The Tool Desk
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Before employees use ChatGPT, Copilot or another AI assistant for company work, put the service inside a managed business tenant, verify its contract and administrator controls, and restrict what data can reach it. The essential settings are training use, retention and deletion, encryption, SSO, MFA, role-based access, connected-file permissions, audit logging, data-loss prevention and offboarding. A privacy-impact assessment and practical employee rules must accompany those technical controls.
Start with a managed tenant, not personal accounts
Do not allow company information to be processed through unmanaged consumer accounts. A business tenant gives IT a place to enforce identity, retention, logging and access policies and gives procurement a contract to review. It also makes it possible to remove access when someone leaves.
OpenAI states that data from ChatGPT Enterprise, ChatGPT Business, ChatGPT Edu, ChatGPT for Healthcare and its API is not used to train or improve models by default. Microsoft states that enterprise data protection applies to Copilot prompts and responses and that those prompts and responses are not used to train foundation models. Treat both statements as commitments that must be confirmed for the exact product, license, region and feature your organization will use; they are not a universal property of every AI service.
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1Clear out junk files and repair common Windows errors2Fix the driver behind crashes, sound loss and screen glitches3Repair Windows errors before they cause bigger problemsInventory every AI product already in use, including browser extensions, meeting assistants, code tools, mobile apps, custom agents and API keys. Record the business owner, data categories handled, tenant or account type, connected systems, region, retention setting and contract or data-processing agreement.
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The ten settings to change before rollout
1. Disable model training or improvement where the administrator can
Use the provider’s organization control to opt out of training or model-improvement use when one exists. Save a screenshot or exported policy record and confirm whether the setting covers prompts, uploaded files, outputs, feedback, voice transcripts, images, agent actions and API traffic. A default business commitment is useful, but verification should cover the feature employees will actually use.
2. Choose retention and deletion deliberately
Set a retention period that matches your legal, security and records requirements instead of accepting the longest default. Define separate rules for conversation content, uploaded files, generated outputs, administrative logs and abuse-monitoring records. OpenAI says qualifying organizations can configure business-data retention, including zero data retention for the API. Microsoft documents retention policies for Copilot and logs prompts, web queries triggered by prompts and responses in Copilot Chat for work or school.
Document what deletion means: immediate removal from the user interface, removal from active systems, expiry from backups, or a legal hold that suspends deletion. Make the administrator responsible for testing a deletion request and recording the result.
3. Require SSO and phishing-resistant MFA
Connect the AI tenant to your identity provider and require single sign-on. Enforce multifactor authentication, preferably with security keys or passkeys for administrators and other high-risk roles. Block password-only sign-in, personal email domains and anonymous sharing. Keep an emergency administrator account protected by a separate, monitored recovery process.
4. Apply role-based access and least privilege
Create roles for ordinary users, help-desk operators, compliance reviewers, security investigators and tenant administrators. Give each role only the permissions needed for its work. Separate the ability to view interaction content from the ability to change retention or export logs. OpenAI’s February 2024 data-processing agreement describes SSO, mandatory MFA, role-based access, periodic access audits, prompt revocation after employee separation and least-privilege access; use those capabilities as a baseline to verify in your selected service.
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5. Clean up permissions in connected systems
AI assistants commonly answer from SharePoint sites, OneDrive folders, mailboxes, ticketing systems, databases or third-party connectors. They generally inherit the signed-in user’s permissions. That means an over-permissioned folder becomes an AI discovery problem: an employee may find sensitive material by asking a natural-language question even if the original application was rarely opened.
Before enabling a connector, remove stale groups, public links, inherited access and broad guest permissions. Review service accounts and connector scopes. Test with accounts representing a new hire, a contractor, a finance user and an administrator; each should retrieve only data that person could already open directly.
6. Preserve sensitivity labels and data-loss-prevention rules
Map labels such as Public, Internal, Confidential and Restricted to the AI service. Confirm that labels and encryption travel with files used in prompts or generated responses. Block or warn on uploads containing credentials, private keys, payment data, health information, customer records, unreleased financial results or privileged legal material. A warning should require a business justification and, for high-risk classes, approval from a named data owner.
