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Keep an AI agent from exposing company documents by treating the knowledge base as an access-control system, not as a prompt-writing exercise. Clean up permissions in SharePoint, Drive, and other repositories before indexing; apply least privilege and sensitivity labels; use a managed enterprise service with written data-protection terms; set retention and audit controls; restrict connectors; and test denied access with real user roles.
Prompt instructions such as “do not share confidential files” cannot repair an inherited repository permission. The agent should retrieve only content the requesting identity could already open, while administrators have clearly documented authority to audit, retain, export, or delete managed-account data.
Why an AI knowledge base is primarily an access-control problem
Retrieval-augmented agents search connected repositories and then generate an answer from the results. If a file is incorrectly shared with a broad group, the agent can surface that file to every member of the group. The model is not a substitute for the source system’s authorization checks.
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What the agent should and should not do
- It should enforce the same user, group, site, folder, file, and label restrictions that apply in the source system.
- It should not grant access merely because a user can phrase a request convincingly.
- It should not reveal titles, snippets, citations, or metadata from documents the user cannot open.
- It should return a refusal or an access request path when a document is outside the user’s authorization.
Prepare repositories before indexing
Privacy settings cannot compensate for oversharing in SharePoint, Google Drive, file servers, ticketing systems, or wikis. Perform this cleanup before an agent receives access.
1. Inventory every searchable location
List each repository, site, shared drive, database, mailbox, and connector that the agent could query. Record the owner, business purpose, region, data classification, synchronization method, service account (if any), and expected user population. Include inherited shares, public links, guest accounts, nested groups, and stale project memberships; these are common sources of unintended access.
2. Remove inherited and stale access
Review permissions at the folder and document level, not just at the top-level site. Remove former employees, dormant groups, anonymous links, and broad “everyone” grants. Where practical, replace individual exceptions with small role-based groups whose owners review membership on a schedule. Keep a record of the changes so a later audit can show why a document was reachable.
3. Classify information
Separate public, internal, confidential, regulated, and highly restricted content. Apply sensitivity labels or equivalent metadata where the platform supports them. Define handling rules for personal data, health or financial records, source code, legal work, credentials, security investigations, and merger or acquisition material. Decide whether restricted classes should be excluded entirely, indexed only for a small group, or exposed through a purpose-built workflow.
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4. Decide what must never be indexed
Exclude secrets, private keys, password stores, raw identity documents, litigation holds, and other data for which natural-language retrieval adds unacceptable risk. Use connector filters, repository boundaries, or a separate tenant rather than relying on a model instruction to suppress results.
Select an enterprise-managed service
Use a business or enterprise offering with contractual data-protection terms and administrative controls. Consumer accounts may have different training, retention, residency, and support arrangements. Obtain the applicable data-processing agreement and identify the legal entity, processing regions, subprocessors, deletion process, and incident-notification terms.
OpenAI states that ChatGPT Business and Enterprise data is not used for training by default unless an organization explicitly opts in. Microsoft’s enterprise data-protection documentation describes Copilot protections that apply organizational identity, permissions, sensitivity labels, retention policies, audit, and administrative settings. Google Workspace materials describe customer-data protections and no-training-by-default treatment for Workspace Agents, while retention and connector behavior depend on the Workspace product and configuration. Verify the exact edition and current terms before enabling a connector.
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| Control axis | Microsoft Copilot / Copilot Studio | ChatGPT Business / Enterprise | Google Workspace Gemini / Workspace Agents |
|---|---|---|---|
| Permission enforcement | Uses the organizational identity model and permissions; sensitivity labels and administrative settings apply. | Workspace administrators control access and features; connected-app access follows account and workspace settings. | Workspace account and administrator controls govern data; verify each agent and connector configuration. |
| Model-training default | Enterprise data-protection terms apply to prompts and responses when using the managed enterprise product. | Business and Enterprise data is not used for training by default unless explicitly opted in. | Workspace materials describe no training on business data by default for Workspace Agents and customer-data protections for Workspace services. |
| Retention | Purview can set retention policies for Copilot interaction data. | Workspace and compliance settings control retention; administrators may retain or delete managed-account data. | Gemini privacy materials list retention periods and user controls; Workspace terms govern customer data. |
| Audit and administrator visibility | Interaction auditing and administrative controls are documented. | Administrators may access, export, audit, retain, and delete managed-account data, depending on configuration and law. | Use Workspace administration, audit, retention, and connector controls; confirm the exact edition. |
| Agent and connector boundary | Microsoft advises checking each agent’s privacy statement and terms of use. | Data sent to a connected third party is governed by that provider’s storage, processing, and residency terms. | Review Workspace Agent and connector-specific terms, retention, and residency commitments. |
Configure retention, deletion, and legal holds
Set separate policies for prompts, generated responses, uploaded files, tool calls, connector indexes, and audit logs. A short chat-retention period does not automatically delete a copied file in an index or an administrative audit record.
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Define retention by record type
- Interaction content: Decide how long prompts and responses remain available to users and administrators.
- Uploaded and generated files: Set expiration or deletion rules independent of chat history.
- Tool traces: Include search terms, retrieved document identifiers, API payloads, and execution logs in the policy.
- Audit records: Retain enough information for security and compliance investigations, even if conversational content expires.
Document legal holds, regulatory preservation duties, and deletion exceptions. Test a deletion request end to end: confirm what disappears from the user interface, what remains in backups or audit logs, and who can authorize restoration. Retention periods and deletion rights can be limited by law, contract, or a hold, so publish those qualifications to users.
