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This is a valid concern, and I would zoom the lens slightly out.
The core risk is not public AI usage itself. It is the growing gap between the speed of adoption and the maturity of governance.
In most cases, people are not leaking data out of carelessness.
They are optimizing their work inside systems that were never designed for AI-mediated decisions.
When friction disappears, policies that rely primarily on individual judgment stop scaling.
Two clarifications matter here.
First, data sensitivity is not binary.
Risk rarely comes from obviously confidential data, but from combinations.
Partial datasets, internal assumptions, or anonymized information that becomes re-identifiable when crossed with other inputs.
These slip through precisely because each element feels harmless in isolation.
Second, the deeper issue is implicit governance.
When there are no explicit rules about what can be shared, by whom, in which context, and with what accountability, the system naturally rewards convenience.
Not because people have bad intent, but because that is what the current design incentivizes.
For that reason, I would not frame the decision as allowing or blocking public AI tools.
A more useful question is: which types of work and decisions can safely use public tools, and which require controlled environments with traceability, data classification, and clear ownership?
A practical starting point is to define simple guardrails.
No customer-identifiable data.
No financial figures tied to real entities.
No contracts, credentials, or personal data.
For anything beyond that, provide a sanctioned alternative, such as a private LLM, approved tooling, or mandatory redaction.
Bans tend to push usage into the shadows.
Clear boundaries, decision thresholds, and accountability bring it into the open.
That is where governance becomes enabling rather than restrictive, and where trust is actually built.
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