AI Governance
AI adoption raises the stakes for everything data governance stands for, and adds questions of its own: which AI models and assistants are in use, what data feeds them, and who is accountable for their outcomes? As AI moves into business-critical processes, staying in control stops being optional, and the EU AI Act adds regulatory weight to that expectation.
In practice
Most organizations discover their real AI landscape the same way they once discovered shadow IT: it's bigger than anyone thought. Copilots, embedded AI features in SaaS tools, models built by enthusiastic teams, vendors quietly adding AI to products you already run. And now AI agents: with platforms like Copilot Studio, anyone can build agents that act autonomously on your data, with permissions nobody reviewed. AI governance starts with an inventory: which AI systems, agents and use cases exist, what data they touch, and who answers for them.
From there, governance becomes proportionate: light-touch oversight for low-risk assistants, rigorous controls for systems that influence decisions about people, money or safety. The EU AI Act's risk-based logic is a useful reference here, and we align with it, but the goal is operational control and trust, not paperwork. We are data and AI governance specialists, not lawyers: where formal legal interpretation is needed, we work alongside your legal counsel. We embed all of this in the data governance organization you already have, an extension of existing accountability rather than a parallel bureaucracy nobody asked for.
Good AI governance rests on good data governance. Security guardrails ensure sensitive information is discovered, classified and protected before Copilot rolls out. A semantic foundation (business glossary, semantic layer, ontology) ensures AI answers with your business definitions rather than guesses. And data quality determines whether outputs can be trusted at all. We connect these building blocks into one coherent path to being AI-ready: ambitious and defensible at the same time.
AI governance is knowing which AI systems you run, classifying their risk, assigning accountability, and grounding them in secure, well-defined, high-quality data, so AI adoption is both ambitious and defensible.
The symptoms we see most often
- Nobody has a complete list of AI tools and models in use across the organization.
- AI is on the risk register, but no one owns the response.
- Copilot is rolled out, or stalled, without clarity on what data it can reach.
- AI pilots multiply, but none can explain who is accountable if an output is wrong.
- AI answers confidently contradict your official reports, because it guesses at your definitions.
- Agents built in Copilot Studio or elsewhere act on company data, and nobody knows how many exist or what they can reach.
None of this argues against AI. It argues for governing it, before an incident or a regulator does it for you.
Our services
AI inventory & risk classification
A structured inventory of AI systems, agents and use cases, bought and built, including shadow agents nobody registered, classified by risk and business impact, so oversight effort lands where the risk is.
AI governance framework
Accountability, review and approval processes for AI systems and agents, embedded in your existing data governance organization: the same domains and owners, extended with AI responsibilities. Every agent gets a responsible sponsor, its own identity and least-privilege access, so autonomy never means anonymity.
AI risk & readiness assessment
A pragmatic assessment of where control is missing across your AI landscape, using the EU AI Act's risk-based approach as a reference, and a remediation plan focused on operational controls that build on what GDPR, DORA and NIS2 already require of you.
Copilot data readiness
The focused track that makes generative AI rollout safe: sensitive data discovered and protected, oversharing remediated, and a semantic foundation so Copilot answers with your definitions. It typically starts with our fixed-price Purview-Powered AI Risk Assessment.
How an engagement runs
Fair warning: this is business-heavy work, not a purely technological project. The tooling is the easy part; the value comes from workshops with your data owners, decisions about definitions, ownership and priorities, and the change management that makes new habits stick. We facilitate exactly that, alongside the implementation.
Inventory & classify
Map the AI landscape and classify systems by risk and business impact.
Define the framework
Accountability, review and approval, embedded in existing governance.
Secure & ground the data
Guardrails on sensitive data, semantic grounding for reliable answers.
Operate & stay in control
Standing oversight, monitoring and evidence of control as AI scales.
Typical deliverables
What we work with
We build on the capabilities of the Microsoft ecosystem, including Purview with DSPM for AI, Microsoft Agent 365 as the control plane for AI agents (agent registry, agent identities and access control), Fabric and Azure AI, complemented by our data governance partner ecosystem, so AI governance runs on the same foundation as your data governance.
- Microsoft Purview
- DSPM for AI
- Microsoft Agent 365
- Microsoft Fabric
- Azure AI
Frequently asked questions
Does the EU AI Act apply to us if we only use AI, not build it?
Very likely yes. Organizations that use AI systems have obligations too, lighter than those who build them, but real: think transparency and human oversight. Our readiness assessment maps where this touches your AI landscape in practice; for formal legal interpretation of your obligations, we work alongside your legal counsel rather than replacing them.
Do we need a separate AI governance board?
Usually not. AI governance works best as an extension of your data governance organization: the same owners, extended mandates, plus specific expertise (legal, security, AI) pulled in for risk reviews. A parallel structure mostly creates parallel confusion.
What about AI agents? Our people are already building them.
Agents raise the stakes: unlike a chat assistant, they act autonomously on your data and systems. Governing them means an agent registry (including the unsanctioned ones), an identity and least-privilege access per agent, a responsible sponsor for each, and lifecycle management so access doesn't outlive its purpose. Platforms like Microsoft Agent 365 make this manageable at scale, and we fold agent governance into the same framework as the rest of your AI landscape rather than treating it as a separate problem.
Where should we start if we have nothing?
With the inventory. It costs little, and it converts an abstract worry into a concrete, prioritized list. Combined with our fixed-price Purview-Powered AI Risk Assessment, it covers the two risks most organizations actually face this year.
Know where you stand in a few weeks
The fastest first step is a Data Governance Maturity Scan: an objective view of your current maturity, a benchmark against peers, and a prioritized roadmap.