To pilot an AI workflow safely, start by testing a narrow, low-risk task with sample or synthetic information, not live customer records. Then check what the tool does with prompts and files, limit access, and keep every result in draft until a person has reviewed it.
A pilot is a way to learn, not a reason to hand customer information to a tool before you understand its data practices. A clear scope helps your team test whether AI is useful while keeping sensitive information out of the process.
Choose a task with limited consequences
Look for a repetitive task where an incorrect result would be easy to catch and would not make a consequential decision for a customer. For example, a team might test an internal checklist or draft a response template using public information or fictional scenarios.
For an initial test, avoid workflows involving decisions about a person’s health, finances, eligibility, or legal position. Also avoid tasks where an AI mistake could be sent to a customer or trigger an action without review.
Before choosing a workflow, ask:
- Can the task be tested with sample, synthetic, or properly de-identified data?
- Can a staff member check the output against a known answer or source?
- Can the workflow stay internal and produce a draft rather than an automatic action?
- Would an error be contained and straightforward to correct?
If those safeguards are hard to put in place, pick a simpler workflow. A pilot does not need real customer records to reveal whether a tool can help with a basic internal task.
Set a data boundary before anyone tests
Write down what information the pilot may use and what is off limits. For a tool that has not been reviewed and approved for sensitive information, exclude customer names and contact details, account or payment information, health details, confidential correspondence, and case records.
Using a different name or removing one identifier does not necessarily make a record safe to share. Information can sometimes be connected back to a person through details left in the text. Prefer fictional examples or data created specifically for testing. If your team considers de-identification, have an appropriate person assess whether the remaining information could still identify someone.
Document the task, approved data types, prohibited information, authorized users, and where outputs may go. Tell employees not to paste real customer information into the pilot unless the tool and workflow have been specifically reviewed and approved for it.
This is one part of a wider data-protection approach. Zevonix also shares guidance on business data protection that can help teams think about safeguarding information beyond an AI pilot.
Check the vendor’s data practices
Do not treat a statement that a provider does not train models on your data as a complete privacy review. Training is only one question. Find out what else happens to prompts, files, and outputs, and get the answers in writing where possible.
- Training: Can the provider use prompts, uploaded documents, or outputs to train or improve models? Does the answer differ by product, account type, or setting?
- Retention and deletion: How long are inputs and outputs retained? What can your business delete, and when? Are there exceptions?
- Logs and access: What is recorded, who can access it, and for what purpose?
- Connected tools: Can the AI reach email, cloud files, customer systems, or other integrations? Can you disable access it does not need?
- Controls: Can you limit users and permissions, manage access when staff roles change, and review usage?
Check the product and settings your business will actually use. A general vendor statement may not describe the configuration, integrations, or retention choices in your account. If answers are unclear, do not include sensitive information in the pilot.
For a broader process for evaluating technology providers, see Zevonix’s business IT vendor management guidance.
Keep the pilot contained
Limit the test to approved users and give the tool only the access it needs. Start without integrations to customer records or shared folders when those connections are not necessary. Use a dedicated test workspace if available, and keep generated content separate from approved business records.
Keep outputs in draft. Require a staff member to check accuracy, completeness, tone, and whether the response includes information that was not in the source. For any facts that matter, have the reviewer verify them against a trusted business record or source. Do not let the tool send messages, change records, or make customer decisions automatically during an exploratory pilot.
Risk management guidance from NIST’s Generative AI Profile can help businesses structure questions about potential risks and controls. It does not replace reviewing the specific tool, workflow, and data your organization plans to use.
Decide in advance when to stop or expand
Before testing, agree on what a useful result looks like and what would make the team pause. Keep the criteria practical and specific to the task.
- Stop if sensitive information enters the tool unexpectedly, the AI accesses data outside the approved scope, or the provider’s data practices are unclear.
- Pause and investigate if outputs contain repeated errors, omit important details, or cannot be checked reliably by a person.
- Consider expanding only when reviewers can consistently verify results, the workflow stays within its data boundary, and the team understands the tool’s data handling.
Keep a brief record of the workflow, the data used, review findings, issues, and decisions. Avoid storing sensitive prompt content in the notes themselves. If the pilot changes, such as by adding a new integration or data type, reassess it rather than assuming the original approval still applies.
Apply extra review in regulated settings
Healthcare and financial services businesses should involve their compliance and security leads before testing workflows that could touch regulated or confidential information. A healthcare team should check applicable HIPAA obligations and whether a business associate agreement is required for the proposed use. Do not assume that removing obvious identifiers or choosing a particular AI plan is enough.
Financial services firms should account for their existing supervision and recordkeeping obligations, and establish how AI-assisted work will be reviewed. FINRA’s annual regulatory oversight report is a relevant reference for firms considering technology use within their supervisory responsibilities. The appropriate review depends on the firm’s workflow and obligations.
For any business, the safer approach is to make the data boundary and review process fit the actual work. Zevonix provides managed AI services alongside managed IT, cybersecurity, and technology planning. To discuss a pilot for your business, contact the Zevonix team at (386) 260-0777.