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A secure AI-assistant policy should default to no sensitive data in public or unapproved tools, then create a documented approval path for narrowly defined business uses. It must connect data classification, vendor contracts, technical safeguards, human review, training, incident response and recurring audits. Employees should know exactly which assistant they may use, what information they may enter, what actions require approval and how to report a mistake.
The practical approach is to classify information before prompting, approve tools and use cases separately, configure least-privilege access, retain proportionate evidence and review the controls whenever a model, connector, contract, law or workflow changes. The policy below is a framework you can adapt to your jurisdiction and risk appetite.
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The policy is a governance control, not a one-time memo. It should make every AI use case answerable: who is using which assistant, for what purpose, with which data, under what technical and contractual protections, and with what human accountability. It should apply to employees, contractors, temporary staff and any supplier acting on the company’s behalf.
Write the policy so that a user can make a safe decision in seconds. A short decision rule, a current approved-tool list and an escalation route are more useful than a broad statement that employees should “use AI responsibly.” Assign an owner, normally information security or privacy with a business sponsor, and require review at least annually and after any material change.
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Set the policy’s scope
- Cover browser chatbots, enterprise assistants, coding copilots, document-analysis tools, meeting and transcription services, image or audio generators, retrieval systems, plug-ins, connectors, APIs, locally hosted models and automations that can take actions.
- Include prompts, uploaded files, pasted text, images, audio, retrieved documents, generated outputs, logs, feedback and metadata such as account identifiers and IP addresses.
- State whether the rules apply to personal accounts or devices used for company work. A simple default is that company information may be processed only in an approved company account and environment.
- Define who can approve an exception and how long it lasts. An exception should name the data, purpose, tool, safeguards, owner and expiry date.
How should a company classify data before anyone writes a prompt?
Classification must come before the prompt, not after an employee has pasted information into a chatbot. Use labels that match your existing records policy and explain them with examples. A workable model has eight levels.
| Class | Typical examples | AI rule |
|---|---|---|
| Public | Published web copy, public filings, approved press material | May be used in approved or public tools, provided the content is genuinely public and its owner has authorized reuse. |
| Internal | Routine procedures, internal project notes, non-public schedules | Use only an approved company environment unless the owner has confirmed that disclosure is harmless. |
| Confidential | Customer lists, pricing plans, vendor terms, unreleased strategy | Prohibited in public tools; requires an approved assistant, limited users and a recorded purpose. |
| Personal data | Names linked to identifiers, contact details, employee records | Minimize, redact or de-identify; use only where a lawful purpose and approved controls exist. |
| Sensitive personal data | Health, biometrics, precise location, financial or other specially protected information | Use only with specific legal, privacy and security approval and strong technical controls. |
| Regulated data | Protected health information, payment data, export-controlled material | Follow the applicable sector or national rules; an ordinary consumer chatbot is not an approved environment. |
| Privileged or legally restricted | Attorney-client communications, investigation files, draft legal advice | Keep out of AI tools unless counsel has approved the exact workflow and protections. |
| Trade secret or critical security data | Source code keys, credentials, vulnerability details, unreleased inventions | Default deny; use a segregated, access-controlled system only after formal approval. |
Public availability is not the same as permission to train or process data. The Australian Office of the Australian Information Commissioner (OAIC, 2024) notes that publicly available information is not automatically lawful for AI training, sensitive information generally needs consent or another valid legal basis, and unnecessary information should be excluded or deleted.
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- Send only the fields needed for the task. Replace names, account numbers and exact dates with neutral labels such as
Customer AandInvoice 17. - Prefer synthetic or de-identified examples for drafting, testing and demonstrations.
- Remove hidden spreadsheet columns, tracked changes, comments, embedded metadata and attachments that the model does not need.
- Check the output as well as the input. A response can reproduce a confidential fragment, infer a person’s identity or combine details into a new disclosure.
What should employees be forbidden from entering into public or unapproved assistants?
Use a clear default-deny rule: employees must not enter company or third-party sensitive information into a publicly accessible or unapproved assistant. The Centers for Medicare & Medicaid Services (CMS) states: “Never disclose or input PII, PHI, or any sensitive CMS data (including financial, health, vendor, procurement, evaluations, draft policies, or proprietary/business information) into publicly accessible AI platforms, chatbots, or prompts.”
