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The reliable way to keep company data out of AI tools is layered control: define what may never be submitted, approve a specific product and configuration rather than “AI” in general, reduce the data each assistant can reach, block or detect risky uploads, and govern retention and incidents. A vendor’s promise not to train on your prompts is useful, but it does not by itself mean that data is not stored, reviewed for abuse monitoring, exposed through excessive permissions, or automatically compliant.
Start with a data policy employees can actually follow
Employees need a short rule they can apply before pasting a prompt, uploading a file, or connecting a repository. Name approved tools and approved use cases. Treat every other service, browser extension, plug-in, API endpoint, autonomous agent, and personal account as unapproved until security reviews it.
Define prohibited data by category
Use categories that match your business. A practical baseline is:
- Passwords, API keys, private certificates, recovery codes, session tokens, and other credentials or secrets.
- Personal data, including employee, customer, patient, student, or applicant records.
- Customer-confidential material, contracts, support cases, and nonpublic partner information.
- Source code, infrastructure configuration, vulnerability details, and unreleased product designs.
- Unreleased financial results, forecasts, acquisition plans, pricing strategy, and board material.
- Regulated records or information covered by a legal hold, confidentiality agreement, or sector rule.
These categories are a policy design starting point, not a universal legal classification. Your privacy, security, legal, and records teams should map them to the organization’s own data inventory and jurisdictions.
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Allow only narrow, documented exceptions
An exception should identify the exact tool, plan, feature, region, data class, user group, and retention setting. For example, “summarize redacted public support text in the approved enterprise workspace” is enforceable; “AI is allowed for business work” is not. Require an owner, an expiry or review date, and a way to revoke the exception.
Teach minimization and redaction with examples
Training should show employees how to remove names, account numbers, identifiers, secrets, and unnecessary context. Ask whether the task can be completed with a synthetic example, a short excerpt, a hash, a schema, or a locally processed result. A prompt that contains only the fields needed for a transformation is safer than an entire ticket export or repository.
Make the safe action explicit: do not paste a real access token to ask why a request failed; replace it with a clearly fake value and rotate the real token if it was exposed. Do not upload a complete customer list to generate a formula when a few fictional rows demonstrate the requirement.
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Approve a precise service, not a brand name
“Enterprise AI” is not a single security configuration. Chat products, APIs, developer assistants, connected agents, and consumer accounts can have different data terms and administrative controls. Record the precise product, subscription, feature, integration, region, and intended workflow in the approval register.
Use this approval checklist
- Training use: Determine whether prompts, files, outputs, or feedback are used to train or improve models. OpenAI’s business privacy information says listed business and API services do not use customer inputs or outputs for training by default; OpenAI’s API data-controls documentation separately states that API data is not used to train or improve models unless an organization explicitly opts in. Confirm that the statement applies to the exact service and account type being purchased.
- Retention and application state: Ask how long prompts, outputs, uploaded files, conversation history, telemetry, and abuse-monitoring records remain available; whether administrators can set shorter periods; and how deletion requests work. OpenAI’s current API documentation describes abuse-monitoring logs that may include prompts and responses and are retained for up to 30 days by default, subject to stated exceptions and approved controls. That is a stated maximum default, not a breach frequency or exposure statistic.
- Human and automated review: Establish whether content can be reviewed for abuse prevention, troubleshooting, legal obligations, or support, who can access it, and what audit trail exists.
- Permissions and connected services: List every repository, mail system, document store, plug-in, agent, browser extension, and external API that can receive data or retrieve context. Verify the scopes granted to the integration.
- Identity and administration: Require central identity, multifactor authentication, role-based administration, user and group lifecycle controls, and the ability to disable the service quickly.
- Audit and investigation: Confirm that administrators can search interaction records, export relevant events, distinguish users and applications, and investigate policy exceptions.
- Contract and subprocessors: Review the governing agreement, data-processing terms, subprocessors, geographic processing, breach notices, deletion commitments, and legal-use restrictions.
- Plan eligibility: Write down the subscription tier and region that provide each control. Do not assume a feature described on an enterprise page is included in your tenant.
Keep evidence for each decision: the terms in force, configuration screenshots or exports, test results, the approving owner, and the date for reassessment. Recheck after a plan change, newly enabled connector, model change, or material contract update.
