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Do not paste confidential, personal, health, financial, security-sensitive, or proprietary information into a public AI chatbot unless your organisation has explicitly approved that service and configuration. Public availability is not a security or privacy approval. Safe use requires classifying and minimising the data, checking exactly how the provider stores and uses prompts and files, restricting permissions and connectors, requiring human review, and monitoring the deployment throughout its life.
An assistant can be useful for drafting, summarising, coding, searching an approved knowledge base, and analysing business material. The safe boundary is not the word “AI”; it is whether a documented control environment keeps the right data away from the wrong people, systems, regions, and future model-training processes.
Can I paste confidential company information into ChatGPT or another AI assistant?
Only when the specific service, account type, data flow, and use case have been approved by your organisation. A consumer or public chatbot should be treated as unapproved for personal information, health records, customer identifiers, source code, contracts, trade secrets, unreleased results, credentials, and regulated data.
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Why public availability is not approval
A tool may be easy for anyone to access while still sending prompts or uploaded files to the developer, search providers, plug-ins, logging systems, analytics services, or other subprocessors. The provider may retain content for a period you did not expect, use it to improve models under a particular setting, or process it in another country. A user usually cannot verify those details from the chat screen.
Approval therefore belongs to the organisation that owns the data. It should document the permitted service, account configuration, data classes, jurisdictions, retention period, administrators, and human review requirements. If a request falls outside that record, stop and ask the data owner or privacy and security team.
What can happen to data sent to an AI assistant?
Think of a prompt as a data transfer, not a private conversation. The text, attached files, retrieved passages, generated embeddings, output, and metadata may each have different recipients and retention rules.
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- The model provider: prompts and outputs may be logged for abuse prevention, service operation, debugging, or model improvement, depending on the plan and settings.
- Search and retrieval services: a browsing or enterprise-search feature can send a query and selected context to another provider.
- Plug-ins and connectors: an assistant linked to storage, email, ticketing, source-control, or customer systems can expose content beyond the chat window.
- Logging and observability systems: administrators, support staff, or security tools may receive transcripts, filenames, user identifiers, and error data.
- Subprocessors and hosting regions: a vendor may use infrastructure companies or transfer data across borders under contractual safeguards.
- Other users through generated output: a wrongly configured index, shared workspace, or over-broad permission can reveal one customer’s material in another person’s answer.
Before adoption, record where each category is stored, who can access it, how long it remains, whether it is used for model improvement, how deletion and export work, and how the provider will notify you of an incident. A privacy or impact assessment should cover the complete chain, not just the model’s name.
Which kinds of information need the strongest controls?
Use the organisation’s existing classification scheme. If no classification is available, apply the conservative labels below and ask the data owner when uncertain.
| Data class | Typical examples | Default assistant rule |
|---|---|---|
| Public | Published documentation, public product descriptions, released code | May be used in an approved or public tool if copyright, accuracy, and policy checks still apply. |
| Internal | Routine procedures, non-public plans without personal details | Use an organisation-managed service with access controls and logging; do not assume a personal account is acceptable. |
| Confidential or proprietary | Contracts, pricing, source code, customer lists, trade secrets, unreleased results | Use only an explicitly approved environment with documented retention, training, residency, and permissions. Minimise the excerpt. |
| Personal, health, financial, or regulated | Names linked to records, patient or benefits information, payment details, employee investigations | Require a documented privacy, legal, and security assessment; public tools are not an acceptable default. |
| Security-critical | Passwords, API keys, private encryption material, incident evidence, production access details | Never enter into an unapproved assistant. Prefer a redacted or synthetic example and an isolated, approved workflow. |
Classification is about the content, not the file extension. A harmless-looking spreadsheet can contain account numbers; a code snippet can contain a credential in a comment. Remove names, identifiers, secrets, unique customer details, and unnecessary context before any upload.
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How should a business decide whether AI is necessary?
Do not start with a tool and search for a justification. Start with a defined task, expected benefit, affected people, and consequences if the answer is wrong. Canadian federal guidance says institutions must be cautious and evaluate risks before using generative AI, rather than applying it to every case.