Your policy should expressly list personally identifiable information, protected health information, financial records, customer and employee data, vendor and procurement information, security findings, credentials, legal-privileged material, export-controlled information, trade secrets and pre-decisional documents. Include prompts, uploaded files, copied snippets, screenshots and connector permissions in the prohibition.
Give users a safe alternative
A prohibition without an approved route encourages workarounds. Publish a current catalogue showing each approved assistant, allowed data classes, permitted purposes, user groups, region, retention setting and support contact. If a tool is not listed, the employee must use the intake process rather than guessing.
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How do you approve an AI assistant and a specific use case?
Approve the tool and the use case together. A vendor may be acceptable for public drafting but unsuitable for customer records, while the same model in a segregated enterprise tenant may pass review for a narrow workflow.
- Submit an intake. The business owner records the task, expected benefit, users, data classes, jurisdictions, volume, outputs and whether the system can send messages, edit records or call other services.
- Perform a risk review. Security, privacy, legal, procurement and the business owner assess confidentiality, integrity, availability, discrimination, intellectual property, records and regulatory obligations.
- Verify the provider. Obtain current contract terms and documentation on retention, training use, human access, subprocessors, processing regions, deletion, incident notice and support for access or deletion requests.
- Design the controls. Choose identity, role, network, DLP, redaction, logging, connector and approval settings. Remove features that the task does not need.
- Test with non-sensitive data. Check prompt injection, data leakage, inaccurate or biased output, access boundaries, logging and failure behavior before production use.
- Record the decision. Enter the approval, restrictions, owner, model and provider, region, retention, training setting, expiry or review date and incident contacts in the AI-use register.
- Launch and monitor. Train users, review logs and near misses, and suspend the workflow if its data, model, connector or contract changes materially.
Fields for an AI-use register
- Use-case name, business owner, technical owner and approved user groups.
- Assistant, model version, account or tenant, provider and subprocessors.
- Purpose, prohibited purposes and data classes allowed.
- Processing locations, cross-border transfers, retention and training-use settings.
- Identity, MFA, role permissions, connector scope, upload limits and rate limits.
- Human reviewer, source-check method, records retained and deletion method.
- Approval date, expiry or next review, exception reference and incident contacts.
Which vendor and contract controls matter most?
Do not rely on a marketing statement that a service is “secure.” Require terms and settings that match the data and the workflow. The Internal Revenue Service (2026) warns that public services may “retain, reuse, or resell entered information,” creating possible disclosure of internal strategies, plans or intentions.
| Control question | What an acceptable answer should establish |
|---|---|
| Are prompts and files retained? | Retention period, storage locations, backup handling and an option to disable or shorten retention. |
| Are inputs or outputs used for training? | Whether training is disabled by default, what feedback does, and whether the setting is enforceable by contract. |
| Who can access content? | Provider personnel access, support procedures, screening, access logging and customer-controlled encryption where available. |
| Where is processing performed? | Regions, transfer mechanisms, subprocessors and notice or objection rights for changes. |
| Can the company delete data? | Deletion of live and backup copies, export before deletion, legal-hold handling and a verifiable completion process. |
| How are incidents handled? | Prompt notification, cooperation, evidence preservation, affected-data details and a named 24-hour contact route if appropriate. |
| Can the company audit? | Independent assurance reports, questionnaires, testing rights and access to relevant logs or compliance evidence. |
| Can access be revoked? | Immediate account, token, API-key and connector disablement, plus return or deletion at termination. |
Keep the approved configuration as evidence. A contract that permits training while an administrator assumes it is disabled is not a control.
What technical safeguards should an approved assistant use?
Use the least permissive architecture that can perform the task. CISA and international partners’ 2024 guidance describes secure deployment as protecting confidentiality, integrity and availability while enabling protection, detection and response to malicious activity involving AI systems and related data.
Identity and access
- Use single sign-on and multi-factor authentication; prohibit shared accounts.
- Map roles to tasks and data classes. Give contractors and temporary users separate, time-limited groups.
- Use tenant isolation and separate development, testing and production environments.
- Restrict API keys, service accounts, model administration and export functions to named operators.
Data and network controls
- Encrypt data in transit and at rest, and manage keys according to your existing security standard.
- Apply DLP, redaction and secret-scanning rules before prompts, uploads and outputs leave the approved boundary.