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Understand what “not used for training” does and does not mean
A no-training commitment answers one question: whether the provider uses specified customer content to improve its models under the stated terms. It does not answer how long content is retained, whether abuse-monitoring systems can process it, whether a connected application stores a copy, who inside your organization can retrieve it, or whether the workflow meets a regulatory or contractual requirement.
OpenAI’s API documentation illustrates the distinction: it describes no training by default while separately describing abuse-monitoring logs and a default retention period of up to 30 days. OpenAI’s business privacy page and API documentation cover particular services; do not generalize them to consumer products or to a different feature without checking its terms.
Microsoft’s enterprise documentation describes encryption, tenant isolation, identity and permission inheritance, sensitivity labels, retention, and auditing for commercial Copilot experiences, while noting that controls vary by subscription plan. Treat those statements as product commitments to verify in your own tenant, not as independent proof that every deployment is safe.
Reduce what an assistant can retrieve
Retrieval-connected assistants can expose information that the signed-in user is already allowed to view. Microsoft states that Copilot surfaces organizational data for which the individual has view permission. This means an assistant can make excessive or stale permissions easier to discover and combine; it does not create a substitute for least privilege.
Clean source permissions before enabling retrieval
- Inventory SharePoint sites, OneDrive folders, mailboxes, code repositories, ticket systems, databases, and file shares that the assistant or connector can search.
- Find broad groups, “everyone” access, inherited permissions, external guests, dormant accounts, and project folders that were never closed.
- Remove access that is no longer needed. Separate confidential projects and regulated records from general collaboration areas.
- Apply sensitivity labels, retention policies, and repository-specific restrictions where your service supports them.
- Test with representative accounts: an ordinary employee, a manager, a contractor, an external collaborator, and an administrator. Ask the assistant for documents each account should and should not see.
- Review connector scopes after every feature or agent change, and monitor for newly indexed locations.
Do not rely on a successful login as evidence of correct authorization. Test retrieval boundaries and inspect the underlying source permissions.
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Block or detect risky submissions
Policy and training reduce mistakes; technical controls provide a second line of defense. Use the controls available in your environment, and test their actual coverage rather than promising a universal block.
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Enterprise DLP
Configure data-loss-prevention rules to identify secrets, regulated identifiers, confidential labels, source-code patterns, or customer records when users paste text or upload files. Depending on the product and destination, a rule may warn, require justification, redact, quarantine, or block. Test prompts, attachments, copy-and-paste, mobile access, APIs, and alternate domains separately.
Endpoint and browser controls
Endpoint security, managed browsers, application controls, and network filtering can restrict unapproved consumer AI sites, extensions, desktop clients, and file transfers. Microsoft guidance for government environments identifies DLP and endpoint protection as relevant ways to prevent sensitive content from being copied into consumer AI applications. Coverage depends on destinations, device ownership, offline use, personal browsers, and configuration; document those boundaries.
Maintain a live AI inventory
Record sanctioned and unsanctioned services discovered through procurement, single-sign-on logs, DNS or proxy telemetry, browser inventories, expense data, and employee disclosures. For each entry, capture owner, business purpose, data classes handled, connectors, plan, region, and status. An inventory lets security prioritize a risky unknown service instead of assuming that procurement records are complete.
Govern interaction records and deletion
Decide which prompts, outputs, administrator actions, and policy events must be retained, for how long, who may inspect them, and how they are deleted. Align the schedule with legal holds, privacy obligations, investigations, and the vendor’s actual storage behavior. Keeping everything forever increases exposure; deleting everything immediately can undermine an investigation or a required record.
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Microsoft describes Purview capabilities for retaining, deleting, auditing, and searching AI interaction records. Confirm which capabilities are included in your subscription and whether they cover the product, connector, and account type you deployed. Define a documented process for legal holds and for access to interaction records by security, privacy, human-resources, or legal teams.
Handle an accidental disclosure as an incident
If someone submits a secret or restricted data to an unapproved service, do not wait for certainty about whether a human viewed it. Preserve the facts needed for assessment and follow your incident process.
- Identify exactly what was sent, including files, prompts, outputs, account, time, destination, feature, and connected services.
- Notify the security and privacy owners, and involve legal or compliance teams when contractual, regulatory, employment, or customer obligations may apply.
- Contain access: disable the account or connector, revoke sessions, remove shared links, and prevent further uploads.