- Describe the task: for example, classify incoming requests, draft a neutral summary, or suggest test cases.
- Identify the people and systems affected: include customers, employees, patients, applicants, suppliers, and downstream decision-makers.
- Assess error impact: distinguish a reversible first draft from an output that could deny benefits, alter a contract, expose a vulnerability, or trigger a safety action.
- Set a no-AI alternative: document how the work will be completed if the service is unavailable or rejected.
- Choose the least capable method that meets the need: a local rule, template, or conventional search may avoid sending data to a model at all.
High-impact uses need a qualified human decision-maker, a documented rationale, and a route for correction. Convenience alone is not a sufficient reason to transfer sensitive data.
What should vendor and privacy due diligence check?
Approval should be based on written terms and technical evidence, not a sales promise or a default setting. OAIC guidance calls for privacy by design, due diligence, human oversight, training, and lifecycle review; the UK Information Commissioner’s Office describes a proportionate, risk-based approach with organisational and technical safeguards.
Data-use and retention questions
- Are prompts, uploaded files, outputs, and embeddings retained? For how long and for which purpose?
- Are business inputs excluded from model training by default, or must an administrator opt out?
- Can the organisation delete data, export records, and verify deletion?
- Which subprocessors receive content, and what happens when the vendor changes them?
- What breach, outage, and legal-request notifications are included in the contract?
Deployment and jurisdiction questions
- Where are content and backups processed and stored?
- Do contractual terms cover cross-border transfers and sector-specific obligations?
- Is each customer’s data isolated from other tenants and from public search?
- Can administrators enforce workspace, retention, connector, and sharing policies centrally?
Assurance questions
- What independent security assessments, penetration tests, or audit reports are available?
- How does the provider handle malicious files, prompt injection, abuse, and model changes?
- Can the provider support a privacy or impact assessment with architecture and subprocessor documentation?
- What is the incident-response contact, and how quickly must the provider notify customers?
Record the answers and the approval date. Revisit them after a major model, policy, hosting, connector, or pricing change; due diligence is not a one-time checkbox.
How do I configure an approved assistant with least privilege?
Give the assistant only the identity, data, and actions required for the stated task. CISA and partner agencies recommend limiting agent autonomy and avoiding broad or unrestricted access, especially to sensitive data and critical systems.
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Identity and workspace controls
- Use organisation-managed accounts, single sign-on where available, and multi-factor or strong authentication.
- Assign role-based permissions and separate administrator, developer, reviewer, and ordinary-user roles.
- Keep sensitive projects in separate workspaces or tenants; do not mix personal and business accounts.
- Enable audit logs for prompts, file access, connector calls, administrative changes, and exports.
- Review membership and service accounts regularly, including immediately after role changes or departures.
Connector and repository controls
- Disable browsing, plug-ins, memory, file indexing, and external connectors unless the task requires them.
- Scope each connector to an approved repository and a specific role; avoid organisation-wide access.
- Prefer read-only permissions. Require a separate approval for writing, deleting, sending messages, changing records, or executing code.
- Use separate credentials and short-lived tokens for integrations. Never place an API key or private key in a prompt.
- Test whether access filters apply to retrieved passages as well as to the original file list.
Autonomy and execution controls
An assistant that drafts text is lower risk than one that can browse internal systems, run code, or take external actions. Put approval gates before irreversible operations, limit the number and scope of tool calls, and require a human to confirm recipients, amounts, permissions, and production changes.
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What is a safe way to prepare and write prompts?
Use the minimum necessary excerpt and transform the data before sending it. Summarise, redact, aggregate, or replace values with consistent placeholders. Synthetic examples are preferable when the task can be demonstrated without real records.
Remove these items from an unapproved prompt
- Passwords, API keys, session tokens, private encryption material, and recovery codes.
- Patient, customer, employee, applicant, or account identifiers.
- Unreleased financial results, merger or acquisition information, legal advice, investigation files, and security incident evidence.
- Full contracts, source repositories, or datasets when a short, redacted excerpt answers the question.
