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.
The five pillars and what goes in each
A governance framework that has all five pillars is defensible; one that is missing any of them has a visible gap that auditors and regulators will find.
1. Accountability and ownership
For every AI system you run, there should be one named person who is responsible for its outputs. This does not have to be the model’s builder or the vendor’s contact — it is the internal owner who can answer questions about the system, approve changes to it, and make the call to take it offline if something goes wrong. Without a name against a system, governance is theoretical.
2. Model inventory
A register of every AI tool or model your organisation uses, what it does, where its outputs go, and whether those outputs affect people in material ways. The inventory does not need to be elaborate — a maintained spreadsheet is enough to start. Its purpose is to make visible what you are actually running, so you can apply the rest of the framework to the right things.
3. Bias and quality monitoring
A plan for how you detect when an AI system’s outputs have degraded, drifted, or produced systematically different outcomes for different groups. The monitoring approach depends on what the system does: a text classifier might be spot-checked weekly; a hiring shortlisting tool might need disparity analysis every quarter. The key is that the monitoring is defined in advance, not improvised when a complaint arrives.
4. Incident response
A written plan for what happens when an AI system produces a harmful, biased, or privacy-violating output at scale. Who is notified, on what timeline, by whom? Who can take the system offline? Who communicates externally? Is a GDPR breach notification triggered? Having these answers written down before an incident is the difference between a handled problem and a crisis.
5. Review cadence
AI systems change — the underlying models update, the data changes, usage grows, regulations evolve. A schedule for reviewing each system’s inventory entry, monitoring results, and governance ownership means the framework stays current. The builder suggests a cadence based on risk level: quarterly reviews for customer-facing or decision-making systems, annual for internal low-risk tools, and an immediate review whenever a significant incident occurs or a new high-impact use case is added.
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, and the regulatory exposure is real.
- Start with the inventory. You cannot govern systems you do not know you run. The model inventory pillar is the foundation everything else depends on.
- Assign named owners, not teams. “The team” owns nothing. Governance works when one person can be asked to answer for each high-risk system.
- Treat it as living. Re-run the builder whenever a new high-impact use case appears, not just on the annual calendar.