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Use generative AI at work only through a service and account your employer has approved for the specific task, and only with information allowed by your company’s data-classification rules. Before you paste a prompt or upload a file, identify the information’s classification, verify the exact product, plan and feature, and check how that service handles prompts, files, responses, retention, training, human access and connected tools.
An enterprise label, encryption, redaction or a “no training” statement is not a universal safety guarantee. Controls differ by account, subscription, geography and configuration. If the policy or service scope is unclear, stop and ask your security, privacy or IT team. That short delay is safer than trying to undo an unauthorized disclosure.
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Start with the two decisions that determine whether AI use is allowed
1. Read your employer’s AI-use policy
Find the current policy in your employee portal, security handbook or acceptable-use standard. Look for rules covering approved AI services, consumer versus employer-managed accounts, application programming interfaces (APIs), browser tools, web search, agents, plug-ins, connectors, uploads and customer or personal information. Some organizations approve a service for drafting public copy but prohibit it for source code, personnel records or client work. Others require a particular tenant, identity login or retention setting.
Approval is usually a combination of the service and the use case. “The company allows AI” is not precise enough. You need an answer to questions such as:
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- Is this exact application approved, or only a separately managed enterprise or API deployment?
- Is my account signed in through the organization’s identity system, or am I using a personal account?
- Is this task approved for the information class involved?
- Are browser search, file uploads, agents, plug-ins and external connectors covered?
- Must a human review the output before it is sent to a customer, regulator or the public?
Do not infer that a consumer interface has the same terms as an employer-managed account. The contract, tenant configuration and administrative controls may be different.
2. Classify the material before sharing it
Use your organization’s labels and handling instructions. The categories below are prompts for checking policy, not a universal legal taxonomy.
| Information you are considering sending | Questions to ask | Typical safe action |
|---|---|---|
| Public or already published material | Is it genuinely public, current and free of hidden metadata or confidential comments? | Use only an approved service; remove unnecessary internal context. |
| Internal business information | Does it reveal strategy, pricing, forecasts, vendor terms or nonpublic operations? | Use only an approved account with the required controls and least data necessary. |
| Confidential or proprietary information | Would disclosure harm the company, breach a contract or give a competitor an advantage? | Do not submit unless the policy explicitly allows that class in that exact service. |
| Personal, customer or employee information | Does it contain names, contact details, identifiers, financial information, health information or employment records? | Use a permitted workflow with privacy and access controls; otherwise redact or escalate. |
| Credentials, keys and regulated or classified material | Could the material enable access, trigger a reporting duty or fall under a special regime? | Never paste credentials or secrets. Follow the designated secure process and ask the responsible team. |
| Source code, unreleased products and intellectual property | Would the content expose a security weakness, invention, design or roadmap? | Submit only where the employer has approved the tool, repository integration and data class. |
When the label is missing, contradictory or unfamiliar, treat the item as uncertain rather than assuming it is harmless. Classification controls only help when they are applied correctly and connected to the AI workflow.
Verify the exact service, account and feature
Write down the product name, sign-in account, subscription or tenant, enabled features and region before using it. A service may have different data practices for a managed workplace tenant, a consumer login, an API call, web search, an agent or a third-party connector.
| What to verify | Why it matters | Evidence to request |
|---|---|---|
| Account and identity | A personal account may not inherit the employer’s contract, access controls or audit trail. | Approved application list, tenant name and identity-management instructions. |
| Plan and subscription | Retention, training, logging and administrative features can vary by plan. | Current product terms, contract addendum and administrator configuration. |
| Prompt, file and response handling | Uploaded documents and generated answers may have different retention or access rules. | Provider documentation that covers each content type. |
| Web search | A prompt sent to a search provider can follow separate handling practices from the model interaction. | Terms for the search feature, destination and query logging. |
| Agents, plug-ins and connectors | An agent may retrieve data from other systems or send actions to third parties. | Approved connector list, scopes, subprocessors and action permissions. |
| Residency and legal requirements | Storage or processing in another country may conflict with contractual or regulatory requirements. | Data-location commitments and the organization’s privacy assessment. |
Marketing language is a starting point, not a substitute for the terms that govern your account. Ask the service owner or security team to confirm which controls are actually enabled.
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What the Microsoft documentation illustrates
Microsoft describes organizational Copilot and Copilot Chat as covered by its Data Protection Addendum and Product Terms, with encryption, tenant isolation, inherited permissions and sensitivity labels, retention policies and auditing. It says prompts, responses and Microsoft Graph data are not used to train foundation models in that enterprise context. The same documentation warns that “the specific controls and policies will vary depending on the underlying subscription plan.” Read the page for the exact product and subscription rather than applying those statements to every Microsoft experience: Microsoft’s enterprise data-protection documentation.
