Learn why governance programs often fail the moment AI goes live.
Discover the symptoms of “performative governance” and how to escape it.
Understand how to embed governance into your everyday data flows, not bolt it on.
See how CluedIn turns governance into a byproduct of doing data right.
Enterprises are racing toward AI adoption. Copilots are embedded into tools. LLMs are deployed into customer service. Generative AI is writing product descriptions, summaries, even board reports. But here’s the reality: AI without clean, contextual, and governed data is just expensive guesswork.
A recent Actian survey revealed that while 75% of companies self-report strong data governance maturity, only 22% can show auditability across data domains. Even fewer have reliable lineage or cross-platform enforcement.
This gap isn’t just an inconvenience. It’s a liability.
Because AI isn’t forgiving. It doesn’t pause to question the lineage of the data it’s trained on. It doesn’t hesitate before executing based on stale or orphaned records. And once it's in production, there’s no “undo” button.
On paper, most governance programs look polished. role-based access controls, policy documents and stewardship workflows. But dig deeper, and the cracks appear:
Ownership is unclear between teams.
Business rules are defined but unenforced.
Data lineage lives in diagrams, not systems.
Sensitive fields move between environments without traceability.
Why? Because traditional governance treats control as a separate project — a policy layer bolted on after the fact. It’s easy to build a policy framework. It’s hard to make it real.
One Gartner analyst put it bluntly:
“You don’t govern data. You govern how data moves and evolves across people, systems, and time.”
That takes more than committees.
Real governance isn’t a compliance checklist. It’s an operational fact. It means:
You can trace every field back to its source.
You know which business rule applied, when, and by whom.
You prevent data from being used unless it meets minimum quality thresholds.
You don’t lose context as data flows between systems.
Companies like Maersk and Telefónica Tech are moving in this direction, focusing not on policies, but on embedding governance into how data is created, changed, and consumed.
At CluedIn, we take a radically different approach.
We believe governance should be the result of good system design, not a layer added after. That’s why our platform doesn’t let you move data without tracking what happened, who touched it, and why.
Here’s what that looks like in practice:
When an AI agent fixes a duplicate or merges conflicting records, the rationale is logged.
When a data steward approves a change, lineage is automatically updated.
When a policy triggers a workflow, it runs inside Teams or Microsoft Purview, not in a siloed tool no one checks.
This means governance happens in real time, inside the tools people already use. You don’t need a “governance project.” You need a platform that bakes control, traceability, and context into everyday operations.
Let’s say your AI model is generating customer churn predictions. If your input data lacks recent service interaction records, or if account closures are misclassified due to bad merges, your model is not just wrong, it’s confidently wrong. That’s dangerous. In regulated sectors, it’s risky. In consumer-facing roles, it’s brand-damaging. We’ve seen this play out:
A financial services client generated compliance reports based on unverified aggregations, which regulators flagged as misleading.
A healthcare provider used an LLM to auto-generate patient summaries, but half the training data excluded recent prescriptions due to broken joins.
In both cases, governance wasn’t missing. It was just misestimated.
The new world of data is fast, messy, interconnected. Governance needs to match that pace. That’s where agentic data management, like CluedIn, comes in. By embedding governance into the agents themselves, we ensure that:
Quality thresholds are enforced before data moves.
Every change is versioned and reversible.
Approvals go to Slack, Teams, or wherever your people work.
Audit logs are built as a byproduct, not an afterthought.
All this running 24/7 at no extra costs. This isn’t a vision. It’s how our customers will running CluedIn when our agents are fully launched.
Many enterprises already use Microsoft Purview to define governance policies, manage access, and catalog data across their estate. It’s a powerful compliance tool, but on its own, it doesn’t fix the data.
That’s where CluedIn steps in. CluedIn makes Purview actionable. While Purview governs the who, what, and where, CluedIn governs the how. It operationalizes policy by cleansing, mapping, validating, and activating data in real time.
Policy Meets Practice:
Define retention or sensitivity policies in Purview. CluedIn agents enforce them during data processing, automatically masking PII or routing approvals.
Lineage That’s Live:
Purview tracks metadata lineage. CluedIn enhances it with real-world event lineage, who merged what, when, and why.
One UI, Unified Controls:
Because CluedIn is natively integrated with Azure, approvals and alerts from CluedIn flow into the same channels Purview already uses, like Microsoft Teams or Defender.
Together, they deliver a complete loop:
Discover with Purview.
Fix and activate with CluedIn.
Monitor and enforce across both.
This integration isn’t just convenient, it’s transformative. It closes the loop between governance intent and operational execution. And that’s exactly what AI-driven organizations need.
If AI is going to act on your behalf, then your governance system has to act in the background. That’s why we built CluedIn the way we did. Not to replace your governance tools, but to render them nearly invisible.
You don’t need more PowerPoints. You need fewer surprises. The companies that grasp this aren’t just AI-ready. They’re AI-resilient.
Because when everyone’s chasing automation, the winners won’t be those who move fastest. They’ll be the ones whose data can be trusted, every step of the way.