AI Governance
AI governance tools, and how to choose one your reviewers will trust
Kriv AI is a consultancy, not a governance software vendor. We help healthcare, life sciences, financial services and insurance teams decide what their AI governance needs to record, then choose and set up the tools that record it.
AI governance tools are software that records which AI systems an organisation uses, who owns each one, what risk it carries, who approved it and how it is checked over time. They range from AI inventories and risk registers to monitoring tools, policy workflow on existing risk platforms, and controls built into tools you already own. The right one depends on your policy and your existing stack, so decide what must be recorded before you buy.
what they do
What AI governance tools do, and what they cannot do for you
Every AI governance program has to answer the same questions for each AI system: what it is, who owns it, what data it touches, how risky it is, who approved it and how anyone would know if it stopped behaving. AI governance software exists to keep those answers in one place, route the approvals and keep the evidence a reviewer will ask for.
What a tool cannot do is decide the answers. The policy, the risk tiers and the approval rules come from your organisation. A platform bought before those exist becomes an expensive, half-filled form. Our AI governance framework page sets out the decisions that have to come first.
tool types
The main types of AI governance tools
Vendors describe overlapping products in different words. Sorting them by the job they do makes a shortlist faster.
| Type | What it records or does | Good fit when | Watch for |
|---|---|---|---|
| AI inventory and risk register | A list of every AI system with owner, purpose, data, risk tier and approval status. | You are starting out, or you cannot yet say how many AI tools are in use. | A register is only as current as the process that updates it; name who keeps it current. |
| Policy and approval workflow | Intake forms, risk assessments and sign-offs routed to the right reviewers. | Your organisation already runs risk, privacy or IT workflow on a platform and wants AI reviews in the same place. | Workflow built for privacy or IT tickets may need adapting to how AI risk is actually assessed. |
| Model and output monitoring | Checks on what deployed AI systems produce over time, with alerts when results drift. | You run your own models or high-volume AI workflows where quality has to be measured, not assumed. | Monitoring needs an agreed definition of a bad output; the tool will not supply one. |
| Controls in tools you already own | Data loss prevention, access rules, audit logs and admin settings in your existing productivity, cloud and AI platforms. | Your AI use runs mainly inside licensed assistants and cloud services. | These controls are configured, not bought; someone has to set and review them. |
vendor ai governance
Where OneTrust, IBM, Collibra and ServiceNow AI governance fit
Several established software companies now offer AI governance as an extension of a platform they already sell. OneTrust comes from privacy and trust management. IBM offers AI governance alongside its watsonx AI products. Collibra comes from data cataloguing and data governance. ServiceNow extends its IT and risk workflow platform.
The practical question is less which of these is best and more which one your organisation already runs. Extending a platform your risk, privacy, data or IT team already works in keeps AI reviews next to the processes they belong to. Buying a separate tool that nobody else uses can create a second, disconnected register.
We do not rank these vendors, and features change between releases. Ask each one to demonstrate your own intake and approval process on their product, using our AI vendor evaluation checklist as the script.
how to choose
How to choose AI governance platforms
These questions decide the choice, in this order. The first one rules out more options than any feature list.
1. Is the policy written?
You need agreed risk tiers, approval rules and owners before a platform can enforce them. If they do not exist yet, write them first; a spreadsheet register will carry you while you do.
2. Which platform do your reviewers already use?
If privacy, risk, data or IT already work in one platform, start by checking what its AI governance extension can do.
3. Where does your AI actually run?
List the assistants, cloud services and models in use. A governance tool that cannot see or connect to them leaves you entering everything by hand.
4. What evidence will reviewers ask for?
Write down what an internal auditor or model risk reviewer will want to see for one AI system, then check the tool can produce exactly that.
5. Who will keep it current?
Name the owner of the register and the review cadence. Without one, any tool goes stale within a quarter.
