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Before employees use ChatGPT, Gemini, Copilot, or another AI tool for business work, inventory the tools, set rules for what data may be shared, and verify each provider’s data and security practices. Then secure accounts and devices, restrict integrations, review consequential outputs, train staff, and prepare for incidents. AI can expose information even when the business systems connected to it are otherwise secure, so these controls belong in your everyday cybersecurity plan—not just in an AI policy.
Why does AI use create security risks for a small business?
An employee can disclose sensitive information simply by entering it into a prompt, uploading a file, connecting an account, or giving an AI agent permission to act. The exposure may occur outside the tools and storage locations the business already manages. Whether information is retained, used to train models, or shared with service providers depends on the particular service and its terms; do not assume that all AI tools handle data alike.
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The checklist below is intended for small businesses, including those without a formal cybersecurity program. NIST SP 1300 is designed for small and medium-sized organizations with modest or no cybersecurity plans. Treat the checklist as a practical baseline, then adapt it to your data, legal obligations, systems, and risk tolerance.
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What should a small business check before employees use AI?
Work through these controls in order: find the AI use already happening, set boundaries, understand data handling, and reduce the chance that a compromised account or integration can cause harm.
1. Inventory every AI tool and connection
Make a list of AI apps, browser extensions, APIs, plug-ins, and agents in use—not only services purchased by the business. Include tools staff adopted independently and AI features built into existing products. For each entry, record:
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- Its business purpose and the accounts or systems it can access.
- The kinds of information it receives, stores, or produces.
- Whether it can take actions, such as sending messages or changing records, rather than only generate suggestions.
Ask teams directly what they use and check the business’s accounts and integrations where practical. This inventory is the basis for deciding which tools to approve, what data they may handle, and where access needs to be removed.
2. Publish an acceptable-use policy
Write a short policy employees can follow at the moment they are about to use an AI tool. It should identify approved tools, prohibited information, when a human must review outputs, how to handle retention concerns, and who can approve a new tool or integration. Explain how employees report a mistake or a suspicious tool.
Cyber.gov.au’s AI checklist asks businesses to understand the benefits and risks of AI, what information may be shared, and the vendor’s data practices. Make those decisions explicit rather than leaving each employee to interpret them.
3. Classify information before prompting
Set data rules before staff paste text or upload a file. Keep confidential, personal, regulated, customer-identifying, credential, and unreleased business information out of an AI service until the business has verified the relevant collection, storage, ownership, retention, and model-training terms. A convenient prompt is not a reason to bypass existing handling requirements.
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4. Vet the vendor before approving use
Review the specific service and plan the business intends to use. Check the provider’s written terms and ask for clarification where needed. A vendor’s general privacy statement or marketing claim may not answer what happens to your particular inputs, account, or connected data.
- Data handling: What information is collected, where it is stored, how long it is retained, and who owns submitted data?
- Model training: Is submitted business data used to train or improve models, and what settings or contractual terms govern that use?
- Security: What encryption, access controls, and security commitments apply? Check relevant attestations, such as ISO 27001, and how the provider relates its practices to frameworks such as the NIST AI Risk Management Framework.
- Service chain: Which subprocessors can handle the data, and where are they located?
- Incident and deletion terms: How can the business request deletion, how are breaches or incidents reported, and whom can the business contact?
- Operational fit: Does the service support the access controls, audit logs, review processes, and integrations the business needs?
Record the answers and the person who approved the service. If a material question remains unanswered, limit use to information the business is comfortable disclosing under the known terms, or do not approve the tool for business data.
5. Control identity and access
Require unique passwords and multi-factor authentication (MFA) for AI accounts, especially those connected to business data. MFA can use an authenticator app, a one-time passcode, or a hardware token such as a USB device; the FTC recommends requiring MFA to protect sensitive information. Give users only the access their role needs, keep administrator accounts separate from everyday accounts, and remove access promptly when someone leaves or changes roles.
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Include connected accounts in access reviews. An AI tool may still be able to read or act on a business account after an employee stops using the tool itself, so review and revoke those permissions when access is no longer justified.
6. Secure devices and networks
AI accounts are used from endpoints and networks that need ordinary cybersecurity protection. Turn on automatic updates, encrypt devices and sensitive data, secure routers, separate guest Wi-Fi from business systems, and maintain offline or otherwise isolated backups. Protect accounts with strong passwords and use network protections appropriate to your business.
The FTC’s small-business guidance highlights updates, backups, encryption, strong passwords, and network protection. Backups should be part of recovery planning, not merely present: the business needs to know how it will restore important systems and information if an incident disrupts them.
7. Limit the power of AI integrations
Review every API key, plug-in, agent, and connected account as a route into business systems. Restrict permissions to the minimum needed for the stated purpose, protect keys from exposure, and avoid unrestricted access to sensitive systems. Keep a record of which person or service owns each connection.
Log important actions and monitor for activity that does not fit the integration’s purpose. For an agent that can change records or send communications, consider which actions should require human approval rather than happen automatically. CISA’s secure-deployment guidance emphasizes confidentiality, integrity, availability, and detecting and responding to malicious activity. Those goals apply to the systems around an AI tool as well as the tool itself.
8. Require human review for consequential outputs
Decide in advance which uses require a qualified person to check the result before it is sent, acted on, or deployed. Review AI-generated code, customer communications, financial analysis, legal text, and security recommendations for accuracy, context, and harmful or manipulated content. Identify who is accountable for that review; “the AI produced it” should not be the final approval.
