Acceptable Use for AI Agents
Which uses are allowed, conditional or prohibited, and how do users know?
Reference notes by Min · Reviewed October 15, 2026
Definition
An acceptable-use policy defines the purposes, information and actions permitted when people use or operate an AI agent, together with conditions and reporting responsibilities.
A useful policy answers concrete questions before a user starts a task. Can this agent access a customer export? Can it contact an external party? Does approval cover one action or a continuing workflow? Ambiguous answers create inconsistent practice and make later accountability harder.
What it covers
- Purpose
- Specify supported tasks and excluded uses.
- Information
- Explain which data may enter each approved service.
- Actions
- Distinguish drafting, recommending and executing.
- Accountability
- Identify support, exception and incident-reporting routes.
Separate organizational permission from technical capability. A connector may technically support bulk export even when policy forbids it for a particular agent. That discrepancy should lead to a narrower tool boundary or access policy, rather than relying entirely on the user remembering a rule.
In practice
Practical design patterns by Min.
- Write examples in terms users encounter in their work.
- Connect conditional uses to an actual approval process and enforcement point.
- Version policy changes and communicate meaningful differences.
Make exceptions traceable and time-bounded where appropriate. Record the permitted purpose, owner, relevant controls and expiry or review trigger. A request for convenience should not silently create a permanent expansion of authority for every user of the same tool.
Research notes
Source-grounded details and editorial applications. Follow each link for the original document.
Policies need organizational ownership
The NIST AI RMF Govern guidance connects policies with responsibilities and processes. The allowed, conditional and prohibited structure here is an editorial way to make those decisions understandable.
Source: NIST AI RMF Playbook: Govern ↗
Working example
A sales agent drafts messages
Staff can prepare outreach but cannot upload restricted customer records to an unapproved tool.
- Describe approved sources and explain how to identify restricted records.
- Constrain the agent’s connector access and external sending authority.
- Provide a clear route for requesting a new use or reporting a mistake.
Why the controls matter
For the sales example, a generic warning to avoid sensitive data is insufficient if staff cannot identify the relevant classification. Provide approved sources and a practical alternative for restricted workflows. Reported mistakes should lead to containment and improvement rather than concealed incidents.
Common pitfalls
- Listing broad principles without decisions users can apply.
- Allowing an exception verbally while the technical policy remains unchanged.
- Publishing prohibitions that the product silently encourages users to violate.
Review the policy against actual product behavior. If the interface presents an unsupported action as a normal option, remove or constrain it. Policy, permissions, user guidance and training should describe the same operational boundaries.
Questions for operational review
Use these questions to decide what evidence your team needs. They are editorial prompts, not a compliance checklist.
- Can users apply the rule to a realistic task?
- Which restrictions are technically enforced?
- How are exceptions reviewed and withdrawn?
Articles linking here
References
Primary documents reviewed for this entry. Examples and implementation patterns are editorial synthesis.
- NIST AI RMF Playbook: Govern ↗Accessed October 15, 2026
- NIST SP 800-53 Rev. 5: Security and Privacy Controls ↗Accessed October 15, 2026