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Use an AI assistant with sensitive or business data only inside an environment your organisation has explicitly approved. Before anyone pastes a prompt or uploads a file, classify the information, remove anything unnecessary, confirm how the provider stores and uses data, restrict the assistant’s permissions, and assign a person to check every consequential result. A publicly available chatbot is not automatically an approved business service.
That rule applies to customer records, source code, contracts, health information, financial data, security details, employee investigations and trade secrets. The safest default is to use a synthetic or redacted example until your data owner, privacy lead and security team have documented an acceptable deployment.
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Safe use is a controlled business process, not a setting hidden in a chat window. Your organisation should know what data enters the system, where prompts and files travel, who can retrieve them, how long they remain available, whether they are used to improve a model, and what happens if the service or an account is compromised.
An approved deployment also limits what the assistant can do. It may be allowed to summarise a particular document library but not browse the whole company drive, send email, change production code or approve a payment. A person remains accountable for decisions and external communications.
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- Approved environment: the service, account type, region and configuration have passed your organisation’s privacy, security and legal review.
- Minimum necessary data: prompts contain only the information required for the task, with identifiers and secrets removed where possible.
- Least privilege: the assistant receives only the repositories, tools and actions it needs, preferably read-only.
- Human accountability: a qualified person checks important output before it affects a customer, employee, patient, payment, contract, policy or security control.
- Continuous oversight: logs, permission reviews, staff training, incident handling and vendor reassessment continue after launch.
Why isn’t a public chatbot automatically safe for company information?
Public availability proves that a service can be opened by anyone; it does not prove that your organisation has accepted its privacy, retention or cross-border-processing terms. The Australian Office of the Information Commissioner (OAIC) states: “As a matter of best practice, the OAIC recommends that organisations do not enter personal information, and particularly sensitive information, into publicly available generative AI tools, due to the significant and complex privacy risks involved.”
A prompt may be copied into provider logs, abuse-monitoring systems, search services, plug-ins or other subprocessors. Files can also persist in conversation history, workspace storage, indexes or embeddings. A “do not train” switch may address one use but not retention, administrator access, legal disclosure, backups or a connector that sends the content elsewhere. Treat an unapproved public service as unsuitable for personal, health, confidential, proprietary or regulated data until a documented review says otherwise.
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How should I classify information before opening an assistant?
Classification determines whether the task can proceed, which environment is permitted and what safeguards are mandatory. If you cannot determine the classification, stop and ask the data owner rather than guessing.
Use a practical classification ladder
- Public: information already cleared for public release.
- Internal: routine work material whose disclosure would be inconvenient but not materially harmful.
- Confidential: non-public business plans, contracts, pricing, source code, customer correspondence or employee records.
- Personal or health: information that identifies a person, describes their health, or can be linked back to them.
- Financial, security-sensitive or regulated: account details, payment data, credentials, vulnerability information, controlled records or information subject to sector rules.
- Trade secret or highly restricted: unreleased transactions, proprietary algorithms, private encryption material, incident investigations or information whose loss could cause exceptional harm.
Minimise before you classify the prompt as usable
Remove names, email addresses, customer numbers, exact dates of birth, account numbers, credentials, API keys, private encryption keys, unique case details and unnecessary narrative. Replace them with neutral labels such as Customer A or Project X. Prefer a short excerpt, a synthetic record or a redacted document over an entire database export. De-identification is not a guarantee: unusual combinations of facts can still identify someone, so have the data owner decide whether the remaining detail is acceptable.
How do I decide whether AI is necessary for the task?
Start with the business outcome, not the availability of a feature. Canada’s guidance advises institutions to be cautious and evaluate risks before using generative AI; it should not be applied to every task simply because it is available.
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- Describe the task: for example, draft a neutral summary of a policy, find duplicate clauses or suggest test cases.
- Record the expected benefit: estimate the time saved or quality improved without assuming the model is accurate.
- Identify affected people and systems: include customers, employees, patients, suppliers and any production or safety-critical process.
- Assess error consequences: a wrong spelling suggestion is different from a wrong medical, hiring, benefits, legal, financial or security recommendation.
- Choose a lower-risk alternative when possible: a local script, a conventional search tool, a template or manual review may meet the need without transferring sensitive content.
If the benefit is marginal and the harm from disclosure or error is substantial, do not use an assistant for that task.
