The Tool Desk
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Start by approving only reviewed Slack apps, restrict native AI features, apply Slack Enterprise DLP where its content coverage fits, classify Microsoft 365 data with Purview sensitivity labels, configure Copilot DLP rules, and use Endpoint DLP on managed Windows devices for browser-based AI services. Then test prompts, files, pasted text, and enforcement modes before treating the controls as reliable.
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Map the data paths before choosing a control
“AI use” is not one data flow. A Slack bot reading a channel, Slack’s own summary feature, a Copilot prompt, an attached document, and text pasted into an unrelated website are handled by different policy engines. A control that blocks one path may not inspect another.
| Data path | Primary control | What it can do | Important boundary |
|---|---|---|---|
| Third-party app installed in Slack | Slack app approval and Enterprise app management | Review requested scopes and prevent unapproved installation | Access depends on the app’s scopes and the conversations where it is installed or added |
| Slack native AI features | Slack workspace or Enterprise AI settings | Limit who can use selected built-in features | These settings are separate from controls on third-party apps |
| Slack messages, text files, and canvases | Slack DLP on eligible Enterprise plans | Alert, warn, or hide content that matches configured rules | Several content types, including AI summaries and non-text media, are excluded |
| Copilot prompts and Microsoft 365 content | Microsoft Purview DLP, sensitivity labels, and permissions | Block processing of prompts containing selected sensitive information types or exclude labeled items | Copilot acts in the initiating user’s security context; policy scope and rights still matter |
| Text pasted or files shared with third-party AI websites | Endpoint DLP on onboarded Windows devices | Warn or block supported sharing actions in a browser | Coverage depends on the device, browser, site, and current endpoint support |
Use this map to avoid a common mistake: enabling Slack DLP and assuming it inspects every prompt sent to an AI website, or configuring Copilot DLP and assuming it governs a consumer service in any browser.
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Secure Slack before employees install AI apps
Inventory every AI app and its actual access
Export or otherwise review the complete Slack app inventory, including Marketplace applications, custom internal applications, and apps installed by individual teams. For each app, record:
- The conversations, channels, or direct messages where it is present.
- The OAuth scopes it requests, such as permission to read channel history, read files, post messages, or access user information.
- Whether it sends data to an external AI service and what that service says about retention, training, security, and deletion.
- Which business owner is accountable for the app and which data classes it may handle.
Slack explains that an app’s default access is limited to messages with the app. Adding the app to a channel or direct conversation gives it access to that conversation’s data, subject to its scopes. Treat an app’s presence in a sensitive conversation as a data-sharing decision, not merely a convenience feature.
Require approval before installation
Enable Slack’s app approval process or the applicable Enterprise app management policy so members cannot unilaterally add an unreviewed AI integration. Publish a short request procedure that tells employees what information to provide: business purpose, requested scopes, intended channels, vendor handling terms, retention period, and an owner.
- Open the Slack administration area and locate the app-management or app-approval settings for the workspace or Enterprise organization.
- Set installation to require administrator approval, or apply the Enterprise policy that restricts which apps members may install.
- Define reviewers from security, privacy, and the relevant business unit. Give them a documented decision standard rather than approving requests ad hoc.
- When a request arrives, compare the requested scopes with the minimum data needed. Reject broad read-history or file scopes when a narrower permission is sufficient.
- Record approval conditions, such as approved channels, prohibited data classes, retention limits, or a required review date.
Review the app’s Marketplace privacy, security, and compliance information, but do not treat a listing as a substitute for scope analysis. The effective exposure is the combination of the app’s permissions and the conversations to which members add it.
Separate native Slack AI from installed apps
Slack’s built-in AI features are a different data path from an external app. Workspace and Enterprise administrators can manage who may use native Slack AI and adjust selected data-source settings for features such as AI workflow steps.
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- Open the workspace or Enterprise administration settings for Slack AI.
- Identify which groups or members need each feature instead of granting access to the entire organization by default.
- Disable or limit features that are not justified for a group’s work, particularly where conversations contain regulated, privileged, or confidential material.
- Review whether an AI workflow step can use files or third-party connectors. Treat each connector as a new source and evaluate its permissions independently.
- Recheck access after reorganizations, new connectors, or changes to the workspace’s retention and compliance configuration.
Do not infer that restricting native Slack AI also restricts a third-party AI app. The two controls must be reviewed and tested separately.
Use Slack DLP where its coverage matches your policy
Slack documents DLP for Enterprise plans. It can scan messages, text-based files, and canvases against preconfigured or custom rules. Depending on the rule and response selected, Slack can alert an administrator, warn a user, or hide violating content.
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- Define the sensitive patterns that matter to your organization, such as credentials, financial identifiers, customer records, or regulated personal information.
- Create rules for the supported Slack content types and choose whether a match should alert, warn, or hide.
- Assign an owner for the DLP dashboard and a second person for escalation when a rule hides business-critical content.
- Run controlled tests with clearly synthetic values before enabling disruptive actions.
- Move from alerting to warning or hiding only after you understand false positives, user messaging, and the recovery process.