- Disable plugins, browsing, code execution, file uploads, external connectors and autonomous actions unless the use case requires them.
- Limit retrieval sources to approved repositories and enforce document-level permissions at query time.
- Set rate limits and spend alerts to reduce abuse and accidental bulk disclosure.
Logging and monitoring
Log account, time, model, tool, connector, policy decision, administrator change and output destination. Where lawful and proportionate, retain the prompt or a structured description of the input, the output version used in a decision and the reviewer. Protect logs as sensitive records; access to them should be role-limited and auditable.
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How should human review work?
No employee should publish or act on AI output without a qualified reviewer. CMS guidance calls for validation against trustworthy sources, expert consultation and independent research. The reviewer remains accountable even when the assistant produced a plausible answer.
Use a risk-based review checklist
- Accuracy: verify calculations, quotations, dates, citations and claims against authoritative sources.
- Bias and fairness: look for stereotypes, disparate recommendations or missing perspectives.
- Privacy: check that the output does not reveal or infer unnecessary personal information.
- Security: inspect links, code, commands, files and suggested configuration for malicious or unsafe behavior.
- Copyright and confidentiality: confirm that supplied material may be used and that the response is not disclosing protected content.
- Suitability: ensure the result fits the audience, jurisdiction, policy and business decision.
Never make employment, credit, health, safety, legal or other consequential decisions solely from unverified AI output. Require a documented human decision and an appeal or correction route where people may be affected.
What records should the company keep?
Keep evidence long enough to meet legal, contractual and operational needs, but do not retain personal data indefinitely. The Information Commissioner’s Office (ICO, accessed 2026) recommends documenting data movement and storage, maintaining clear audit trails, deleting intermediate personal-data files when no longer required and considering de-identification or privacy-enhancing technologies.
- AI-use register entries and approval or exception records.
- Material prompts or input descriptions where lawful, plus the output version relied upon.
- Reviewer identity, source checks, edits and final decision.
- Administrator changes, access reviews, connector grants and security alerts.
- Training completion, policy acknowledgements and user attestations.
- Incidents, containment actions, notifications, root cause and corrective changes.
Define a deletion schedule for temporary uploads, test data, chat histories and exported results. Preserve records under a legal hold when counsel directs, and document why a record was retained.
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How do you train employees and contractors?
Training should be required before access and refreshed when the policy or tools change. ASD ACSC guidance addresses organizations using both self-hosted and third-party AI systems; CMS also emphasizes user understanding, stakeholder communication, documentation and continuing education.
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- Recognize each data class and apply prompt minimization, redaction and de-identification.
- Use the approved-tool catalogue and intake route; understand that personal accounts are not a workaround.
- Identify hallucinations, bias, prompt injection, malicious files and unsafe code.
- Verify sources and document human review before sharing or acting on output.
- Respect privacy, intellectual-property, records-management and confidentiality duties.
- Report accidental disclosure, suspicious output, account compromise and policy bypass immediately.
Use short scenario exercises: a request to summarize an employee medical file, a spreadsheet containing hidden identifiers, a connector asking for broad mailbox access and a convincing but false legal citation. Record attendance and test comprehension rather than treating a slide deck as proof of competence.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What should the AI incident playbook require?
Make reporting immediate and blame-free. Events include accidental disclosure, suspicious or biased output, prompt injection, a malicious file or connector, compromised credentials, unauthorized model changes and attempts to bypass the approved-tool list.
- Report and preserve. Contact the security or privacy channel, stop further use, preserve relevant prompts, files, logs and account details, and do not delete evidence.
- Contain. Revoke sessions, tokens and connectors; disable the assistant or affected workflow; restrict access to copied outputs.
- Assess. Identify the data, people, systems, jurisdictions, recipients and time period involved. Determine whether the provider or a subprocessor accessed it.
- Notify. Privacy, legal, security, business owners, affected customers or regulators are notified according to applicable law and contract.
- Remediate. Remove exposed data where possible, rotate credentials, patch the connector, tighten DLP or permissions and retrain users.
- Learn. Document root cause, decisions and evidence; update the register, policy and risk assessment before restoring the use case.
CMS explicitly requires reporting suspected or verified privacy breaches. Align the playbook with your existing breach-response process rather than creating a parallel mailbox that nobody monitors.
