This free AI governance checklist generator from Paloren builds a control checklist for your AI systems from four inputs: workflow count, data sensitivity, decision impact and regulatory exposure. It outputs named controls, including sources, permissions, approval points, logging, correction, monitoring and an owner, as an exportable table.
Enter your own figures below. The results table and chart update as you type. The default scenario is pre-loaded so you can see the method before you change anything.
Know what needs a human decision.
| Result | Value |
|---|
Every output comes from the formulas below. Nothing is drawn from a survey, a client result or a third-party benchmark. The figures are model estimates based on the inputs you supply.
| Output | Formula | What it means |
|---|---|---|
| Control count | workflows x 2 + sensitivity x 2 + impact x 2 + regulatory x 2 | Number of checklist items generated by the model. |
| Risk score | sensitivity + impact + regulatory | 3 to 15 composite used to set review cadence. |
| Review cadence | risk score 3 to 6 quarterly, 7 to 10 monthly, 11 to 15 weekly | Suggested operational review frequency. |
A team runs 4 AI workflows with sensitivity 3, decision impact 4 and regulatory exposure 3. The model generates 28 checklist items and sets a monthly review cadence.
| Input | Value |
|---|---|
| workflows | 4 |
| sensitivity | 3 |
| impact | 4 |
| regulatory | 3 |
This model is deliberately narrow. It values time and direct cost only. It does not price quality improvement, customer satisfaction, risk reduction or revenue lift, because those need evidence from your own operation.
Adoption is the most common source of error. A system that works in a pilot rarely hits its full share on day one. For planning, assume a ramp and test the conservative case.
The model also ignores integration difficulty. Two systems with the same name can differ enormously in effort. List each integration and its owner before you commit budget.
Finally, the figures are not a quote. They are a planning tool. Use them to frame a conversation, then get a scoped proposal based on your actual systems and workflows.
Approved sources, permission boundaries, human approval points, logging, correction routes, model monitoring and an owner for each workflow.
The checklist is operational, not theoretical. Each item should map to something a person can check, sign off or fix during a normal working week.
Governance fails when it lives only in a policy document. Attaching it to workflows makes it usable.
Each workflow has a different risk profile. A customer support bot and a pricing recommendation engine need different controls, even inside the same business.
Decision impact measures how much a wrong output affects customers, money, safety or legal standing.
High-impact decisions need human approval before action. Low-impact tasks can run with logging and periodic review. The score sets that threshold.
Weekly review suits high-risk workflows or those in their first weeks after launch.
Once a workflow is stable and its logs look clean, you can lengthen the cadence. Lengthening is a decision, not a default.
No. It is a planning aid. It does not replace legal advice or a formal regulatory assessment.
Use it to structure internal governance. Then involve legal or compliance specialists where regulation, contracts or sector rules apply.
Paloren provides AI implementation and training. This calculator uses illustrative inputs and the formulas shown here, not client results or a service quotation. Explore Paloren services.
An AI governance checklist covers an inventory of AI systems in use, data access and privacy rules, risk tiering for each use case, human approval points, logging, a correction process, monitoring and a named owner.
The generator above turns those controls into a list filtered by your inputs.
Governance controls only matter when they attach to work someone actually does. Tying each control to a workflow gives it an owner, a cadence and a place in the process, which is what keeps it running after the project ends.
Decision impact is what happens when the AI gets it wrong: a wrong email draft costs minutes, a wrong pricing or lending decision costs money and trust. Higher impact raises the approval and monitoring controls in your checklist.
Weekly review fits AI systems that touch customers, money or regulated decisions, or that change often. Lower-risk internal tools usually need monthly or quarterly review. The generator sets the cadence from your impact and exposure inputs.
No. This generator is a planning aid that produces a starting checklist based on your inputs. It does not certify compliance with any law or standard, and it does not replace legal advice or a scoped governance program.
Paloren's governance program starts from $10k and runs 3 to 4 weeks. A readiness assessment starts from $8k over 2 to 3 weeks. Prices are indicative until scoped.
Talk through the result with Paloren. Bring the workflow, the assumptions and the questions this opened up.
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A number is a starting position. The next step is a decision you can defend.
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