7. Turn on audit logging and define an investigation path
Enable logs for sign-ins, administrator changes, prompts, file uploads, connector access, agent actions, exports and policy violations. Decide who may inspect content, how long logs remain, and whether they can be exported to your security-information and event-management system. Microsoft documents logging of work or school Copilot Chat prompts, web queries triggered by prompts and responses. Use that scope to design alerts for unusual downloads, repeated attempts to submit blocked data, large exports and activity immediately before an employee leaves.
8. Set regional storage, encryption and contractual controls
Confirm where prompts, files, logs, backups and support data are processed and stored. Verify encryption in transit and at rest, key-management options, subprocessors, breach-notification terms, deletion assistance and your data-processing agreement. Residency may differ by product feature or region, so obtain a written answer for the tenant configuration you are buying rather than relying on a general marketing statement.
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9. Put prohibited data and approved uses in writing
Publish a short policy employees can apply while working. Prohibit credentials, API keys, private certificates, regulated personal data, health information, customer records, unreleased financial information and privileged legal material unless the approved service, contract, lawful basis and technical controls explicitly cover that use. Require human review for legal, financial, medical, employment and safety decisions. Define approved low-risk uses such as drafting non-confidential text, summarizing public material or brainstorming with synthetic data.
10. Revalidate after every material change
Record the model, license, connectors, agents, regions and retention settings in a change register. Recheck them after a model update, new connector, new agent capability, license change, acquisition, policy revision or regional expansion. Microsoft notes that Copilot models and capabilities evolve; privacy, retention, residency and agent controls therefore need recurring validation rather than a one-time approval.
Use a control matrix to verify the tenant
| Control | Minimum configuration | Evidence to retain |
|---|---|---|
| Training use | Business commitment or administrator opt-out covering the enabled features | Contract clause, policy page and tenant export |
| Retention | Defined periods for chats, files, outputs and logs; deletion and legal-hold procedure | Retention policy, deletion test and approval record |
| Identity | SSO, mandatory MFA, disabled personal accounts and monitored recovery | Identity-provider and tenant configuration |
| Authorization | Role-based administration, least privilege and periodic access review | Role catalogue and review sign-off |
| Connected data | Revalidated source permissions, labels, DLP and connector scopes | Permission reports and test results |
| Audit | Interaction and administrative logs with retention, access and export rules | Sample events and investigation procedure |
| Residency and security | Documented processing regions, encryption, subprocessors and incident terms | Data-processing agreement and provider response |
| Change management | Review triggered by model, connector, agent, license or regional changes | Change register and reapproval record |
Classify prompts before employees write them
| Data class | Default action | Example |
|---|---|---|
| Public | Allowed in the approved tenant | Published product documentation or a public webpage |
| Internal, non-sensitive | Allowed when the tenant is managed and logging is enabled | A draft agenda or generic project outline |
| Confidential | Conditional; require an approved use case, label enforcement and access review | Internal forecasts or source code |
| Restricted or regulated | Block unless legal, contractual and technical approval explicitly covers it | Credentials, health data, customer records or privileged legal advice |
Classification is not a substitute for a contract. Even an approved tenant can expose information to the wrong employee if source permissions are wrong, and a model that does not train on prompts can still retain, display or log them under its service rules.
Complete a privacy-impact assessment
Assess the AI application before production use and whenever its purpose or data changes. ICO guidance recommends measures to identify, evaluate, minimise, monitor and control data-protection risks. Microsoft governance guidance, dated April 2, 2025, also recommends privacy-impact assessments for AI applications and data.
- Describe the purpose. State the business outcome, users, decisions supported and why AI is necessary.
- Map the data flow. List input sources, prompts, files, outputs, logs, subprocessors, regions, backups and deletion points.
- Assess people and rights. Identify personal-data subjects, lawful basis, transparency notices, access rights and human-review requirements.
