What is AI Policy?
Also called acceptable use policy for AI, internal AI policy.
An AI policy is an internal document stating how members of an organization may and may not use AI systems in their work. It typically covers approved tools, data that may not be submitted, disclosure expectations, review requirements for external output, and who to ask when a situation is unclear. Its usefulness depends on being specific.
Policies fail in two directions. Blanket prohibition drives usage into personal accounts where nothing is visible or governed, which is worse than the situation it was meant to prevent. Blanket permission leaves people guessing about client data and regulated information. Workable policies name categories with examples, since abstract principles do not resolve concrete questions at the moment they arise.
The data section usually carries the most weight. It should name what may not be submitted to external systems in terms recognizable to staff, such as client identifiers, health information, unpublished financial results, or credentials, rather than referring to sensitive data and leaving the classification to individual judgment under time pressure.
Disclosure practice varies by jurisdiction and sector and is genuinely unsettled. Some regulated contexts require disclosure that a customer is interacting with an automated system, some sectors have professional obligations about authorship, and elsewhere it is a choice. The policy should state the organization's position explicitly rather than leaving each person to decide case by case.
A policy nobody can locate has no effect. Practical implementations keep it short, link it from where people actually work, name a person to ask, and review it on a schedule, since both the tools and the regulatory picture change faster than most document review cycles assume. Length is generally inversely related to compliance.
Key points
- States approved tools, prohibited data, and disclosure expectations
- Blanket bans push usage into unmonitored personal accounts
- Data rules must name categories with concrete examples
- Disclosure obligations vary by jurisdiction and sector
- Short, findable, and regularly reviewed beats comprehensive
In practice
A two page policy names three approved tools, prohibits submitting client names, contract terms, or unpublished financials to any external system, requires a named human reviewer before AI-assisted text reaches a client, and states that customers are told when a first response was automated. It names the operations lead for questions and carries a review date six months out.