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Complete vendor due diligence and a privacy or impact assessment before connecting business data. Record answers in an approval register so a later product change does not silently invalidate the decision.
| Control area | Questions to document | Decision consequence |
|---|---|---|
| Data use and retention | Are prompts, files, outputs, logs and embeddings retained? Are they used for model improvement by default? Can your organisation set a deletion period and obtain confirmation of deletion? | Reject or restrict the service if retention or training use conflicts with your classification or contract. |
| Data flows and subprocessors | Do content or metadata go to the developer, search providers, plug-ins, analytics systems or other third parties? What subprocessors and support personnel can access them? | Permit only documented flows covered by contract and your privacy assessment. |
| Identity and access | Does it support organisation-managed accounts, single sign-on, strong authentication, role-based access, tenant separation and audit logs? | Do not rely on shared personal accounts for business data. |
| Hosting and jurisdiction | Where are prompts and files processed and stored? What cross-border transfer safeguards, sector terms or contractual commitments apply? | Match the deployment to legal, customer and residency requirements. |
| Capability and autonomy | Can browsing, code execution, memory, retrieval, plug-ins and external actions be disabled independently? Can administrators limit connectors and operations? | Enable only the capabilities required for the approved use case. |
| Assurance and incidents | What independent security assessments, testing evidence, incident history, support process and breach-notification commitments are available? | Unanswered questions become explicit residual risks, not assumptions. |
| Deletion, export and exit | Can you export records, remove a workspace, revoke tokens and verify deletion when the contract ends? | Require an exit procedure before storing restricted material. |
The OAIC calls for privacy by design, due diligence, human oversight, training and lifecycle review. The UK Information Commissioner’s Office (ICO) describes a proportionate, risk-based approach using organisational and technical safeguards. A review is not “set and forget”: reassess the service after a model, policy, connector, hosting or pricing change.
How do I configure an approved assistant with least privilege?
Configure the assistant as if it were a new employee with a narrowly defined job. The Cybersecurity and Infrastructure Security Agency (CISA) and partners specifically recommend limiting agent autonomy and avoiding broad or unrestricted access to sensitive data or critical systems.
- Use managed identities: provision organisation accounts, enforce multi-factor or other strong authentication, and disable unowned personal accounts.
- Separate workspaces: keep restricted projects and client tenants isolated; do not mix personal chats with business repositories.
- Apply role-based permissions: map each connector to a team or project. Give read-only access unless a write operation is essential and separately approved.
- Turn off unnecessary features: disable public browsing, plug-ins, memory, file indexing, code execution and autonomous actions when the task does not require them.
- Constrain actions: require confirmation before sending messages, changing records, merging code, creating tickets or calling an external API.
- Log access: capture who used the assistant, which repository or connector was accessed, what action occurred and when, while protecting the logs themselves.
- Test boundary failures: try an account with the wrong role, a revoked token and a prompt that requests unrelated files. The assistant should refuse or return no data.
Prompt-injection attacks and malicious files can attempt to make an assistant ignore these boundaries. Permissions must therefore be enforced by the connected systems, not merely by instructions in a prompt.
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What information should never go into an unapproved tool?
Do not enter credentials, API keys, private encryption material, unreleased financial results, merger or acquisition information, legal advice, patient records, employee investigations or customer identifiers into an unapproved service. The following examples show how to choose a safer form of the same task.
| Requested task | Unsafe input | Safer approach |
|---|---|---|
| Summarise support cases | Full tickets containing names, addresses and account numbers | Remove identifiers and provide only the issue, product and resolution fields needed for the summary |
| Review source code | A private repository with deployment keys and proprietary algorithms | Use an approved code environment, revoke or exclude secrets, and send the smallest relevant module |
| Compare contracts | Executed agreements with client names, rates and signatures | Obtain legal approval, redact parties and signatures, and use a restricted workspace with deletion controls |
| Draft a health response | A patient’s record or free-text clinical history | Use an approved health-data system or a fictional case, then have a qualified professional review any draft |
| Investigate an incident | Live credentials, exploit details and unredacted logs | Keep secrets in the incident system; provide a sanitised timeline or indicators only when the environment is authorised |
CMS guidance is explicit: “Never disclose or input PII, PHI, or any sensitive CMS data … into publicly accessible AI platforms, chatbots, or prompts.” That is a useful baseline even when your organisation is outside the United States.
How should I write a safer prompt?
A safe prompt states the task and constraints without exposing more context than necessary. It also tells the assistant what not to infer and how to flag uncertainty.
- State the role and objective: “Extract renewal dates from this redacted clause.”
- Define the allowed source: “Use only the text between the markers; do not retrieve other files.”
- Specify the output format and uncertainty rule: “Return a table and mark missing dates as unknown.”
- Use placeholders for people, accounts, products and projects.
- Ask for a quotation or line reference so a reviewer can check the result.
- Do not include passwords, keys, hidden instructions or unrelated background.
Do not assume that a sentence such as “ignore previous instructions” is harmless. Treat instructions inside uploaded documents, web pages and retrieved content as untrusted data unless your security design explicitly validates them.