- Rotate exposed credentials, keys, certificates, and tokens. Invalidate them at the issuing system rather than merely deleting the chat.
- Ask the provider what deletion, retention, access, and abuse-monitoring rules apply to that service and plan. Do not assume that deleting a conversation removes every log or backup.
- Assess affected data subjects, customers, systems, and jurisdictions; document notifications or other obligations determined by the responsible teams.
- Record the root cause and improve policy, DLP rules, permissions, training, or approval controls.
The appropriate response time and notification duties depend on the data, contract, jurisdiction, and facts. Your incident plan should define escalation contacts and decision authority before an event occurs.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Compare control options on the dimensions that matter
When evaluating two or more products or architectures, compare capabilities in the same workflow rather than comparing marketing labels.
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|---|---|---|
| Destination coverage | Does it cover approved enterprise apps, consumer websites, APIs, browser extensions, desktop clients, mobile devices, and connected agents? | Supported-destination list and test results for each channel. |
| Submission control | Can it detect, warn, redact, justify, quarantine, or block sensitive prompts and file uploads? | Rules, sample detections, false-negative and false-positive observations. |
| Identity and permissions | Does it enforce central identity, least privilege, source permissions, labels, and connector scopes? | Configuration export and tests using representative accounts. |
| Lifecycle governance | Can administrators retain, delete, audit, search, export, and place holds on interaction records? | Retention policy, audit events, deletion test, and eDiscovery procedure. |
| Service and contract scope | Which plan, region, feature, training rule, abuse-monitoring process, and subprocessors apply? | Contract terms, provider documentation, plan entitlement, and review date. |
No independent performance statistic establishes that one product universally prevents disclosure. Validate the exact configuration against your employees’ devices, destinations, connectors, and workflows.
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A practical rollout sequence
Phase 1: discover and classify
Interview teams, inspect identity and network telemetry, and list every AI service and integration in use. Map each workflow to data classes and business owners. Start with high-risk workflows such as source-code analysis, customer support summarization, legal drafting, financial forecasting, and automated actions.
Phase 2: publish minimum rules
Release the prohibited-data categories, approved-use list, exception process, reporting channel, and examples of redaction. Require managers and contractors to complete role-specific training.
Phase 3: pilot an approved configuration
Choose a bounded group and a small set of use cases. Configure identity, DLP, endpoint, connector, retention, and audit controls. Test allowed and denied prompts, file uploads, retrieval permissions, account offboarding, deletion, and incident escalation.
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Phase 4: expand with measured gates
Approve additional teams only after the pilot owner resolves identified permission, detection, and retention gaps. Keep a rollback path: disable the connector, revoke access, or return the service to a smaller group without deleting evidence needed for an investigation.
Phase 5: review continuously
Reassess when the vendor changes terms, plans, models, regions, connectors, or administrative controls. Review new services discovered in telemetry, stale source permissions, DLP exceptions, and incident patterns. Treat AI governance as an operating process, not a one-time procurement questionnaire.
Common mistakes to avoid
- Approving a vendor name instead of a workflow: A chat product, API, extension, and agent can have different storage and permission behavior.
- Equating no training with no retention: Training use, abuse monitoring, application history, backups, and connected-system copies are separate questions.
- Turning on retrieval before fixing permissions: The assistant may faithfully surface information that users already have excessive rights to view.
- Blocking only the most famous websites: Employees can use APIs, extensions, personal devices, mobile apps, or newly created services.
- Ignoring outputs: Generated text can contain confidential source material and can be copied into tickets, email, code, or public documents.
- Assuming compliance from an enterprise label: Compliance depends on data, jurisdiction, contract, plan, feature, and configuration.
- Failing to rotate exposed secrets: Deleting a prompt does not invalidate a credential that was included in it.
What good governance looks like
A mature program can answer five questions for every AI workflow: What data enters it? Which exact service and plan process it? Which people, connectors, and agents can retrieve or receive it? How are submissions and interactions detected, retained, audited, and deleted? Who can stop the workflow and respond when something goes wrong?
Use vendor documentation as a statement of the provider’s terms and controls, then verify the entitlement and behavior in your own environment. Data minimization, least privilege, employee training, DLP, endpoint protection, identity controls, and records governance remain necessary even when a provider offers strong enterprise protections.
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