Before deploying AI in a public institution: 7 risks that need governance
· BEXTIO
Artificial intelligence creates opportunities, but it also creates new dependencies and new control points.
A public institution should not wait for an incident before organizing governance.
Seven risk families deserve particular attention. 1.
Confidentiality and data.
Sensitive information may be sent to a service that was not intended to process it.
Institutions need clear rules for which data are authorized, in which environment, for what purpose and for how long. 2.
Errors and plausible but incorrect answers.
A well-written output can still be wrong, incomplete or grounded in the wrong source.
Important use cases need verification mechanisms and test sets. 3.
Excessive access.
A document assistant should not allow a user to reach information they would not normally be entitled to see.
Access controls should reflect institutional roles. 4.
Vendor dependency.
An institution can become dependent on a model, format, hosting environment or provider.
Data export, portability, future cost and exit plans should be assessed. 5.
Invisible changes.
A provider may change a model, terms, behavior or architecture.
An authorization should therefore never be assumed to apply automatically to all future versions. 6.
Unclear accountability.
When a result is wrong, who is accountable: the tool, the user, the manager, the institution or the vendor?
Serious governance always assigns a business owner and responsible authority. 7.
Lack of reversibility.
A system can become too expensive, unavailable, non-compliant or simply less useful.
The institution needs to know how to continue operating, recover assets, migrate and retire the system properly.
These risks do not mean innovation should be blocked.
They mean control should be proportionate to risk.
An internal assistant working on public documents may follow a lighter path.
A public-facing system, a system using sensitive data or a system influencing significant decisions should receive more testing, validation, monitoring and stop authority.
Good governance is therefore not the opposite of experimentation.
It enables experimentation without surrendering control.