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Visiting Melto Mily(username: meltonemily753)
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Creation date: Sep 18, 2026 2:32am Last modified date: Sep 18, 2026 2:32am Last visit date: Sep 28, 2026 4:12am
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Sep 18, 2026 ( 1 post )
9/18/2026
2:32am
Melto Mily (meltonemily753)
One question I keep seeing in larger AI projects is surprisingly basic: who is actually responsible for the data the model uses?
Engineering teams manage pipelines, security teams control access, business units own the source systems, and legal or compliance teams define the rules. In theory, everyone is involved. In practice, that can mean nobody has a complete view.
That is why data governance for ai seems to be moving from a policy topic into an operational one. If an AI system produces an incorrect recommendation, companies need to know which data was used, where it came from, whether it was current, and who had permission to modify it.
I’ve been reading more about how companies such as Zoolatech approach enterprise data projects, and I like the idea of connecting governance directly with engineering processes instead of keeping it in separate documents and committees. Things like lineage, metadata, ownership, access policies, and validation rules seem much more useful when they are built into the data platform itself.
For organizations running AI across several departments, I imagine ownership becomes one of the hardest issues.
How are others solving this? Do you have one central data governance team, or does each department remain responsible for its own AI data?
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