Microsoft also describes web-search queries as going to Bing under separate handling practices and tells customers to check agent terms. A protected chat therefore does not automatically make a connected search, plug-in or agent safe for the same data.
What the OpenAI Zero Data Retention statement illustrates
OpenAI says Zero Data Retention (ZDR) is available to eligible API customers and that prompts and responses are not retained after processing under that arrangement. It also says customer content is not available to personnel for review and that enterprise data is not used to train models unless the customer opts in. Those commitments are scoped to eligible API customers, not every account or service. OpenAI’s September 22, 2026 update says Private Safety Processing is rolling out to API customers in phases; it notes an exception in which images flagged for potential child sexual abuse material may be retained for manual review and reporting. See the ZDR terms and scope and have your organization confirm eligibility and configuration.
Read the data terms before the first sensitive prompt
Use this checklist for the exact deployment your employer approved:
- Content covered: Determine whether the terms address prompts, uploaded files, generated responses, images, audio, telemetry and data retrieved through connectors.
- Retention: Find the normal retention period, deletion process, backups, legal holds and whether administrators can set a shorter period.
- Model training: Confirm whether content is used to train or improve models, and whether an opt-out applies to your plan or only to a contractually defined service.
- Human access: Check whether provider personnel, reviewers or support teams can access content, under what controls and for what purposes.
- Provider role: Establish whether the provider acts under your organization’s instructions or uses data for its own purposes under the applicable contract.
- Subprocessors and connected services: Identify who receives data when search, storage, agents, plug-ins or integrations are enabled.
- Location: Confirm where content is processed and stored and whether that meets the company’s contractual and regulatory requirements.
- Deletion and exit: Know how to delete chats and files, what remains in logs or backups and what happens when an employee leaves.
No single setting answers all of these questions. “Not used for training” does not necessarily mean “not retained,” “not reviewed,” “not copied to a connector” or “approved for confidential data.”
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Minimize the information you send
Even when a workflow is approved, share the smallest useful amount. Data minimization reduces the impact of a mistake and limits what appears in logs, responses and downstream systems.
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- Define the output. State exactly what you need, such as a plain-language summary, a list of missing fields or a formula explanation.
- Remove direct identifiers. Replace names, email addresses, phone numbers, account numbers, employee IDs and customer IDs with neutral labels.
- Remove secrets. Never include passwords, API keys, access tokens, private certificates, recovery codes or authentication headers.
- Generalize distinctive details. Change exact dates, locations, contract numbers, product codenames and unusual figures when the task does not require them.
- Use synthetic examples. Create a structurally similar sample with invented values when you are testing a prompt, format or code pattern.
- Limit the excerpt. Send only the rows, paragraphs or fields required for the answer instead of an entire workbook, mailbox or repository.
- Check the result. Make sure the redacted sample still cannot be combined with obvious context to identify a person, customer, deal or secret.
Redaction is a risk-reduction measure, not proof that information is anonymous or policy-approved. A rare combination of job title, date, location and transaction value can identify a person even after a name is removed. If the task requires the original data, use the approved protected workflow rather than weakening the data yourself.
Example: turning a finance request into a safer prompt
Instead of uploading a payroll export containing employee names, bank details and salaries, ask the approved tool to analyze a synthetic table with columns such as employee_group, month and gross_pay_total. If the calculation requires real records, confirm that the service and account are approved for employee data, retain only the fields needed for the calculation and keep the source file in the organization’s permissioned storage.
Use the controls your organization already owns
Permissions and least privilege
Keep source documents in systems with role-based access and give the AI integration only the scopes it needs. An agent that can read an entire drive or send messages on your behalf has a larger failure radius than one limited to a single folder and a draft-only action. Review inherited permissions before connecting a repository; an AI assistant can surface information that the user is already technically able to access but did not expect to see.
Sensitivity labels and classification
Apply the organization’s sensitivity labels to files and messages, and verify that the AI product honors them. Labels are useful only when the service and tenant support the relevant enforcement. A label displayed in a document does not by itself prevent a user from copying text into an unapproved website.