Example transformation
Instead of asking an assistant to review a real complaint containing a customer’s name, address, order number, and medical detail, replace those fields with CUSTOMER_A, ORDER_123, and a general description of the issue. Keep only the clauses needed to identify the drafting problem, and state the desired output format. Store the original record in the approved system, not in the chat transcript.
For a coding task, remove credentials and proprietary endpoints, reproduce the error with a minimal synthetic project, and state the language version and expected behaviour. A redacted example may still reveal a trade secret if its structure is unique, so classification remains necessary.
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How should people verify AI output before using it?
AI output is a draft or recommendation, not an authoritative record. CMS guidance says not to rely solely on generated output for policies or decisions; validate it with trustworthy sources, expert consultation, and independent research.
Checks that apply to every consequential answer
- Verify quotations, dates, calculations, extracted fields, citations, and legal or policy interpretations against the original source.
- Run generated code in an isolated environment, inspect dependencies, and test security and edge cases before deployment.
- Check that a summary did not omit a qualification, exception, minority view, or negative result.
- Look for invented references, confident wording unsupported by evidence, and instructions hidden in retrieved documents.
- Record the reviewer, source material, decision, and any correction needed for auditability.
High-impact decisions
Require a qualified person to review and own decisions involving hiring, health, benefits, finance, legal rights, safety, security, or access to essential services. The reviewer must be able to reject the output, obtain the underlying evidence, and explain the final decision without treating the model as the accountable party. VA guidance similarly keeps accountability with personnel and calls for documentation and risk management for high-impact uses.
How should an organisation monitor and respond after launch?
Controls must continue through the product lifecycle. Monitor use, permissions, outputs, and provider changes rather than assuming the initial configuration remains safe.
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Operational monitoring
- Log who used the assistant, which connectors and repositories were accessed, what actions were taken, and which outputs were exported.
- Sample outputs for data leakage, unsupported claims, discrimination, prompt injection, and policy violations.
- Alert on unusual bulk downloads, attempts to access restricted repositories, repeated failed authentication, or unexpected external actions.
- Review permissions, retention settings, and shared links on a defined schedule and after organisational changes.
- Train staff on classification, redaction, verification, and reporting; refresh training when features change.
Incident response
Provide a simple route for reporting an accidental disclosure, malicious attachment, prompt injection, harmful output, or unauthorised action. Preserve relevant logs, revoke exposed credentials, restrict the connector or workspace, notify the data owner and required authorities, and assess whether affected people or customers must be informed. Do not delete the only evidence while trying to clean up the incident.
Change management
Reassess the service after a model update, new memory or browsing feature, new subprocessor, changed retention policy, hosting-region change, or newly enabled integration. A previously approved text-only workflow may need a new assessment when the assistant gains code execution or the ability to send messages.
How should I compare approved AI assistants?
Compare the controls that affect your use case, not just model quality or a benchmark score. A capable model with unrestricted access can create more risk than a less capable model inside a well-contained workflow.
| Comparison area | Questions to ask | Why it matters |
|---|---|---|
| Data use | What is retained, for how long, and is training use off by default? Can you delete and export content? | Determines whether a prompt can become a persistent or secondary data set. |
| Identity and access | Are SSO, MFA, role controls, tenant isolation, connector scopes, and audit logs available? | Limits who can submit, retrieve, and administer sensitive material. |
| Deployment | Which hosting regions, contractual terms, certifications, and cross-border safeguards apply? | Supports privacy, regulatory, and residency obligations. |
| Capability versus autonomy | Can browsing, retrieval, memory, code execution, and external actions be disabled or approval-gated? | Reduces the blast radius of a bad prompt, malicious file, or erroneous output. |
| Assurance | Are independent assessments, incident history, testing evidence, architecture details, and impact-assessment support available? | Provides evidence beyond a product claim. |
| Human oversight | Can you enforce review gates, escalation, explanations, and accountability for high-impact uses? | Keeps decisions with responsible personnel rather than an automated response. |
Document why the selected configuration is proportionate to the task. If a required control is unavailable, reduce the data, reduce the assistant’s permissions, choose another method, or do not proceed.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What changes for common business use cases?
