An AI integration security checklist can help your team spot access and workflow risks before an AI tool connects to company files or software. The goal is not simply to confirm that the integration works. It is to understand what information it can reach, what actions it can take, who reviews its work, and how your team can disable it.
Use the checklist below during planning and testing. Adjust it to fit the specific tool, data, and business process involved. If an integration may handle sensitive or regulated information, bring in the appropriate privacy or compliance lead before using real records.
Map the connection before enabling it
Start by documenting the proposed connection in plain language. Identify the AI tool, the company system it will connect to, the intended task, and the information needed for that task. Include any file repositories, mailboxes, business applications, or data sources the integration may reach.
- Name the purpose. Describe the task the integration is meant to perform, such as finding information in approved files or drafting a response from selected records.
- List the data sources. Identify the folders, records, accounts, and systems the integration may access. Note whether those sources contain confidential, personal, health, financial, or other sensitive information.
- Identify the people affected. Consider who will use the tool and whose information may appear in its results.
- Write down the expected actions. Separate reading or drafting from changing records, sending messages, approving transactions, or deleting information.
This map gives reviewers a clear basis for deciding whether the proposed connection is limited to its stated purpose. It also makes it easier to notice access that is broader than the pilot requires.
Check permissions and identity
Review the permissions the integration requests before granting access. Ask whether it needs every file, mailbox, or feature in the connected system, or whether a smaller set will support the task. Apply the principle of least privilege: give the integration only the access it needs, and avoid using a broad administrator account when a narrower permission is available.
- Compare requested access with the task. If the tool only needs to summarize a particular set of documents, test whether it can be restricted to those documents rather than a whole shared drive.
- Use a separate account or service identity where supported. This can make the integration's access easier to identify, review, and revoke than access tied to an employee's personal account. Check who owns and monitors that identity.
- Review inherited permissions. Test whether the integration can find files or records that a specific user should not be able to see. Do not assume that connecting a tool automatically preserves every intended access boundary.
- Confirm authentication and credential handling. Know where credentials are stored, who can manage them, and what process is used to remove or replace them.
For administrator-focused guidance on identity and access management, consult CISA's recommended identity and access management practices. If you are assessing what happens to information submitted to an AI tool, see this business checklist about AI data and training as a separate part of your review.
Test with limited access and sample data
Do not make the first test with broad access to live company information. Begin with sample data or a controlled test environment, and use an account with deliberately limited permissions. Confirm that the AI can complete the intended task without seeing unrelated information.
- Try asking for information from an approved source, then check whether the result matches what that source contains.
- Test requests for information the account should not be able to access. Confirm that the tool does not return restricted files or details.
- Check whether results include information from a different user's files, records, or workspace.
- Try unusual or out-of-scope requests that might lead the tool to reveal more than the task requires.
- Record what was tested, which account and data were used, what happened, and any changes made to permissions.
Keep the test narrow enough that your team can inspect the results. A successful demonstration of the intended task is not enough by itself. The review should also look for unintended access and responses that cross user or data boundaries.
Separate reading from actions that change things
An integration that can read or summarize information has a different risk profile from one that can edit records or act on a user's behalf. Treat each permission to change something as a separate decision, rather than assuming it is needed because the connection works.
For actions with meaningful consequences, set a human approval step. This may include sending an external message, changing a customer or patient record, approving a transaction, deleting information, or making a commitment on behalf of the business. Specify who reviews the proposed action, what they must verify, and how the action is recorded.
- Test the integration in read-only mode where that is sufficient for the task.
- Require a person to review and approve consequential actions before they occur.
- Make it clear to the reviewer what information the AI used and what it proposes to do.
- Decide how staff should handle an uncertain, incomplete, or incorrect result.
A written AI acceptable-use policy checklist can help clarify staff responsibilities and which uses need review. The NIST Generative AI Profile is another resource for organizations considering AI risks and controls.
Ask the vendor about data handling
Before connecting company information, get clear answers about how the vendor handles it. Review the terms that apply to your specific product and configuration, rather than relying on general statements about the service.
- Is submitted information retained, and for how long?
- Is company information used to train or improve models? Can that use be restricted?
- Which providers or subprocessors may handle the information?
- How can your organization request deletion, and what happens to backups or logs?
- What security controls and incident notification processes does the vendor describe?
- Does the vendor's documentation address the type of sensitive or regulated information your team plans to use?
Have the appropriate internal lead review the answers and relevant terms before using sensitive data. A tool's ability to connect to a system does not, by itself, establish that the configuration is appropriate for every kind of company information.
Rehearse how to disable and recover
Plan for a problem before launch. Identify who can pause the integration, revoke its credentials or permissions, and contact the vendor if needed. Then test the shutdown process in a controlled setting.
- Confirm who is authorized to disable the connection and where that person can find the required controls.
- Revoke the integration's access and verify that it can no longer reach the connected system.
- Check how your team will identify and review changes the integration already made.
- Determine how to restore affected information or return to the previous workflow.
- Document how work continues while the integration is unavailable.
A rollback plan should cover more than switching off a feature. It should address access removal, review of prior activity, data recovery where needed, and a workable alternative for staff. For a control-oriented set of questions about keeping an AI stack under organizational control, see TechRadar's questions for evaluating AI control.
Use a clear launch decision
Before launch, review the test record with the people responsible for the connected system, the business process, and the data involved. Proceed only when the access matches the task, the expected actions have appropriate human review, and the team has tested how to stop the integration. If a concern remains unresolved, keep the connection limited or pause the launch while you address it.
Zevonix provides managed IT support, proactive maintenance, and technology services for Daytona businesses. Its stated focus includes helping local businesses streamline operations, enhance security, and support sustainable growth through proactive IT management. For help reviewing an AI connection alongside your wider technology environment, contact Zevonix at (386) 260-0777.
Frequently asked questions
Should an AI integration use a separate account or service identity?
Where supported, a separate identity can make the integration's access easier to distinguish from an employee's access. Keep its permissions limited, assign an owner, and include it in access reviews and shutdown procedures.
How can a business test whether AI exposes information to the wrong users?
Use a limited test account and sample data. Try requests for information that account should not see, and check whether results include content from other users or restricted areas. Record what happened and correct access issues before expanding the test.
What actions should an AI tool require a person to approve?
Consider approval for actions that change records, send messages, approve transactions, delete information, or make commitments. Define who reviews each action and what they need to check before approving it.
What extra checks apply to health or other regulated information?
Involve the relevant privacy or compliance lead, review the vendor's terms and data handling, and confirm that the proposed use and configuration have been assessed for the information involved. Do not connect real sensitive records until those checks are complete.
Conclusion
A careful pre-launch review should establish what an AI integration can access, what it can change, how people supervise it, and how to shut it down. Start with a limited test, document the results, and expand access only when the controls fit the task. Contact Zevonix if you would like support evaluating an AI integration within your business IT environment.