shadow AI.

What an AI Policy Typically Covers

Purpose and scope explain why the policy exists and which people, systems and activities it covers.

Approved tools and uses list the AI tools staff may use and for what purposes, often with a process to request new ones. Keeping the list current matters, since outdated lists push people toward unapproved tools.

Prohibited uses set out what is not allowed, such as entering confidential or personal data into unapproved tools or using AI for certain decisions about people.

Data rules explain what information may be shared with AI tools, especially confidential, personal and client data.

Human review rules require people to check AI outputs before relying on them, particularly for external content, code and decisions affecting individuals.

Transparency rules explain when the use of AI must be disclosed to customers, colleagues or regulators.

Roles, approvals and reporting define who owns AI decisions, how new uses are approved and how to report incidents.

Types of AI Policies

An acceptable use policy sets rules for how employees use AI tools in their daily work.

A development policy or standard sets requirements for teams that build or fine-tune AI systems, covering data, testing, documentation and approval.

A procurement policy sets requirements for buying AI products and for reviewing AI features added to existing software.

Many organizations combine these into one policy with supporting standards, keeping the policy short and moving detail into the standards.

Importance of AI Policies in AI Governance

An AI policy sets clear expectations. Employees know what they can and cannot do, which reduces both risky behavior and unnecessary hesitation. Clear rules also make it easier for managers to answer staff questions consistently.

It protects data. Rules on what may be shared with AI tools reduce the risk of leaking confidential information, intellectual property or personal data.

It also supports compliance and accountability. A documented, communicated policy is evidence that the organization governs AI, which regulators, auditors and customers increasingly expect.

AI Policies and Financial Management

Approved tool lists help consolidate AI spending on a few licensed tools instead of many individual subscriptions.

Procurement rules make sure AI purchases are reviewed for value, cost and risk before contracts are signed.

Clear rules reduce the likelihood of costly incidents, such as data leaks or contract breaches caused by misuse of AI tools.

Requiring a business case for new AI uses connects the policy to decisions about where AI spending delivers the most value.

Common Challenges

Policies written too restrictively push employees to use unapproved tools anyway. A policy that offers approved alternatives is more likely to be followed.

Policies quickly become outdated as new AI tools and features appear. Short review cycles and a named owner help keep them current.

Employees may not read or understand long policies, especially if they are full of legal or technical language. Short summaries and examples help employees understand what the policy means for them.