Control administrator visibility
Users should be told that managed-account conversations may be accessible to authorized administrators. Depending on the service and configuration, administrators can access, export, audit, retain, restrict, or delete account data. Access should be limited to named roles, protected by strong authentication, and logged with a business reason.
Separate operational roles
- Agent builder: Creates instructions, tools, and data connections.
- Publisher: Approves an agent for a defined audience.
- Data owner: Approves access to a repository and its classification.
- Security or compliance reviewer: Examines logs, retention, and policy exceptions.
- Workspace administrator: Manages accounts and service settings, but should not automatically receive unrestricted document access.
Use least privilege for each role and require a second approval for agents that can search regulated or highly restricted data. Keep administrator access to conversations exceptional and auditable rather than an informal support privilege.
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Every connector creates a separate privacy boundary. Allow-list only the repositories and actions that the agent needs. For each connector, record:
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- Provider and product name
- Data fields, files, or prompts transmitted
- Processing and storage regions
- Retention and backup behavior
- Subprocessors and support access
- Permission-synchronization method and refresh interval
- Deletion and disconnect procedure
Review the connector’s own privacy statement and terms, not just the AI platform’s general policy. A third-party agent may store prompts or retrieved content under different terms. Disable write actions by default; searching a document library is materially less risky than sending email, editing records, creating tickets, or invoking a production API.
Prevent cross-tenant and personal-account mixing
Require company single sign-on and managed accounts for company knowledge-base agents. Block personal accounts from connecting the same repositories. If a user copies an answer into a personal service, the organization’s managed retention and audit controls may no longer apply; state this boundary in the acceptable-use notice.
Test privacy before production
Run a permission test plan with representative identities and deliberately restricted documents. Do not accept a successful response merely because the agent followed a “do not share” instruction.
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- Create test files in each classification, including one file visible to a single user, one visible to a group, and one denied to everyone in the test cohort.
- Test users with different roles, group memberships, guest status, and recently removed access.
- Ask for exact content, a summary, document titles, quoted phrases, and “what files mention this topic?” to detect metadata leakage.
- Try alternate spellings, indirect questions, multilingual prompts, and requests that combine permitted and denied information.
- Disconnect or revoke a user’s access, wait for the documented synchronization interval, and repeat the queries.
- Inspect citations, snippets, logs, tool traces, exports, and downloaded files for data that should have been hidden.
- Verify that refusal messages do not disclose the existence or classification of a restricted document.
- Record the expected result, observed result, timestamp, identity, connector version, and remediation owner.
Block publication when a denied user receives content, a stale permission remains effective beyond the approved interval, or a connector cannot explain where data is stored and how it is deleted.
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Configure user-facing notices and operating rules
Before launch, show a concise notice in the agent interface and link to the complete policy. Explain that the agent searches company sources, that answers can contain confidential information, and that managed-account activity may be monitored or retained by authorized administrators.
Tell users which data they may enter, which categories are prohibited, how long interactions are kept, how to request deletion or access, and how to report a suspected disclosure. Distinguish a managed company workspace from a personal account. Include a warning that generated answers can be incomplete and that users must verify permissions before forwarding content.
Vendor-specific checks
Microsoft Copilot and Copilot Studio
Microsoft documents that organizational access controls and policies apply to Copilot: identity and permissions, sensitivity labels, retention policies, interaction auditing, and administrative settings. Map each Copilot or Copilot Studio agent to its data sources and verify that Purview retention and audit settings cover the interaction records you need. Microsoft also advises checking the privacy statement and terms of use for every agent, particularly agents supplied or operated by another party.
ChatGPT Business and Enterprise
OpenAI states that it will not train on a workspace’s data for Business and Enterprise by default. Confirm that users are in the managed workspace, not a personal account, and review connected-app settings before enabling access. Administrators may access, export, audit, retain, or delete managed-account data depending on workspace configuration and applicable law, so make that visibility explicit in the user notice.
Google Workspace Gemini and Workspace Agents
Use Workspace administrator, audit, retention, and connector controls, and verify the precise Workspace edition. Google’s Workspace privacy materials describe customer-data protections and no-training-by-default treatment for Workspace Agents, while retention periods, residency, and third-party connector terms can vary. Review each agent’s configuration rather than assuming that one Workspace-wide setting covers every integration.
Operate the agent as a controlled system
Assign an owner and review the agent whenever its data sources, instructions, connectors, model, licensing, or vendor terms change. Re-run denied-access tests after permission-system migrations and major product updates. Monitor audit events for unusual bulk retrieval, repeated denied requests, large exports, and new connector authorization.
When an exposure is suspected, disable the affected connector or agent, preserve relevant audit records, identify the documents and identities involved, revoke unnecessary access, and follow the organization’s incident-response and notification requirements. Re-enable service only after the permission or connector defect is corrected and the test plan passes again.
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Quick Recap
Launch checklist
- Inventory repositories, inherited shares, groups, guests, and service accounts.
- Remove oversharing and stale memberships before indexing.
- Classify data and exclude categories that should never be retrieved.
- Choose a managed enterprise plan with written no-training-by-default terms and a data-processing agreement.
- Set separate retention rules for chats, files, tool traces, indexes, and audit logs; document holds and exceptions.
- Enable auditing and limit who can build, publish, invoke, export, or delete agents.
- Allow-list connectors and document their processing, storage, subprocessors, and deletion behavior.
- Test denied access and metadata leakage with representative users before production.
- Publish a notice covering administrator access, monitoring, retention, prohibited data, and managed versus personal accounts.
- Recheck vendor documentation and repeat tests after major product, connector, or licensing changes.
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