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Short answer: Treat public, consumer AI tools as unsuitable for confidential, personal, regulated or trade-secret company data unless your organisation has completed and documented a security and privacy review. A managed business service can add no-training defaults, encryption, single sign-on, access controls, retention settings and audit logs, but it does not replace classification, least-privilege permissions, human review or an incident plan.
This guide gives IT teams, managers and everyday users a practical way to decide what may be entered, select a governed service, configure it safely and respond when information is exposed. It applies to ChatGPT, Microsoft Copilot and comparable generative-AI products.
What does it mean to secure an AI tool used with company data?
Security means controlling the entire information path: what a user submits, which model and connected applications can access it, where data is stored or processed, who can retrieve records, how long they remain available, and how people verify the output. Prompts, uploaded files, generated answers, feedback and administrative logs can all become business records.
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A trustworthy deployment therefore combines product controls with organisational rules. Encryption or a no-training promise is useful, but it cannot stop an employee from pasting a customer list into an approved account, granting an agent excessive access, or acting on an unverified answer.
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Why public accounts are a poor default for sensitive information
Consumer services can have different training, retention, access and jurisdiction terms from business offerings, and administrators may have no reliable way to enforce settings or investigate use. The Office of the Australian Information Commissioner (OAIC), in guidance updated 17 January 2025, recommends that organisations do not enter personal information—particularly sensitive information—into publicly available generative-AI tools because of significant and complex privacy risks.
CMS guidance last reviewed 26 August 2025 is even more explicit for its workforce: “Never disclose or input PII, PHI, or any sensitive CMS data (including financial, health, vendor, procurement, evaluations, draft policies, or proprietary/business information) into publicly accessible AI platforms, chatbots, or prompts.” Use that as a practical baseline even when your organisation is outside the US healthcare sector.
What data should employees be allowed to enter?
Start with a classification rule that is simple enough to use under deadline pressure. The safest default is that information stays out of public AI tools unless an owner has approved the specific use case and service.
| Classification | Typical examples | Public AI default | Governed business service |
|---|---|---|---|
| Public | Published web copy, public product documentation, press releases | Usually permitted after checking copyright and confidentiality | Permitted, subject to normal account controls |
| Internal | Non-public procedures, meeting notes without personal data, internal FAQs | Normally exclude unless a documented review approves the tool and prompt | Permit only with approved workspace, retention and access settings |
| Confidential | Pricing plans, contracts, customer proposals, unreleased product plans | Prohibited | Use only in a specifically approved workflow with least privilege and an accountable reviewer |
| Personal or regulated | Names linked to records, health or financial information, government identifiers | Prohibited | Require privacy, legal and security approval; minimise or de-identify before processing |
| Trade secret or security-sensitive | Credentials, private keys, exploit details, proprietary source code, incident evidence | Prohibited | Keep in approved systems; any exception needs written risk acceptance and strict technical controls |
Use minimisation and de-identification
Remove fields the model does not need. Replace names, account numbers, exact dates, credentials and unique identifiers with consistent placeholders, and keep the re-identification key outside the AI service. De-identification is not automatically safe: rare combinations of facts can still identify a person, so a privacy specialist should review high-risk cases.
Separate instructions from source material
Keep a prompt template free of secrets, then attach only the excerpt needed for the task. Never include passwords, API keys, session tokens, private certificates or data that would let a model or connected tool take an unauthorised action.
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How do you choose a governed AI service?
Compare the service and the exact plan—not just the product name—against your legal, privacy and security requirements. OAIC advises due diligence covering intended use, testing, human oversight, privacy and security risks, and who can access personal information.
- Confirm model-use terms. Determine whether prompts, uploads, outputs and feedback are used to train or improve models. OpenAI states: “By default, we do not use data from ChatGPT Enterprise, ChatGPT Business, ChatGPT Edu, ChatGPT for Healthcare, ChatGPT for Teachers, or our API platform—including inputs or outputs—for training or improving our models.” Verify that the selected plan, settings and current terms match that commitment.
- Map storage and processing. Record where content and logs are processed, which residency options apply to your plan, and whether cross-border transfers require contractual or regulatory review.