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Choose the least permissive tool that can perform the approved task, not the tool with the longest feature list. Score candidates against the same use case and record unresolved gaps.
| Comparison area | Questions to answer |
|---|---|
| Data handling | Retention, training use, human access, deletion and legal-hold support. |
| Isolation and location | Tenant separation, regional processing, transfers and subprocessors. |
| Identity and administration | SSO, MFA, roles, administrator separation, lifecycle controls and export. |
| Protection features | Encryption, DLP, redaction, secret scanning, connector permissions and sandboxing. |
| Evidence | Audit-log detail, export format, alerting, retention and integration with monitoring. |
| Reliability and quality | Known limitations, model-change notices, availability commitments and safe failure behavior. |
| Response and contract | Incident notice, audit evidence, support, liability, termination and deletion obligations. |
| Cost and operations | Licensing, implementation, administration, training and ongoing review effort. |
A lower-cost tool that cannot enforce retention or connector boundaries may create higher legal and security costs. Document the trade-off and reject a candidate when a required control cannot be verified.
How often should the policy be reviewed?
Review it at least annually and whenever a model, provider, connector, processing region, contract, law, data class or business purpose changes. The ICO notes that AI adoption can exacerbate existing risks and requires organizations to reconsider governance and risk-management practices.
- Reconfirm the approved-tool inventory and remove unused accounts.
- Sample logs and approvals to test that users follow data-classification and review rules.
- Reassess incidents, near misses, complaints, model updates and vendor changes.
- Check that retention, deletion, subprocessors and training settings still match the contract.
- Refresh training, examples, contacts and escalation paths.
- Ask business owners whether the assistant now performs a more consequential task than originally approved.
What policy language can you adopt?
The following clauses give employees an operational baseline. Adapt legal references, retention periods and approval roles to your jurisdiction.
- Approved use: “Use only assistants listed in the company AI catalogue and only for the purposes and data classes recorded for that assistant. Personal accounts and unapproved tools may not process company information.”
- Prohibited input: “Do not enter personal, health, financial, confidential, privileged, regulated, export-controlled, trade-secret or security-sensitive information into a public or unapproved assistant.”
- Minimization: “Use the minimum information needed. Redact identifiers and prefer synthetic or de-identified examples.”
- Human accountability: “A qualified employee must verify material output before publication or action. AI output alone may not determine a consequential decision.”
- Reporting: “Immediately report accidental disclosure, suspicious output, prompt injection, malicious files or connectors, compromised accounts and attempts to bypass this policy.”
- Change control: “Stop using an approved workflow and seek review when its model, provider, connector, data, region or purpose changes materially.”
What does a workable rollout look like?
- Appoint an executive sponsor and policy owner.
- Inventory current assistants, personal accounts, APIs, plug-ins and informal use.
- Publish the data-classification decision rule and default-deny list.
- Build the intake form, approval register and approved-tool catalogue.
- Review high-risk existing uses first, then configure identity, DLP, logging and connector restrictions.
- Train employees and contractors with realistic scenarios and a short knowledge check.
- Run a controlled pilot using non-sensitive data and test incident reporting.
- Open approved workflows gradually, monitor evidence and correct violations without normalizing them.
- Schedule the annual review and change-triggered reviews in the governance calendar.
Frequently Asked Questions
Can an employee use an AI assistant with anonymized customer data?
Only if the organization has verified that the data is effectively de-identified and the approved use case permits it. Rare combinations of dates, locations or attributes can re-identify a person, so privacy or security review should confirm the transformation before use.
Should AI prompts be treated as company records?
Treat prompts and outputs as records when they support a decision, create a contractual or regulatory obligation, or contain information needed to explain an action. The retention period should follow the relevant records schedule rather than a blanket rule for every casual experiment.
What is prompt injection in a company workflow?
It is an instruction hidden in a document, web page, message or file that attempts to make the assistant ignore its rules or take an unauthorized action. Restrict retrieval and connector permissions, isolate untrusted content and require human approval for external actions.
Who should own an AI-use register?
A central security, privacy or risk function should maintain the register, while each business owner remains accountable for the accuracy and ongoing necessity of their entry. Shared ownership prevents a catalogue from becoming stale when a team changes its tool or purpose.
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Treat a material model, training, retention, subprocessor or processing-region change as a review trigger. Pause sensitive processing if the new terms or behavior no longer match the recorded approval, then document the decision to reapprove, restrict or retire the workflow.
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