A convincing demonstration is not enough to decide whether an AI tool is suitable for business work. First you need to define intended use, permissible information, people involved and consequences of an error. The evaluation must produce evidence and responsibility, distinguishing what has been proven from the supplier's declarations.
The operational proposal described here requires competent contacts before using real data. It is not a legal assessment, certification or promise of compliance.
Prepare users with different permissions and separate summary documents. Check whether a question can get information outside the scope or cause the system to perform an unexpected action. For a limited test, avoid write connections and unnecessary stand-alone functions.
OWASP categories dedicated to generative applications include prompt injection, information dissemination, and output management. They are ideas for building test cases: they do not in themselves prove that a product is safe or unsafe.
Record the tested case, the expected behavior, the outcome and the person responsible for the verification. Store only information permitted to reconstruct the test; a registry that copies requests and responses indiscriminately may itself contain data that needs to be protected.
Build a sample with the process contact: frequent requests, ambiguous questions, missing information and cases outside the perimeter. Define when the system should cite a source, declare a limit, or ask for human intervention.
Compare alternatives on the same sample and record corrections, missing hits, and human effort. A smooth answer may be inaccurate: the test is about content and its use, not just style. We do not attribute levels of accuracy to specific products here.
Before a test with real information, collect updated documentation on the data sent, places and subjects involved in the processing, storage, deletion, assistance access and possible reuse of the contents. The actual settings must correspond to the contractual conditions which are evaluated by the competent contact persons.
Start with synthetic data or licensed and minimized samples. If it is not clear what information can be included, discontinue the use of real data. A checked box in the interface does not in itself constitute verification of the treatment.
Lists test volumes, expected usage, licenses, consumption, connections, human controls, maintenance, and decommissioning. Clarify which warnings or limits allow you to spot an unexpected increase in usage.
The useful measure is linked to the verified activity: not only how much it costs to generate a response, but the work necessary to control and use it. Supplier prices and conditions change; This article does not publish rates or trade recommendations.
It indicates who controls, in which cases they must intervene and how they can stop or correct the process. The person in charge must be able to consult the sources and have the necessary context: an automatic or hasty confirmation is not an effective verification.
An internal suggestion and an action on a business system require different controls. Before extending a trial, check how to handle unavailability, repeated errors, version changes and user reports.
| Area | Proof or document required | Condition to be met |
|---|---|---|
| Access | Test with authorized and unauthorized users and documents. | No access outside the perimeter; anomalies to be resolved before extending. |
| Outputs | Agreed sample, expected sources and recorded errors. | Quality and review criteria defined by the contact person. |
| Data | Documentation and actual treatment settings. | Privacy review and admitted data confirmed. |
| Costs | Usage hypotheses and overall items, including human control. | Approved budget and limits for the trial. |
| People | Roles, reports, correction and arrest. | Responsible identified and real possibility of intervening. |
| Evolution | Service version and conditions that cause retesting. | Review expected when context changes. |
The sheet is an original work proposal and does not replace a specific technical or privacy analysis. For each point separate verified evidence, hypothesis and open question. A missing piece of data must remain missing, without being transformed into a positive judgement.
The return should explain why to continue, limit, change or discontinue the trial. Reports scope, version, sources, tested cases, limits and approvals. The NIST profile for generative AI addresses risks such as inaccurate content: in this article the warning is to motivate verification and oversight, not to declare a level of compliance.
To set up the comparison you can start fromAssessment. Per discutere accessi, dati e controlli, request a discussion on the topic of governance. Un percorso già avviato può essere seguito attraverso il Trusted AI Advisor.