- Check deletion and retention. Ask how an administrator deletes chats, files, vector indexes, backups and audit records; whether legal holds override deletion; and whether retention can be set by workspace, user or data type.
- Review access paths. Identify vendor personnel, support access, administrators, connected applications, browser extensions, plug-ins, agents and APIs that may see content.
- Require contractual assurances. Confirm privacy terms, breach notification, subprocessors, confidentiality, data-return or deletion obligations, and any required data-processing agreement.
- Test before deployment. Run representative prompts with synthetic data, inspect logs, test account off-boarding and attempt to access another user’s files. Keep evidence of the tests and unresolved limitations.
What managed plans change—and what they do not
Business plans can provide an administrative boundary that consumer accounts lack: company identity, role-based controls, audit records, retention settings and documented encryption. They do not make every prompt lawful, prevent malicious instructions in uploaded documents, or guarantee that a connected application has appropriate permissions. Governance remains your organisation’s responsibility.
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Make the company identity provider the only route into work AI accounts whenever the product supports it. This lets you disable access when someone leaves, enforce conditional access and correlate activity with a named person.
- Use SSO and MFA. Configure SAML single sign-on where available, require phishing-resistant or app-based multi-factor authentication, and prohibit shared credentials. OpenAI lists MFA and SAML SSO among controls for its business and API products.
- Assign least-privilege roles. Separate workspace owners, billing administrators, security auditors, ordinary users and developers. Give project, file, agent and API-key creation rights only to people who need them.
- Control invitations and external sharing. Restrict who may invite guests, publish custom assistants, share conversations or export data. Review public links and shared workspaces on a schedule.
- Manage lifecycle events. Disable accounts promptly at termination, transfer or delete their projects according to policy, rotate API keys, and review dormant accounts and service principals.
- Keep an audit trail. Send administrative and security events to a protected log store where feasible. Limit who can read those logs and record the purpose for each investigation.
Why shared accounts are dangerous
A shared account hides the person who submitted a prompt, makes off-boarding unreliable and often accumulates broad permissions. If a team needs a common workflow, use a named service identity with narrowly scoped access, an owner, key rotation and logs that tie each request to an individual.
How do connected apps and agents affect risk?
Connected tools can turn a text request into an action against mail, files, customer systems or code repositories. OpenAI’s Work documentation notes that connected apps use the permissions of the authorising account; retention and audit visibility vary by data category, storage location, event and configuration.
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Apply a permission checklist
- Authorise with a dedicated account rather than a senior executive’s broad personal account.
- Grant read-only access unless a write action is essential.
- Limit the folders, projects, repositories and records in scope.
- Require confirmation before sending messages, changing records, deleting files or publishing content.
- Review OAuth grants, tokens and agent tools at least quarterly and after a vendor or workflow change.
- Test prompt-injection scenarios, such as a malicious instruction hidden in a document that the agent is asked to summarise.
Disable connectors that cannot provide adequate scoping, revocation and activity visibility. A useful answer is not worth an uncontrolled path into production data.
How should retention, residency and logging be set?
Write a retention schedule before users begin uploading files. Define separate periods for conversations, source files, generated outputs, feedback, API payloads, security logs and backups, and identify the business owner for each category.
Questions administrators must answer
- Can users delete their own chats, and can administrators enforce deletion?
- Are deleted items removed from search indexes, attachments and backups, and on what timetable?
- Who can export conversations or audit data?
- Do legal holds or investigations suspend ordinary deletion?
- Which countries process or store each data type?
- What is the minimum logging needed to investigate misuse without creating an unnecessary second database of sensitive prompts?
OpenAI says qualifying organisations can configure retention controls, including zero data retention for the API, but eligibility and product constraints apply. Document the exact setting and verify it after upgrades; do not assume an API control automatically applies to a chat workspace.
Microsoft reports that work-account Copilot prompts, Bing queries triggered by prompts and responses are logged and can be viewed by IT administrators. Logging helps detect misuse, but logs may contain the same sensitive material as the original interaction, so protect them with strict access, encryption and their own retention schedule.