Cyber.gov.au’s checklist asks where and how outputs will be fact-checked. The UK National Cyber Security Centre identifies unreliable or manipulated outputs as a risk. A review process should match the consequence of an error: the more a decision affects customers, finances, security, or legal obligations, the more important it is that a competent person verify the underlying facts and reasoning.
9. Train staff and reinforce the rules
Teach employees how to recognize phishing, classify information, use only approved tools, prompt safely, and report a suspected disclosure or account problem. Explain the practical reason behind each restriction: a prompt or integration can send business information beyond the systems employees normally use.
Training should keep pace with new tools and threats. FTC and CISA guidance both treat workforce education as a core control for small businesses. Include new staff in onboarding and repeat relevant instruction when the business approves a new tool or changes its rules.
10. Prepare for incidents and recovery
Document who can isolate an AI account or integration, who contacts the provider, how evidence is preserved, and who decides whether customers or regulators must be notified. Include a restoration plan for affected systems and data. Test the plan and revise it after exercises or real incidents.
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Make sure the contact and recovery steps are usable by the people expected to carry them out. If a vendor is involved, staff should know how to reach its incident contact and what information to provide. CISA’s secure-deployment guidance covers detecting and responding to malicious activity; FTC small-business guidance also addresses practical security measures such as backups and updates that support recovery.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How should you compare AI services for business use?
Compare the specific services and plans your business is considering against the same questions. A consistent record makes gaps visible and helps you avoid approving a tool based only on convenience or an unsupported vendor claim.
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1Repair Windows errors before they cause bigger problems2Scan for outdated or missing drivers - takes under a minute3Clear out junk files and repair common Windows errors| Area | What to verify |
|---|---|
| Data collection and retention | What inputs and account data are collected, where they are stored, and how long they are kept. |
| Training and ownership | Whether business data trains models, who owns submitted data, and how deletion works. |
| Geography and service chain | Storage locations and the subprocessors that may handle data. |
| Security controls | Encryption, access controls, security commitments, and relevant attestations. |
| Account administration | Availability of MFA and single sign-on (SSO), plus controls for managing users and privileges. |
| Visibility and incidents | Audit logs, incident-notification terms, and named support or incident contacts. |
| Outputs and integrations | Output-review controls, permissions available to integrations, and how important actions can be monitored. |
| Business fit | Support and total cost alongside the security and operational requirements. |
Evaluate what the business can verify in terms, settings, or written answers. If a comparison item is not documented, mark it as unknown rather than treating silence as an assurance. Use the result to decide whether to approve the tool, narrow its permitted use, or decline it.
What should you do if someone shares sensitive information with an AI tool?
Use your incident process rather than assuming that deleting a prompt resolves the exposure. The appropriate actions depend on the service, data involved, and permissions granted, so the business should establish ownership and escalation paths before an incident occurs.
- Tell the designated incident lead and record what was shared, when, by whom, and through which tool or account.
- Assess whether the information was sensitive and whether the AI service or an integration has continuing access to business systems.
- Contain access where appropriate, such as disabling an account or revoking an integration, and contact the provider through its incident channel.
- Preserve relevant evidence and document the provider’s response and any deletion or containment steps.
- Determine whether customers, regulators, or other parties must be notified, then restore affected systems and update controls based on what happened.
Do not promise that an input has been erased or excluded from training unless the service’s terms and the provider’s response establish that outcome. The incident lead should follow applicable legal and contractual obligations when deciding notifications.
How can a small business put this checklist into practice?
Start with the highest-exposure decisions instead of trying to write an elaborate AI program all at once. Assign a responsible person to maintain the inventory and policy, and bring in appropriate legal or security expertise when the business handles regulated data or cannot assess a material vendor commitment.
- Find current use: Ask staff and review business integrations to populate the AI inventory.
- Set immediate boundaries: Publish approved tools and clear rules for sensitive information while vendor reviews are underway.
- Review services: Compare each tool’s data practices, security, permissions, and incident terms against the vendor questions above.
- Apply baseline controls: Check MFA, least privilege, device updates, encryption, backups, and network separation.
- Assign review and response: Name the people responsible for checking consequential outputs and managing incidents.
- Revisit the decisions: Update the inventory and policy when tools, integrations, data use, or threats change, and after incidents or exercises.
For a small organization, a concise written policy, a current tool register, and clearly assigned responsibilities are more useful than rules staff cannot find or follow. The controls should make legitimate work possible while preventing employees from having to guess about data, permissions, and accountability.
Frequently Asked Questions
Can an employee use a personal AI account for company work?
Only if the business has approved that service and understands the terms and controls that apply to the account being used. A personal account may not provide the contractual protections, administrative visibility, or access management the business requires.
Does turning off model training make it safe to paste confidential data?
Not by itself. Training is only one part of data handling; retention, storage, access, ownership, subprocessors, deletion, and contractual commitments also matter.
Should a small business ban AI completely?
That is a business decision, not a universal security requirement. An inventory, risk-based data rules, vendor review, and human accountability can help a business decide whether to permit limited uses, approve particular tools, or prohibit AI for certain work.
Who should own an AI security policy in a small company?
Assign a named person to maintain it and make sure business leadership can approve risk decisions. Security, legal, operations, and teams using AI may all need input, but staff should know who has final responsibility for tool approval and incident escalation.
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