To pilot an AI tool without exposing company data, begin with a task that does not require confidential information. Test it with public, synthetic, or carefully de-identified material, limit who can use the tool, and review the vendor’s data practices before adding any real business information.
A small pilot is not automatically a safe pilot. Data may be affected by account settings, retention, connected apps, sharing, or vendor access as well as by whether prompts are used to train a model. Treat the evaluation as a series of approval gates, not as permission to experiment with any available tool.
Start with a bounded, low-risk task
Choose a task with a clear purpose and a result that a person can check. For example, a team might test whether an AI tool can organize public information or help draft a generic internal outline. Avoid tasks that make decisions about customers, employees, eligibility, finances, care, or legal matters.
Write down what the pilot is meant to determine. Is the tool useful, accurate enough for a human-reviewed draft, or easy for staff to use? A specific question makes it easier to judge the result without expanding the pilot into an open-ended trial.
Keep the task separate from systems and workflows that hold sensitive records. If a test requires access to real customer files, employee details, internal financial information, credentials, or regulated records, pause and reassess the use case before proceeding.
Choose safe material for testing
Use public information or synthetic examples created for the test whenever possible. Synthetic material should resemble the shape of the work without reproducing real names, account details, messages, or records. For instance, a made-up sample request can help test a drafting workflow without copying a customer’s actual request.
De-identification may reduce exposure, but it is not a blanket guarantee that information is safe to share. Details that seem harmless in isolation can sometimes identify a person or reveal business-sensitive context when combined. If you cannot confidently remove identifying or confidential details, use synthetic or public material instead.
Decide in advance which types of information are out of bounds. Make the rule concrete for employees: do not paste confidential business material, personal information, passwords, access tokens, or sensitive customer records into an unapproved tool. Make clear that a work-related purpose does not, by itself, make a tool approved for that data.
Review the vendor and its data handling
Read the vendor’s current privacy and security documentation for the specific product and account type you plan to test. Do not assume that settings or terms for a personal account are the same as those for a business account. The GSA guidance on buying AI can help frame questions to ask when evaluating a tool.
Ask the vendor or your account administrator:
- Are prompts, uploaded files, and outputs used to train or improve models? Can that use be disabled, and does the answer depend on the product or account settings?
- How long is data retained, and what deletion options are available when the pilot ends?
- Who may access the data, including vendor personnel and subprocessors?
- Where is data stored or processed, and what security controls protect it?
- How does the vendor notify customers about a security incident involving their data?
- What commitments apply under the contract, and do they cover the features the pilot will use?
A statement that prompts are not used for training does not answer every data-handling question. Review retention, access, integrations, sharing, deletion, and contractual terms too. The NIST Generative AI Risk Management Framework profile offers broader context for considering risks throughout an AI system’s use.
Limit access and disable unnecessary connections
Use an organization-managed business account if the vendor’s terms and settings are suitable for the pilot. Avoid personal accounts for company testing, since the organization may not be able to manage access, settings, or data consistently across them.
Give access only to the people who need to test the tool. Use individual accounts rather than shared logins, require multifactor authentication where available, and keep administrative permissions limited. Turn off connected apps, file access, or integrations that are not needed for the chosen task. Review whether users can share conversations or outputs externally, and set expectations for handling them.
Before launch, decide who will oversee the pilot and how to remove access and test data when it ends. This closeout plan should include any files or prompts saved outside the tool, as well as accounts, integrations, and shared links.
Collect feedback and make a clear decision
Keep a short decision log that records the task, test material, approved users, settings, and review owner. Ask participants to note whether the tool helped, where it made errors, how much correction was needed, and whether they encountered a data-handling concern. A human should verify any output before it is used or shared.
Set a review point before the test begins. At that point, decide whether to stop, revise the controls, or consider a broader evaluation. Do not expand access or introduce real company information simply because the first test seemed useful. Any change in task, data, or integration should prompt another review.
Businesses in healthcare, financial, legal, and government settings may need additional internal, security, or compliance review before testing with relevant records. The appropriate review depends on the information and intended use. Zevonix’s IT compliance support overview may help local businesses think through when to involve a knowledgeable technology partner.
Frequently asked questions
How can a business test an AI tool without using confidential data?
Pick a task that works with public or synthetic examples. If the task cannot be tested without confidential material, pause and review the need, the tool’s terms, and the organization’s controls before proceeding.
Does a vendor use business prompts or files to train its models?
It depends on the vendor, product, account type, and settings. Check the current product-specific documentation and confirm whether the relevant training or improvement use can be disabled. Also review retention, access, and deletion practices.
What should a business measure during an AI pilot?
Record whether the tool is useful for the selected task, how often its output needs correction, whether staff can use it appropriately, and whether any data-handling concerns arose. Have a person review outputs before use.
What extra precautions should regulated businesses take?
Before testing with regulated or sensitive information, involve the appropriate internal decision-makers and review applicable organizational requirements, vendor terms, and security controls. Start with public or synthetic inputs unless the use of real records has been specifically reviewed.
Make the next step a controlled one
A well-scoped AI pilot gives your team a way to learn while keeping data exposure in view. Zevonix provides managed AI services alongside managed IT and cybersecurity support for businesses in Palm Coast, Flagler Beach, Bunnell, and Flagler County. For help planning a cautious evaluation, contact Zevonix at (386) 260-0777.