Slack DLP is not a universal AI-prompt inspection layer. Documented exclusions include non-text media, AI summaries, certain workflow data, content hosted in external file services, and files larger than 100 MB. A rule that covers a message does not automatically cover every file, summary, workflow payload, or external link associated with that message.
Consider additional Slack DLP services only after defining the gap
If the built-in controls do not cover the content or enforcement action you need, evaluate third-party Slack DLP services at the category level. Slack notes that partner data ingestion differs. Compare, in writing:
- Which messages, files, canvases, and app events are ingested.
- Whether the service can prevent, quarantine, redact, or only alert.
- What administrator rights and Slack scopes it requires.
- Retention, encryption, residency, deletion, and secondary-use terms.
- How it handles excluded content and what happens when Slack changes an API or event type.
Do not approve a partner merely because it advertises “AI protection.” Confirm the exact Slack paths it can inspect and the actions it can enforce.
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Protect Microsoft 365 Copilot with Purview controls
Classify content with sensitivity labels first
Microsoft Purview sensitivity labels provide the context that DLP policies use to distinguish ordinary work from protected information. Create labels that match your handling policy, then verify that their access and encryption settings are correct.
- Identify the information classes your organization must protect, such as confidential customer data, legal material, credentials, or restricted financial records.
- Configure Purview sensitivity labels for those classes and define who may read, edit, copy, or share each class.
- Apply labels consistently to supported documents and emails, using automatic or recommended labeling where appropriate.
- Test the labels with the same user roles that will use Copilot. Confirm that a user who should not read a file cannot retrieve it through an AI response.
- For encrypted content, verify that the user and the AI retrieval path have the usage rights required for the content to be processed.
Copilot operates in the initiating user’s security context. Labels do not replace permissions, and a label cannot make an incorrectly permissioned repository safe. Conversely, Copilot should not be expected to reveal content the user is not authorized to access.
Configure DLP rules for Copilot and Copilot Chat
Microsoft documents DLP policy locations for Microsoft 365 Copilot and Copilot Chat. Rules can target sensitive information types detected in prompts and sensitivity labels applied to files or emails. The documented actions include blocking prompt processing and preventing selected items from being processed.
- Open the Microsoft Purview compliance portal and create or edit the DLP policy location for Microsoft 365 Copilot and Copilot Chat.
- Select the sensitive information types that should prevent a prompt from being processed. Tune confidence levels and instance counts to match your risk tolerance.
- Add conditions for sensitivity labels on files or emails that Copilot must not process.
- Choose the action for each rule: block processing of the prompt, exclude matching items, or generate an alert where prevention is not yet justified.
- Scope the policy to the intended users, groups, and locations. Avoid assuming that a rule for one Copilot surface automatically applies to every Microsoft 365 workload.
- Configure user notifications that explain what was blocked and how to request an exception without asking the user to paste the sensitive data into a ticket.
Use the narrowest rule that meets the requirement. A broad rule that blocks every mention of a customer number may create workarounds; a narrowly defined rule with a clear exception path is easier to operate and audit.
Change the default simulation policy to enforcement deliberately
Microsoft’s default Copilot DLP policy starts in simulation mode. Simulation records activity and can generate alerts, but it does not block prompts. Treat simulation as a measurement phase, not as protection.
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- Leave the policy in simulation long enough to capture normal business prompts and identify false positives.
- Review matches with data owners and security analysts. Adjust sensitive information types, label conditions, exclusions, and user scope.
- Test the user experience for a blocked prompt and for an allowed prompt that contains no protected data.
- Change the policy to enforce mode only after the review is complete.
- Confirm enforcement with a controlled test account, then monitor alerts and help-desk reports during the first production period.
Record the policy mode in your operating documentation. A policy that is technically configured but left in simulation is a frequent source of false confidence.
Understand the uploaded-file limitation
The cited Copilot DLP policy documentation says directly uploaded files in prompts are not scanned by that policy. Do not infer that a prompt rule inspects the contents of every document a user attaches.
Test two separate cases: a user pastes sensitive text into the prompt, and a user uploads a document containing the same sensitive text. If the organization needs to control the second path, use the applicable file permissions, sensitivity labels, endpoint controls, or another supported policy rather than relying on prompt inspection alone.
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Use Endpoint DLP on managed Windows devices
Employees may bypass approved integrations by opening an AI website in a browser. Microsoft documents Endpoint DLP controls for onboarded Windows devices that can warn or block supported attempts to share sensitive information with third-party generative AI sites.
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- Define the sensitive information types and sensitivity labels that should trigger endpoint action.
- Configure browser sharing controls for the supported sites and browsers in your environment.
- Choose audit-only or warn mode for initial deployment so you can identify legitimate workflows.
- Move selected rules to block mode after reviewing activity and communicating the policy to users.
- Monitor endpoint activity and confirm that the browser, site, operating-system version, and policy configuration are supported.
Endpoint DLP is distinct from Copilot DLP. It applies to supported actions on onboarded devices; it is not a server-side guarantee that every AI website, unmanaged device, mobile browser, or private application is covered.
