Governance that enables AI rather than blocking it
Governance has a reputation for slowing things down, usually because it arrives after the fact as a list of restrictions. Designed properly it does the opposite, giving teams a clear boundary within which they can move quickly without escalating every decision. We build enterprise data governance for AI that makes the safe path the easy path, grounded in the requirements of the EU AI Act and GDPR rather than generic best practice.
Our AI data governance services
We cover framework design, regulatory readiness and the practical controls that make governance work day to day.
AI governance framework design
We design a governance framework fitted to your organisation, covering the policies, roles, approval paths and review points that determine how AI gets built and deployed.
Regulatory readiness
We assess your AI systems against the EU AI Act, GDPR and any sector-specific requirements that apply, and identify what needs to change before enforcement dates rather than after.
Data classification and access control
We establish what data can be used for which AI purposes, and put in place the classification and access controls that enforce it technically rather than relying on policy alone.
Model risk and evaluation policy
We define how models get assessed before deployment and monitored afterwards, covering accuracy thresholds, bias testing, documentation standards and the criteria for taking a system out of service.
Governance for generative AI
Generative AI raises its own questions around prompt content, data residency, provider retention and output review. We put controls in place that let teams use these tools productively without creating exposure.
Audit trails and lineage
We build the logging and lineage that let you show what data trained a model, what informed a given output, and who approved each step, which is what regulators and enterprise clients increasingly want to see.
Governance frameworks and tooling
We work to recognised standards and implement governance through tooling your teams already use where possible, rather than adding another platform to maintain.
AI data governance cases
We work with organisations in regulated sectors including healthcare, transport and logistics, and professional services, where governance requirements shape what can be built.
Our AI data governance process
Understanding your risk position
We assess the AI systems you have or plan to build, the data they use, and the regulatory and contractual obligations that apply to your organisation.
Identifying the gaps
We compare your current controls against what your risk position requires, and prioritise the gaps by exposure rather than by how easy they are to close.
Designing the framework
We build the governance framework itself, covering policies, roles, decision rights and approval paths, sized to your organisation rather than copied from an enterprise template.
Implementing controls
We work with your technical teams to put the controls in place, covering classification, access management, logging and evaluation, so governance is enforced by systems rather than goodwill.
Embedding and reviewing
We help you establish the review cadence, reporting and training that keep governance current as regulation shifts and your AI estate grows.
Enabling users to be part of the design and delivery process in such an artful way makes the leaders of this business quite unique.
The WeAreBrain team stood out because they understood the problem we were trying to solve, and how we aimed to solve it.
Their speed and their attitude were impressive — the speed of their work is limited by your speed only!
FAQs
Questions about governing AI systems and the data behind them? Find the answers here.
What is data governance for AI?
Data governance for AI is the set of policies, controls and accountabilities determining how data is used to build and run AI systems. It covers which data can be used for what purpose, who approves it, how decisions are documented, and how systems are monitored once live.
How is AI governance different from traditional data governance?
Traditional data governance largely concerns data at rest and its use in reporting. AI governance also has to address model behaviour, including how systems perform across different groups, how outputs are explained, and how a system is monitored for drift after deployment.
What does the EU AI Act require from us?
Obligations depend on how your systems are classified under the Act, ranging from minimal-risk through to high-risk with substantial documentation, testing and oversight requirements. The first step is classifying what you have, which is where most of our engagements begin.
Do we need governance in place before we start building AI?
You need enough to make safe decisions, though not a complete framework. We often run lightweight governance alongside an early project and formalise it as the AI estate grows, which avoids stalling everything while a policy document gets written.
How does governance apply to generative AI specifically?
Generative AI raises additional questions around what data goes into prompts, where it’s processed, what the provider retains, and how outputs are checked before use. Organisations handling sensitive data often address this through private AI deployment alongside policy controls.
Can you work with our existing risk and compliance teams?
Yes, and we generally prefer to. Our role is usually to bring the technical understanding of how AI systems actually behave, working alongside the people who already know your regulatory and risk position.