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Data loss prevention and endpoint coverage
Microsoft documents Purview capabilities that can use classification, sensitivity labels, auditing and DLP policies with supported generative-AI applications. It describes policies that can warn or block users on onboarded Windows computers from sharing sensitive information with third-party generative-AI sites. Coverage depends on the application, operating system, licensing, policy configuration and whether the site is onboarded. Read the Microsoft Purview guidance and ask IT whether browser-based tools, personal devices, mobile apps, agents and uploads are included.
Audit, retention and legal hold
Use logs to see which applications receive sensitive prompts, who accessed connected data and whether a response was exported. Confirm whether AI conversations are covered by the company’s retention schedule, legal holds and records-management process. An audit trail helps investigate an event; it does not prevent the initial disclosure.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Match the control level to the task
| Task example | Potential exposure | Appropriate approach |
|---|---|---|
| Rewrite a publicly posted announcement | Low, provided the draft contains no hidden internal comments. | Use the approved service and remove internal notes or unreleased details. |
| Summarize an internal meeting | Names, strategy, personnel matters or customer commitments may be revealed. | Use the approved tenant, apply the required label and limit the transcript to the necessary section. |
| Analyze customer transactions | Personal and financial information may be exposed or re-identified. | Use an approved protected workflow, minimize fields and confirm privacy and retention requirements. |
| Debug proprietary source code | Code may reveal vulnerabilities, credentials or intellectual property. | Use only an approved coding environment with repository permissions and secret scanning; otherwise create a synthetic reproduction. |
| Ask an agent to update records or send mail | An incorrect answer can become an external action. | Restrict permissions, require confirmation before execution and retain a human review step. |
Risk is determined by the data, the action and the service path, not by whether the task feels routine.
Review every output before it leaves the company
AI can be inaccurate, omit a qualification or reveal information in an unintended way. Check calculations against the source, verify citations and dates, inspect code for secrets and security flaws, and compare summaries with the original record. Follow the normal approval process before using output in a customer communication, financial report, filing, contract, public post or production system.
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What to do when the policy or service scope is unclear
- Pause the submission. Do not “test” an uncertain service with real data.
- Record the decision needed. Note the data class, task, service name, account, feature and proposed fields.
- Ask the designated team. Contact security, privacy, legal, compliance or IT through the approved channel.
- Request a documented answer. Ask whether the exact use, data class, plan and connector are approved, and which settings are mandatory.
- Use a safe fallback. Work with public or synthetic data, an approved internal tool or a conventional non-AI process while waiting.
Public documentation cannot reveal your employer’s contract, tenant configuration, approved data categories or incident procedures. The organization’s designated team must resolve those questions.
If you accidentally share company data
Report the event promptly under your company’s incident-response procedure. Include the service and account, date and time, information involved, files or prompt text, recipients or connected systems, and whether the content included credentials or personal information. Do not try to conceal the event by deleting local evidence or repeatedly resubmitting altered prompts. Security staff may need the original details to request deletion, revoke credentials, assess notification duties and contain access.
If a password, token or private key was included, treat it as compromised and follow the organization’s rotation process immediately. If customer or employee information was involved, use the privacy or breach-reporting route rather than relying on an informal message to a coworker.
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Use a risk framework without treating it as permission
The National Institute of Standards and Technology’s AI Risk Management Framework is voluntary and was released on January 26, 2023; its Generative AI Profile followed on July 26, 2024. NIST describes AI use as a cybersecurity and privacy risk-management task and notes that AI can create re-identification risks and reveal greater insights about people. Use the framework to organize questions about governance, mapping, measurement and management, not as proof that a particular tool is safe or certified. See NIST’s AI Risk Management Framework and its Cybersecurity, Privacy, and AI guidance.
How government guidance should be read
An Australian Protective Security Policy Framework advisory says entities can use OFFICIAL information with generative AI technologies, but only subject to their existing security responsibilities, provider-assurance requirements, authorization processes and appropriate treatment of classified information. That is a jurisdiction-specific policy example, not blanket permission for every employer, country, provider or information class. Read the Australian advisory only in its government framework and follow your own organization’s rules.
A short pre-send checklist
- Is the service and account approved for this exact task?
- What is the information’s classification?
- Have names, identifiers, credentials and unnecessary proprietary details been removed?
- What happens to prompts, files, responses, logs and connector data?
- Are retention, deletion, training use, human access and data location acceptable?
- Are labels, permissions, DLP, audit and least-privilege settings active?
- Does web search, an agent or a plug-in introduce a separate recipient?
- Who will review the output before external use?
- What is the approved escalation path if any answer is uncertain?
If you cannot answer one of these questions from the policy, contract or configured control, do not send the company data yet. Use a synthetic example or ask the responsible team.
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