AI governance
Using AI in your business? Your insurance might not cover it.
Most business policies were written before staff started pasting company information into AI tools. Tensor Garden reviews where AI is already in use, what data it touches, who reviews its output, and what your current cover and vendor agreements actually say about it.
The answer before the details
AI use inside a business rarely arrives through a decision. It arrives through individual staff habits, a vendor feature that switched on, and an integration nobody documented. A governance review finds where that is happening, what data is exposed, who is accountable for the output, and which of those facts your insurer, your clients and your own policies have never been told.
This is technical and operational readiness. It documents AI use, data exposure, review controls and vendor terms so the business can answer questions from insurers, clients and leadership with evidence rather than assumption.
What a review looks at
What breaks, and what Tensor Garden fixes first.
These are practical workflow patterns, not case studies. The exact sequence comes from the assessment and the systems already in place.
- 01
Where AI is actually in use
What breaks
Adoption happens tool by tool and person by person, so the business has no inventory and no owner.
What we fix
We inventory the tools, the accounts, the integrations and the vendor features, and name an owner for each.
- 02
What data it touches
What breaks
Sensitive information reaches AI tools through copy and paste, connectors and default settings nobody reviewed.
What we fix
We map the data flows, define what stays out, and put the boundary somewhere staff can read it.
- 03
Who reviews the output
What breaks
AI output reaches customers, documents and decisions without a recorded review step.
What we fix
We define the review points, who owns them, and what gets logged so the decision trail exists.
- 04
What the paperwork says
What breaks
Cover, vendor terms and client agreements were written before any of this, and nobody has read them against current practice.
What we fix
We review the terms against actual use and produce a prioritized gap list for you to take to your broker and counsel.
Common pain points
- Staff are already using AI tools, and nobody can say which ones or with what data.
- A vendor added AI features to software you already run, and the terms changed underneath you.
- An AI output reached a customer and no one can show who reviewed it.
- Renewal or a client questionnaire asks about AI use and the honest answer is that you do not know.
Common projects
- AI usage and tool inventory
- Data exposure mapping
- Acceptable-use policy and review rules
- Vendor and integration terms review
What to avoid
- Assuming a general business policy covers AI-driven errors, disclosure or misuse.
- Writing a policy nobody can follow because it bans the tools staff already depend on.
- Treating a vendor AI feature as governed simply because the vendor is reputable.
- Letting an AI system act on customer, financial or regulated matters with no named reviewer.
Expected outcomes
- A written record of where AI is used
- Data boundaries staff can actually follow
- Named review points on AI output
- Evidence you can hand to an insurer or client
Connect the problem to the right service
- Responsible AIWhat we do not automate, and where humans stay accountable.View service →
- Cyber Insurance ReadinessControl and evidence review before a questionnaire or renewal.View service →
- Compliance ReadinessEvidence workflows with named owners and a refresh cadence.View service →
- AI AutomationAutomation designed with the review points built in.View service →
Security and compliance caveats
- Scope and pricing are agreed after reviewing your needs. This is not legal, insurance or broker advice. Coverage questions belong with your broker, your carrier and your counsel. The review documents the technical and operational facts they need.
- No certification and no guarantee of coverage or compliance. The output is a prioritized gap list and an evidence record.
- Findings describe what the review can observe. Contractual and regulatory conclusions require the appropriate professional.
What owners ask about AI governance
Does my business policy cover AI mistakes?
That is a question for your broker and carrier, and the honest answer usually depends on facts nobody has written down yet. The review produces those facts: where AI is used, what data it touches and who reviews the output.
We only use AI a little. Is a review worth it?
Small use is still worth documenting, because the exposure is usually in the data rather than the volume. A short inventory and a written boundary often settles it.
Will this stop staff using AI?
That is not the goal. A usable policy names the approved tools and the data that stays out, so staff can keep the productivity without creating unmanaged exposure.
What do we get at the end?
An inventory of AI use, a data exposure map, review points with named owners, and a prioritized gap list you can take to your broker, your clients and your leadership.
How the work is controlled
Your priorities set the work. You approve additional charges before extra work starts.
The assessment maps the whole stack, stabilizes the risk first, then builds the workflow layer. Nothing chargeable starts before your authorized decision-maker says so.
How we work, in fullMap the AI governance workflow before choosing tools.
One assessment across your industry workflow, IT stack and automation path. We identify what is urgent, what is risky, what is wasting staff time, and what can safely become a system.