Acceptable use policy for an AI product: enforcement basics

An acceptable use policy for an AI product should name the specific abuse you expect, say what happens when it occurs, and be backed by detection, a review step and a way to suspend and appeal. Write it short, tie each rule to a control you actually run, and check that it fits your provider's own usage rules.

Start from your provider's rules

When you build on a model API, your customers' behavior reaches your provider through your account. Providers publish usage policies and can suspend accounts that break them. Read yours and make sure your policy is at least as strict where it matters, so you can act on a customer before the provider acts on you. Link to the provider's policy where relevant and note when it changes.

What to cover

Pick the rules that match your product. Common ones for AI products:

• No attempts to extract the system prompt, model or training data at scale
• No automated signups or reselling of access without agreement
• No use to generate content that is illegal or that harms others, with your own examples
• No sending of data the user has no right to submit, such as another person's sensitive data
• No attempts to bypass limits, filters or safety controls
• No use to attack other systems, including through connected tools

OWASP's unbounded consumption page describes model replication, where attackers use a model's outputs as synthetic training data to build a copy, as an abuse pattern. If that matters for your business, name it. See settings that help prevent model extraction.

Keep it readable. Long lists of legal phrases do not stop abuse, and plain examples help honest users avoid mistakes.

Tie every rule to a control

For each rule, write down how you will notice a breach and what you can do:

• No automated signups. Signal: clusters of new accounts with scripted timing. Action: pause free credits and review.
• No limit bypass. Signal: many keys or accounts from one source. Action: block the source and notify.
• No illegal content. Signal: output filter hits and user reports. Action: remove content, warn or suspend.

If you cannot detect a rule, it is a statement of intent. That is fine, but know which ones they are.

A fair enforcement path

1. Detect. Automated flags plus user reports.
2. Review. A person looks at borderline cases, with the least data needed. Content access should be logged and limited. See prompt logs for debugging.
3. Act in steps. Warn, rate limit, suspend a feature, suspend the account. Match the response to severity. Serious cases, such as threats to others, may need immediate suspension and legal advice.
4. Notify. Tell the user what rule was broken and how to appeal.
5. Appeal. A named contact and a time frame.
6. Record. Keep a short record of the decision and reason.

Reporting duties

Some content creates legal reporting duties that vary by country. If your product could receive such content, get legal advice before launch and write the process down. Do not improvise it during an incident.

Keep the policy alive

Review it twice a year against the abuse you actually saw. Add rules for new features, such as agents that act on a user's behalf, which create new ways to misuse the product.

Quick check

Sources

• OWASP LLM10:2025 Unbounded Consumption

Last reviewed: 19 October 2026.