- Test threats and misuse. Include prompt injection, accidental disclosure, excessive connector access, malicious insiders, model overreliance and unsafe generated content.
- Choose mitigations. Apply labels, DLP, minimisation, redaction, approval gates, monitoring and fallback procedures.
- Obtain sign-off. Have privacy, security, legal, records and the data owner approve residual risk before launch.
Train, offboard and review
Training should use realistic examples, not only a policy link. Show an allowed prompt, a prohibited prompt containing a secret, a safe redaction pattern and the reporting path for an accidental submission. Explain that generated text may be wrong, confidential, biased or unsafe and must be reviewed by a qualified employee.
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Automate offboarding through the identity provider: revoke sessions and tokens, remove group membership, disable API keys, transfer business records and preserve only the logs required by policy. Run quarterly access reviews for administrators, connectors and agents, and review high-risk interaction alerts with a documented escalation path.
What to compare when selecting an AI service
Procurement should compare the following capabilities for the exact edition and region, not just the product name:
- Training-use commitment for prompts, files, outputs, feedback and API traffic
- Retention, deletion, legal-hold and zero-retention options
- Processing and storage regions, encryption and key management
- SSO, MFA, role-based administration, lifecycle automation and least privilege
- Sensitivity-label, DLP and e-discovery integration
- Audit-log scope, content visibility, export format and retention
- Connector, plug-in and agent approval, sandboxing and action controls
- Data-processing agreement, subprocessors, breach notice and regulatory support
- Change-notification and revalidation practices when models or features evolve
Ask the provider to identify which controls are defaults, which require an administrator setting, which vary by geography or license and which are unavailable for a particular feature. Make the answers part of the approval record.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Common rollout failures and fixes
- Employees keep using personal accounts: block unsanctioned domains where practical, provide a usable managed alternative and explain the data boundary.
- A connector reveals too much: disable it, repair source permissions, retest with least-privileged accounts and only then re-enable it.
- Logs exist but nobody reviews them: assign an owner, define alert thresholds and rehearse an investigation.
- Retention conflicts with legal holds: involve records and legal teams before setting automatic deletion and document exceptions.
- A model or agent changes behavior: pause high-risk use, compare the new settings with the approved baseline and repeat the privacy-impact assessment.
Official guidance worth recording in your policy file
OpenAI’s business data statement says: “By default, we do not use data from ChatGPT Enterprise, ChatGPT Business, ChatGPT Edu, ChatGPT for Healthcare, or our API platform—including inputs or outputs—for training or improving our models.”
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Microsoft’s enterprise data-protection statement says: “Your access controls and policies apply to Copilot: Copilot respects your identity model and permissions, inherits your sensitivity labels, applies your retention policies, supports audit of interactions, and follows your administrative settings.”
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Microsoft’s work-or-school Copilot support states: “Your prompts, including any work content you add to the prompt, and Copilot’s responses aren’t used to train foundation models.”
These statements support a managed rollout, but they do not replace your own classification, permission reviews, retention schedule, incident process or employee training. NIST SP 800-218A, published in 2024, frames generative-AI security work around trustworthy AI, risk management, cybersecurity and privacy; use that framing to keep technical and organizational controls together.
Frequently Asked Questions
Does a no-training commitment make it safe to paste any company data into an AI tool?
No. Training use is only one control. Retention, employee permissions, connector scope, logging, residency, contractual coverage and the lawful basis for processing still determine whether a particular prompt is acceptable.
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Should a business use zero data retention for every AI workload?
Not automatically. Zero retention can reduce exposure for eligible API workloads, but the organization still needs operational logs, abuse monitoring, legal holds and records required by its obligations. Choose the shortest workable period for each data class and document exceptions.
Who should approve a new connector or AI agent?
At minimum, the system owner, security, privacy or legal reviewers, and the owner of the connected data should approve it. The approval should include scopes, actions, labels, retention, logging, test accounts and a rollback plan.
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