Use web-specific controls for Copilot Chat where available
Microsoft documents block-paste and label-based file-blocking capabilities for Microsoft 365 Copilot Chat on the web. These controls address browser actions that a prompt-processing rule may not cover.
- Block pasting when the clipboard content carries a protected label or matches a configured sensitive information type.
- Block files whose sensitivity label makes them unsuitable for Copilot Chat processing.
- Test both labeled and unlabeled files, because an unlabeled document may not trigger a label-based rule.
- Verify behavior in every browser your organization permits; do not assume that one browser’s result proves coverage in another.
Test the controls as a complete system
Run tests with synthetic data that resembles real records but cannot harm a customer or employee. Use separate test accounts for ordinary users, privileged users, and users who should receive exceptions.
| Test | Expected control | What to verify |
|---|---|---|
| Add an unapproved AI app to a Slack channel | Installation is denied or routed for approval | The request reaches the designated reviewer and the user sees a useful explanation |
| Approved Slack app in a sensitive channel | Access is limited to the app’s scopes and channel membership | The app cannot read unrelated channels or files |
| Sensitive text in a Slack message | Slack DLP alerts, warns, or hides according to the rule | The dashboard records the event and the owner can restore or handle it |
| Sensitive Slack AI summary or non-text attachment | Behavior reflects documented exclusions | You do not mistake the absence of a match for proof that the content is safe |
| Sensitive text pasted into a Copilot prompt | Purview DLP blocks or allows it according to policy mode | The policy is in enforcement, not simulation, when blocking is expected |
| Protected file retrieved by Copilot | Label and permissions prevent processing when configured | The test user’s rights and the label’s encryption settings are correct |
| Sensitive file uploaded directly to a Copilot prompt | Behavior reflects the documented upload limitation | You have another control if this path must be prevented |
| Sensitive text pasted into a third-party AI website | Endpoint DLP warns or blocks on a supported device and browser | The device is onboarded and the site is within current support |
Repeat the tests after changing labels, adding connectors, updating browsers, changing Slack app membership, or moving a policy from simulation to enforcement. Configuration drift can reopen a path that previously appeared closed.
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Operate alerts, exceptions, and incidents
Assign ownership before enabling blocking
Every prevention rule needs a named operational owner. That owner should review alerts, approve time-limited exceptions, coordinate with data owners, and document why a blocked action was legitimate or suspicious.
- Security owns policy design, escalation, and incident response.
- Data owners confirm whether a match is genuinely sensitive and whether an exception is acceptable.
- Workspace or Microsoft 365 administrators maintain scopes, groups, labels, and device coverage.
- The service desk handles user questions using a procedure that does not request sensitive content in plain text.
Make exceptions narrow and temporary
Allow an exception only for a defined user or group, application, data class, and time period. Require the requester to state the business purpose and the approved destination. Review exceptions regularly and remove them when the work ends.
Respond when a violation is detected
- Preserve the alert and policy context, including the user, application, channel or site, rule, and enforcement mode.
- Determine whether the data was merely attempted, actually delivered, retained by the AI service, or exposed to other users.
- Contain the path by removing an app from a channel, disabling a connector, revoking access, or blocking the endpoint action.
- Notify the data owner and privacy or legal teams when the information class requires formal assessment.
- Rotate credentials or tokens if secrets may have been pasted.
- Update the rule, user guidance, or app-approval standard based on the failure.
What these controls cannot guarantee
No documented control here blocks every route to every AI service. Coverage depends on plan or license, policy location, user and group scope, content type, device onboarding, browser support, app permissions, and enforcement mode.
- Slack DLP does not inspect all media, summaries, workflow data, externally hosted files, or files over 100 MB.
- Slack native AI restrictions do not govern a separately installed third-party app.
- Purview Copilot rules do not automatically govern unrelated AI websites.
- Endpoint DLP does not prove that an unmanaged device or unsupported browser is protected.
- A policy in simulation can alert without preventing processing.
- Prompt inspection does not imply inspection of a directly uploaded document.
Communicate these boundaries in your acceptable-use policy. Tell employees which AI features are approved, what information they must never submit, how to request an exception, and how to report an accidental disclosure.
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Deployment checklist
- Inventory Slack Marketplace, custom, and internal AI apps.
- Review each app’s scopes, conversation membership, vendor handling terms, and business owner.
- Require administrator approval or an Enterprise app-management policy for installation.
- Restrict native Slack AI features and review AI workflow files and connectors.
- Configure Slack Enterprise DLP rules for supported messages, text files, and canvases.
- Document Slack DLP exclusions and assign dashboard ownership.
- Apply Purview sensitivity labels with correct permissions and encryption rights.
- Configure Copilot and Copilot Chat DLP rules for sensitive information types and labeled items.
- Move the default Copilot policy from simulation to enforcement only after testing.
- Evaluate Endpoint DLP for onboarded Windows devices and supported browsers.
- Test prompt text, conversation messages, files, browser paste, and direct uploads separately.
- Maintain an exception process, incident procedure, and recurring coverage review.
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