AI governance policy repository builder
A scattered set of ad-hoc AI rules is not governance. This builder generates a structured policy repository — the folder layout plus a section-by-section outline for each policy your organization needs — scoped to your size, industry, and regulatory exposure, so you start from a coherent scaffold instead of a blank page.
How it works
Tell the tool your organization size, industry, and regulatory scope. It selects the policy set proportionate to your risk: a small low-risk org gets the core five (acceptable use, risk management, incident response, vendor management, training); regulated or high-risk organizations also get bias/fairness testing, model documentation, human-oversight, and data-governance policies. Each policy comes with an outline of the sections it should contain. You can copy a single outline or download the entire repository as a Markdown file.
Why a repository rather than a single policy document
AI governance fails most commonly in one of two ways: organisations have nothing written down at all, or they have one sprawling “AI policy” that nobody can find the relevant section of under pressure. A structured repository — separate, named documents for each governance function — solves both problems. It makes each policy short enough to read, easy to locate during an incident, and simple to update when one area changes without touching the others.
What goes in each policy
Acceptable use policy sets out what AI tools staff are and are not permitted to use, for which tasks, and with what categories of data. The core questions: Can staff paste customer PII into a consumer LLM? Must AI use be disclosed? What happens if someone violates the rules?
Risk management and assessment process defines how your organisation evaluates new AI use cases before adopting them. A lightweight version is a one-page questionnaire asking about data sensitivity, decision impact, and regulatory exposure; a heavier version is a formal conformity assessment for high-risk systems.
Incident response policy for AI failures covers what happens when an AI system produces a harmful, biased, discriminatory, or privacy-violating output. Who is the first responder? On what timeline must incidents be escalated? Who decides whether to take a system offline? Is a GDPR breach notification triggered?
Vendor and third-party management policy governs AI tools and models you procure from outside. What contractual terms are required (data retention, training, liability)? How do you assess a vendor’s own governance before adopting their product? What happens when a vendor changes their model or data practices?
Staff training policy specifies the minimum AI literacy every employee needs, the additional training required for anyone who works with AI in a decision-making or customer-facing role, and how often that training is refreshed.
Bias and fairness testing policy (required for higher-risk organisations) describes how outputs from AI systems that affect people are tested for demographic disparities, how often tests are run, and what remediation looks like when a disparity is found.
Notes and tips
- The output is a scaffold, not finished policy. Fill in your organisation’s specifics and route everything through legal and compliance before adoption.
- The structure aligns with common requirements across ISO/IEC 42001, the NIST AI Risk Management Framework, and the EU AI Act, but using it does not by itself confer certification or satisfy a regulator.
- Assign an owner to each policy document and review at least annually and after major model changes, vendor changes, incidents, or significant regulatory developments.