6. What will it cost at your scale?
Ask for pricing against your expected number of AI systems and users, in writing, at the time you decide.
top platforms
Top AI governance platforms: why we do not publish a ranking
Lists of top AI governance platforms rank products against each other. For a regulated organisation the better test is fit: the platform your reviewers already use, that connects to where your AI runs and that produces the evidence your auditors expect. A product that ranks well in general can still be the wrong choice for your stack.
That is why we help clients build a short list against their own requirements instead of handing over a ranking.
what we do
What Kriv AI does in an AI governance tool engagement
Kriv AI is a consultancy, not a governance software vendor. We write the policy, risk tiers and approval rules to the frameworks you already answer to, such as the NIST AI RMF, ISO/IEC 42001 or the EU AI Act. We build the first AI inventory with your teams, including the tools staff adopted on their own. Then we either configure the controls in platforms you already own, such as Microsoft Purview for Microsoft 365 data, or help you select a dedicated platform when the inventory justifies one.
Our credentials on the Microsoft side are as Microsoft lists them: Solutions Partner for Cloud & AI Platforms · Partner ID 7036589. We do not claim formal alignment with any framework, and your compliance team keeps the final judgement on what the policy must say.
Responsible AI platform or governance program?
A responsible AI platform is a tool. Responsible AI is the outcome of a program: the policy, the people who approve, the training and the checks. We start with the program, because the tool can only record decisions someone has already made.
how an engagement runs
How the engagement runs
1. Discovery call
We learn which AI systems you know about, which policies exist and which platforms your reviewers use. Work is scoped and priced after this short discovery call.
2. Policy and inventory
We draft the risk tiers and approval rules with your owners and build the first inventory, including tools adopted without approval.
3. Tool decision
We recommend configuring what you own or selecting a platform, with the reasons and a short list if a purchase makes sense.
4. Setup and handover
We configure the chosen tool, run one AI system through the full intake and approval process with your reviewers, and hand over the documentation.
Related resources
Continue exploring
Straight answers
Frequently asked questions about AI governance tools, and how to choose one your reviewers will trust
What are AI governance tools?
They are software for keeping track of the AI an organisation uses: an inventory of AI systems, their owners and risk tiers, the approvals each one received and the checks run on it over time. Some are dedicated products; others are extensions of privacy, data or IT platforms, and some are controls inside tools you already license.
Do we need an AI governance platform?
Not on day one. If you have a handful of AI systems and no written policy yet, a well-kept register and a clear approval process come first. A dedicated platform starts to pay off when the inventory is large, many teams submit requests and reviewers need a shared workflow and evidence trail.
What are the best AI governance tools?
The best fit is the one that extends a platform your reviewers already use, connects to where your AI runs and produces the evidence your auditors ask for. Build a short list against those three tests and ask each vendor to demonstrate your own intake and approval process.
Is Microsoft Purview an AI governance tool?
Microsoft Purview is Microsoft's family of data governance, security and compliance tools. It does not replace an AI governance program, but for Microsoft 365 organisations its data protection and audit controls are an important part of governing assistant use. We configure those controls as part of AI governance work on Azure and Microsoft 365.
Is there a Gartner Magic Quadrant for AI governance?
Gartner publishes research on AI governance for its subscribers, and vendors cite it in their marketing. We do not reproduce analyst rankings. Analyst research is a reasonable way to find names for a short list; the decision should still rest on how each product handles your own policy, stack and evidence requirements.
What is a responsible AI platform?
It is a vendor term for software that supports responsible AI practice: inventories, risk assessments, approvals, monitoring and documentation. The platform records and routes decisions. The responsible part comes from the policy, the reviewers and the training behind it, which is where the real work sits.
How much does AI governance software cost?
Vendors price on different bases, such as the number of AI systems, users or modules, and prices may not be published, so ask for a written quote at your expected scale. Our own work to write the policy, build the inventory and set up the tool is scoped and priced after a short discovery call.
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