What technical safeguards belong around the AI service?
Use layered controls rather than relying on a vendor statement. CISA, the NSA AI Security Center, the FBI and allied agencies released joint guidance on 15 April 2024 focused on protecting, detecting and responding to malicious activity so AI systems preserve confidentiality, integrity and availability.
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Baseline controls
- Encryption: OpenAI describes AES-256 encryption at rest and TLS 1.2 or higher in transit for its business services. Confirm whether customer-managed keys or equivalent key-management options are available for your plan.
- Endpoint protection: Prevent copy-and-paste or file upload from high-risk managed devices where policy requires it, and monitor browser extensions and unsanctioned applications.
- Network controls: Use secure DNS, web filtering, egress monitoring and private connectivity options when offered. Block consumer endpoints if your policy prohibits them.
- Vulnerability management: Patch identity systems, browsers, plug-ins, code libraries and self-hosted model infrastructure. Include AI connectors and prompt-processing services in dependency inventories.
- Monitoring: Alert on bulk downloads, unusual exports, new OAuth grants, impossible-travel sign-ins, repeated policy violations and sudden increases in API volume.
- Incident response: Predefine who can revoke tokens, disable accounts, preserve evidence, notify privacy or legal teams and communicate with affected customers.
OpenAI also describes independent audits, red-team and adversarial assessments and 24/7 incident monitoring for business services. Treat those statements as inputs to your vendor risk assessment, not substitutes for your own controls and testing.
What should an AI acceptable-use policy include?
Publish a short policy that users can apply while working, then maintain technical standards for administrators. OAIC recommends transparent policies and procedures; CMS calls for continuing education, independent validation and prompt reporting of suspected disclosure.
Required policy sections
- Approved services and accounts: Name the workspaces, API endpoints and identity method. State that personal accounts and unapproved browser tools are not for company information.
- Prohibited data: List personal, health, financial, customer, credential, unreleased, proprietary source-code and regulated information, with examples relevant to each department.
- Allowed uses: Define low-risk drafting, summarising and analysis, and require approval for decisions, external communications, code deployment or automated actions.
- Disclosure rules: Tell users when AI assistance must be disclosed to a customer, colleague, regulator or publisher.
- Human review: Assign a named reviewer and specify what must be checked: facts, calculations, citations, bias, confidentiality, copyright and operational side effects.
- Retention and sharing: Explain deletion, exports, shared links, screenshots, downloads and legal holds.
- Connected applications: Prohibit unapproved connectors and require least privilege, confirmation gates and periodic access reviews.
- Reporting: Provide a simple channel and deadline for reporting accidental disclosure, suspicious output, prompt injection, account compromise or unauthorised access.
Train with realistic examples rather than a one-time slide deck. Staff should practise refusing a request for a customer spreadsheet, replacing identifiers, checking a generated answer against a trusted source and reporting an accidental paste.
How should people verify AI-generated output?
Assign accountability to a human who has the subject expertise and authority to approve the result. CMS advises: “Do not rely solely on AI-generated output for formulating policies or decisions. Always validate information using trustworthy sources, expert consultations, and independent research.”
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A review checklist
- Re-run important facts against authoritative internal or external sources.
- Check dates, figures, units, formulas, quotations, citations and links.
- Look for invented sources, plausible but false explanations and missing exceptions.
- Test code in an isolated environment; scan dependencies and inspect for secrets or unsafe operations.
- Check that the output does not reproduce confidential text from the prompt or expose another person’s information.
- Record the reviewer, source material, approval date and any changes when the output influences a policy, customer decision or regulated record.
How do ChatGPT, Copilot and other AI tools compare for company use?
There is no universally safest brand. Compare the exact product, edition and configuration across the controls that determine your risk.
