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About this tool
Test yourself or your team on acceptable AI use at work — 10 scenario questions with instant explanations and a readiness score.
The AI Policy Quiz scores you on ten workplace scenarios covering acceptable AI use — confidential data in prompts, verifying model output, disclosure, intellectual property, hiring bias, meeting notetakers and accountability — with an instant explanation for every answer. The result is graded against the 80% pass mark common in compliance training, with themes drawn from typical enterprise acceptable-use policies and the NIST AI Risk Management Framework. It is built for team leads onboarding staff to an AI policy and for anyone checking their own judgement before using AI with work data.
Open AI Policy Quiz on AltFTool — it loads instantly in your browser.
Provide your input — an image, text, or data.
Let the tool analyze or generate the result.
Review, refine, and reuse the output wherever you need it.
Ten realistic workplace situations — pasted contracts, leaked API keys, AI notetakers — not abstract definitions.
Every answer comes with the reasoning and the rule or framework behind it, right or wrong.
Scores band at 90/80/50 percent, mirroring the 80% threshold most compliance training uses.
This quiz uses 80% (8 of 10) as its pass band, which is the threshold most corporate compliance training applies. Scores of 90% or above rate as excellent, and below 50% flags serious gaps worth retraining before using AI with work data.
Not into unapproved public tools — that is the most common rule in every corporate AI policy. Prompts sent to consumer AI services may be retained or used for training, so confidential, client or personal data belongs only in deployments your organisation has approved, or must be redacted first.
Because language models hallucinate: they produce fluent, confident text that can contain invented facts, figures and citations. Frameworks like the NIST AI Risk Management Framework and virtually all editorial policies therefore require a human to verify factual claims against real sources before output is used or published — running it through a second AI does not count.
The human who reviewed and shipped it. AI policies consistently keep accountability with people, not tools or vendors — using AI changes how work is produced, not who answers for it. That is why review gates for AI-generated code and content exist.