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Verification must match the consequence of an error. The Centers for Medicare & Medicaid Services (CMS) says: “Do not rely solely on AI-generated output for formulating policies or decisions. Always validate information using trustworthy sources, expert consultations, and independent research.” Veterans Affairs guidance likewise keeps accountability with personnel, requiring documentation and risk management for high-impact uses.
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Check the substance
- Recalculate figures and test code in a controlled environment.
- Open the cited policy, contract clause or source and confirm that the quotation and context are correct.
- Compare extracted facts with the authoritative record, not with another AI answer.
- Look for invented sources, missing exceptions, biased recommendations and unsafe assumptions.
Set a human release gate
A qualified reviewer must approve anything sent to a customer or used in hiring, health, benefits, finance, legal, safety or security decisions. Record the reviewer, source material, changes made and final decision for high-impact cases. Explainability has limits: a fluent answer is not evidence that the underlying reasoning or data is sound.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How should we monitor an AI deployment over time?
Monitoring makes the controls operational rather than aspirational. Establish an owner for the service and review usage at a frequency appropriate to the risk.
- Usage and access: review logs for unusual downloads, new connectors, privilege escalation and use outside approved projects.
- Output quality: sample responses for confidential-data leakage, factual error, unsafe code and discriminatory or otherwise harmful recommendations.
- Configuration: recheck memory, browsing, indexing, plug-ins, retention and model-training settings after every product update.
- People: refresh training, publish examples of prohibited data and remove access when roles change.
- Supplier: reassess subprocessors, hosting, incident history, terms and assurance evidence periodically and whenever the provider changes them.
- Records: retain the approval decision, risk assessment, permitted use cases, review dates and exceptions.
OAIC guidance emphasises that due diligence, training and monitoring must continue throughout the AI product lifecycle.
What should I do if sensitive data was pasted accidentally?
Act quickly, but do not destroy evidence that investigators need. Follow your organisation’s incident plan and privacy-notification rules.
- Stop further sharing and disconnect the affected connector or account if safe to do so.
- Record the time, workspace, user, exact data category, recipients and any downloads or external actions.
- Use the provider’s deletion, revocation and token-reset controls; ask for written confirmation where available.
- Notify your security, privacy, legal and data owners through the established escalation route.
- Assess whether people, customers, regulators or contractual partners must be informed.
- Review permissions, prompts and training before restoring the use case.
If credentials or keys were exposed, rotate them immediately and investigate access logs; deleting a conversation does not prove that a copied secret is unusable.
How should we compare approved assistants?
Compare services against the same documented use case rather than choosing the most capable general model. A smaller or less autonomous system may be safer when it meets the requirement with fewer data flows.
| Evaluation axis | Evidence to request | Questions for the approval record |
|---|---|---|
| Data-use terms | Retention schedule, training defaults and opt-outs, deletion process, subprocessors and breach notice | Can the service contractually prohibit secondary use and meet your deletion deadline? |
| Identity and tenancy | Single sign-on, multi-factor authentication, roles, tenant isolation, connector permissions and audit logs | Can an administrator see and revoke every user and integration? |
| Deployment | Hosting locations, residency options, transfer safeguards and sector commitments | Does processing fit your legal and customer obligations? |
| Autonomy | Controls for retrieval, browsing, code execution, memory and external actions | Can each capability be disabled or approval-gated? |
| Assurance | Independent assessments, testing evidence, incident history and documentation | Are unresolved risks documented with an owner and review date? |
| Human oversight | Review queues, audit trails, escalation and explanation features | Can a person reconstruct and challenge a high-impact decision? |
Document the approved model, workspace, connectors, data classes, permitted actions and review interval. Approval for one configuration does not automatically cover a new model, plug-in or data source.
Frequently Asked Questions
Does turning off chat history make an AI assistant safe for confidential data?
Not by itself. A history toggle may change what appears in the user interface while separate logs, abuse monitoring, backups, support access, connectors or model-improvement systems continue to receive content. Treat the setting as one control and verify its effect in the contract and technical documentation.
Can a small business use an AI assistant without a formal privacy team?
Yes, but it still needs a named owner and a written, proportionate assessment. Record the data classes, approved tasks, provider terms, permissions, retention, review steps and incident contact; ask an external privacy or security professional about regulated or high-impact uses.
Is self-hosting or on-premises deployment automatically compliant?
No. Hosting can reduce some transfer and provider-access risks, but your organisation still controls identities, logging, patching, model updates, backups, deletion and human review. A self-hosted system can expose data if its administrators, connectors or storage are poorly secured.
Can anonymised customer data be used to improve prompts?
Only after confirming that the transformation prevents practical re-identification for the people who can access the prompt and any linked datasets. Remove rare combinations and free-text clues, document the method, and apply the same retention and access controls as the approved use case.
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