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| Control area | Questions to ask | Evidence available in the supplied guidance |
|---|---|---|
| Training use | Are inputs and outputs excluded from model training by default, and can that setting change by plan? | OpenAI says business, enterprise, education, healthcare, teacher and API data is not used for training by default. Microsoft reports work-account Copilot prompts and responses are not used to train foundation models. |
| Identity | Are SSO, MFA, SCIM or equivalent lifecycle controls supported? | OpenAI documents MFA and SAML SSO for business/API products; verify availability in the purchased edition. |
| Roles and connectors | Can administrators restrict projects, agents, file access and OAuth scopes? | OpenAI documents granular access controls; connected apps inherit the authorising account’s permissions. |
| Retention and deletion | Can retention be configured by workspace, API or data type, and can deletion be proved? | OpenAI documents configurable retention for qualifying organisations, including zero data retention for the API; product and eligibility limits apply. |
| Audit visibility | Which prompts, responses, exports and admin events are logged, and who can view them? | Microsoft reports Copilot prompts, triggered Bing queries and responses are visible to IT administrators. Visibility for other products is configuration-dependent. |
| Encryption and keys | What is encrypted, which protocols are used, and are customer-managed keys available? | OpenAI describes AES-256 at rest and TLS 1.2+ in transit for business services. Other values are not stated in the guidance. |
| Residency and contracts | Where is data processed, which subprocessors are involved, and will the vendor sign required terms? | Not stated generically; obtain current plan-specific documentation and contractual commitments. |
| Human verification | How will reviewers check factual integrity, bias, confidentiality and side effects? | CMS requires trusted sources, expert consultation and independent research; this is an organisational workflow, not a product feature. |
What should you do after an accidental disclosure?
Move quickly, preserve evidence and avoid spreading the exposed content further.
- Stop the interaction and do not continue prompting with additional sensitive context.
- Record the account, workspace, time, data categories, recipients, connected applications and any downloads or shares.
- Delete or revoke the conversation, file, shared link, OAuth grant, API key or agent permission where possible, while preserving evidence required by legal or incident procedures.
- Notify the security, privacy and legal contacts named in your policy. They determine regulatory, contractual and customer notifications.
- Contain related systems: rotate exposed credentials, suspend tokens, restrict connectors and review access logs for follow-on activity.
- Assess whether the vendor can confirm deletion, access history or containment, and document what remains uncertain.
- Update the policy, training or technical controls that failed, then brief affected users without naming or redistributing the sensitive material.
How can an organisation roll out AI safely?
Use a staged rollout that creates evidence before broad access.
- Inventory use: Survey departments, browser extensions, API keys, personal accounts and existing automations.
- Classify workflows: Label each proposed use by data sensitivity, decision impact, external exposure and required integrations.
- Pilot with synthetic data: Test approved prompts, red-team prompt injection, verify logs and practise off-boarding.
- Configure guardrails: Enforce SSO, MFA, roles, connector scopes, retention, exports and alerts before adding real data.
- Train and certify: Require a short practical exercise and record completion for anyone handling internal information.
- Review continuously: Reassess vendor terms, model changes, new connectors, incidents, audit findings and regulatory guidance at scheduled intervals.
Keep a decision record for each high-impact use case: owner, purpose, data classes, approved service and plan, retention, residency, permissions, human reviewer, testing evidence and expiry or review date.
Frequently Asked Questions
Can I paste a small amount of confidential text into a public chatbot if I delete the chat afterward?
Deletion after the fact may not remove provider backups, logs, screenshots, browser history or copies made by a connected service. Treat the information as disclosed, report it under your incident process and use an approved, minimised workflow instead.
Does turning off chat history guarantee that company data is not retained?
No. History settings can differ from training controls, security logs, abuse monitoring, backups and connected-app records. Obtain the plan’s retention documentation and test deletion with an administrator.
Should employees be allowed to use personal AI accounts for work drafts?
Only for material that is genuinely public and approved by policy. Personal accounts usually sit outside company identity, retention, audit and off-boarding controls, so they are inappropriate for internal or regulated information.
How often should AI connectors and permissions be reviewed?
Review them at least quarterly and whenever a user changes role, a connector is added, a vendor changes its terms or an incident occurs. High-risk write access warrants more frequent or continuous monitoring.
Who is responsible when an AI answer causes a business error?
The accountable business owner and designated reviewer remain responsible for the decision or communication. Your policy should identify that person, require source verification and preserve the approval record.
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