Guide: Allowed AI Use Rules Starter
Responsible use needs examples, not just a poster. Allowed AI Use Rules Starter helps turn the policy intent into practical examples people can follow.
Allowed AI Use Rules Starter
A practical starter for turning “use AI responsibly” into everyday rules people can actually follow.
If people need permission, examples and boundaries, give them more than a slogan.
What this guide helps with
This guide helps teams turn broad AI policy into simple use rules, it is not legal advice. It is a way to make AI use a bit clearer for people who are already using tools in real work and need to know what is safe, allowed or worth checking first.
Why now
AI tools are already inside daily work; People are drafting, summarising, analysing, reviewing and publishing with them. A policy that only says “be responsible” leaves too much interpretation to individuals who may not know the legal, data, customer or reputational risks attached to the task.
The pattern
The pattern is policy without translation into real work. Leaders approve a high-level message, but daily operators need examples. The missing layer is usually not the big policy, it is the simple table that says: in this role, with this data, for this task, here is what you may do.
The check
Ask people what they are actually using AI for, not what the policy imagines. Examples might include drafting emails, summarising meeting notes, rewriting customer messages, reviewing spreadsheet formulas, preparing interview questions, analysing survey comments or creating social posts. The real list is usually messier and more useful than the official use-case deck.
Create simple buckets: public information, internal information, customer or employee information, confidential business information and regulated information. People do not need a legal lecture first. They need to know which bucket their task falls into and whether that bucket is allowed, restricted or requires approval.
A draft email, a contract clause, a customer response and a hiring summary should not all have the same review rule. Write plain rules for each output: can be used as draft only, needs human check, needs manager approval, must not be used, or must be labelled before publishing.
People need examples that trigger caution. For instance: paste customer records, summarise medical information, screen candidates, publish AI-generated images, analyse employee performance, automate contract review. These examples help people pause without making every harmless task feel like a legal incident.
If AI-generated content may be shared externally, published, sent to customers or used in formal decisions, decide what needs a label or disclosure. Keep it practical: what gets labelled, where the label goes, who checks it, and what happens when a human edits the AI output before release.
Responsible use needs somewhere to go. If a worker is unsure, who do they ask: manager, legal, privacy, AI champion, IT, comms? Without a route, people either stop using AI entirely or quietly make their own rules, which is how shadow AI becomes an operating model.
The first version will not be perfect. Set a monthly or quarterly check: which questions are people asking repeatedly, which use cases were unclear, which rules were ignored, and which examples need updating? AI use changes quickly, so the rules need to stay alive without becoming a cathedral.
Quick examples
| Situation | Better question |
|---|---|
| Marketing drafts a social post with AI | Can it be used as draft text, does it need a label, and who checks for brand, accuracy and copyright risk before publishing? |
| HR asks AI to summarise interview notes | What candidate data is being used, who reviews the output, and is the tool approved for hiring-related material? |
| Finance asks AI to explain a variance | Is the data internal, confidential or regulated, and can the explanation be used in reporting without a human checking assumptions? |
| Customer support uses AI to rewrite responses | Can the AI suggest wording while a human still owns accuracy, tone, escalation and customer-specific context? |
The Satire
Nothing says "we're ready" like a policy that starts every answer with "it depends."
Related Vieews paths
Chaos scenes spot the contradiction. Signals name it. Guides give you the next simple move.
Chaos
The Blue Blob and the Responsible Use Sign
The discovery scene that started this thread.
Signal
A Policy Is Not An Operating Guide
Use the signal when you want the pattern named clearly.
Playbook
AI Rollout Control Kit
Use the heavier structure when you need the deeper lens.
Useful context
This guide is a starter. It helps teams turn broad AI expectations into simple use rules that match actual work, especially where AI literacy, content labelling and human review are becoming practical requirements.
These are Vieews, not bibles, use as basic lenses, not prediction, investment advice, legal advice, or a replacement for doing your own investigation. If a line makes the spreadsheet uncomfortable, excellent, ask one more question, tug on that thread (don't get fired!).
