AI governance framework builder
Most teams adopt AI faster than they govern it. This builder turns a few answers about your organisation into a practical governance framework outline covering the parts that actually matter: who is accountable, what models you run, how you watch for bias and drift, what happens when something goes wrong, and how often you review the whole thing. It is intentionally lightweight — enough structure to be defensible, not so much that nobody reads it.
How it works
You enter your company size, industry, regulatory context, and a list of AI use cases. The tool scores an overall risk level from those inputs (a fintech with customer-facing decisioning lands higher than a startup using AI for internal drafting) and assembles a framework with five pillars: accountability and ownership, model inventory, bias and quality monitoring, incident response, and review cadence. Each pillar is tailored — higher-risk profiles get stricter monitoring and faster review cycles. You can copy the structured summary, or copy a doc prompt that expands the outline into a full written policy with any LLM. Everything runs in your browser; nothing is uploaded.
Tips and notes
- Be honest about risk. Customer-facing, money-moving, or health/safety-relevant AI should be flagged high — the framework gets stricter accordingly.
- Inventory first. You cannot govern models you do not know you run. The inventory pillar is the foundation everything else depends on.
- Assign a named owner. “The team” owns nothing. Governance works when one accountable person signs off on each high-risk system.
- Treat it as living. Re-run the builder whenever a new high-impact use case appears, not just on the calendar.