Customer data and support tickets
Use de-identified samples for drafting and classification. If live records are required, restrict retrieval to the assigned queue, mask identifiers where possible, prevent the assistant from exposing one customer’s history to another, and require a human before a response is sent.
Source code and technical documents
Remove secrets and production connection details. Limit indexing to the repository or branch needed for the task, use read-only access for analysis, and run generated code through normal review, testing, dependency, and security pipelines.
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Contracts and legal material
Send only the clauses necessary for a defined comparison or plain-language explanation. Treat the result as drafting assistance; a qualified legal reviewer must check the original wording, jurisdiction, definitions, exceptions, and negotiation context.
Health, benefits, and employee information
Public assistants are not an acceptable place for identifiable records. Use an approved environment only after privacy, security, retention, access, and jurisdiction requirements are documented, and keep a qualified professional responsible for any consequential interpretation.
Financial and security operations
Never expose account credentials, private keys, unreleased results, or live incident evidence to an unapproved tool. Use synthetic data for experimentation, isolate execution, and require dual review for transactions, production changes, or incident communications.
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What are the most common mistakes and how are they fixed?
- “It is only an internal link.” Internal sharing does not prove tenant isolation or least privilege. Verify the actual permission path and retrieved content.
- “The vendor says it does not train on our data.” Training is only one use. Check retention, support access, subprocessors, logs, backups, deletion, and legal-request handling.
- “We removed the name, so it is anonymous.” Rare facts, dates, account numbers, and combinations of attributes can re-identify a person. Remove unnecessary context and use aggregation or synthetic records.
- “The assistant has read-only access, so it cannot cause harm.” Read access can expose secrets or confidential records, and generated output can trigger a human action. Narrow the repository and review exports.
- “A human glanced at the answer.” Review must be competent and independent enough to catch omissions, fabricated citations, unsafe code, and biased recommendations. Define what evidence the reviewer must check.
- “Approval happened once.” New memory, browsing, connectors, model versions, subprocessors, or hosting locations can change the risk. Put reassessment in change management.
What does a practical approval checklist look like?
Before enabling a sensitive-data workflow, confirm every item below and keep the record with the system owner.
- The task, benefit, affected people, error impact, and non-AI alternative are documented.
- Data classes are identified, and the prompt or file is minimised, redacted, or synthetic where feasible.
- Retention, model-improvement use, deletion, export, subprocessors, hosting regions, and incident notification are documented.
- A privacy or impact assessment and security review are complete for the use case.
- Organisation-managed identity, strong authentication, role controls, tenant separation, and audit logs are enabled.
- Connectors, memory, browsing, indexing, code execution, and external actions are disabled unless required and scoped.
- Read-only access and human approval gates protect repositories and irreversible actions.
- Reviewers know how to validate outputs and where to report leakage, prompt injection, or harmful results.
- Monitoring, access recertification, staff training, incident response, and periodic vendor review have owners and dates.
If any answer is unknown, treat the workflow as unapproved until the data owner, privacy lead, and security team resolve it. Safe use is a continuing operating practice, not a setting hidden in a chat window.
Frequently Asked Questions
Can I use an AI assistant with anonymised customer data?
Only if the remaining fields cannot reasonably identify a person in context and the service is approved for that data class. Names are not the only identifiers; rare events, dates, account references, and combinations of attributes can still be identifying. Prefer aggregation or synthetic records when the task allows it.
Does turning off model training make a public chatbot safe for confidential work?
No. Training settings do not answer where content is stored, who can access logs, which subprocessors receive it, how backups are deleted, or where processing occurs. Those controls and the organisation’s contractual and technical approval still matter.
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Usually not. Scope retrieval to the smallest approved repository and role, use read-only permissions, and test whether access filters also apply to passages returned in answers. Broad indexing increases the impact of a compromised account, bad permission, or prompt injection.
What should I do if an employee already pasted sensitive data into an unapproved tool?
Treat it as a potential data incident: preserve relevant details, notify the data owner and security or privacy contacts, revoke exposed credentials, and ask the provider about deletion and access. Assess what was sent, who could have received it, legal notification duties, and whether affected people need information.
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